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DTSTART;TZID=America/New_York:20260518T090000
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DTSTAMP:20260711T064210
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SUMMARY:Workshop on Calabi-Yau metrics and optimal transport
DESCRIPTION:Workshop on Calabi-Yau metrics and optimal transport \nDates: May 18–22\, 2026 \nLocation: Harvard CMSA\, 20 Garden Street\, Cambridge MA \nRecent advances in the study of Calabi-Yau metrics have revealed an interesting connection with optimal transport\, and the regularity theory for optimal transport is expected to play an increasingly important role in the study of Kähler geometry. The goal of this workshop is to bring together the optimal transport and complex geometry communities to investigate problems arising from these exciting developments. \nMinicourse Speakers \n\nRobert McCann\, University of Toronto\nYang Li\, Cambridge University\n\nWorkshop Speakers \n\nRolf Andreasson\, Chalmers University\, Sweden\nBenjy Firester\, MIT\nJakob Hultgren\, Umea University\, Sweden\nYoung-Heon Kim\, University of British Columbia\nNam Le\, Indiana University\nJiakun Liu\, University of Sydney\nArghya Rakshit\, University of Toronto\nGabor Szekelyhidi\, Northwestern University\nYueqiao Wu\, Johns Hopkins University\n\nOrganizers: \n\nTristan Collins\, University of Toronto\nMattias Jonsson\, University of Michigan\nConnor Mooney\, University of California\, Irvine\nFreid Tong\, University of Toronto\n\n  \nVideos of selected talks are now available on the  Youtube Playlist \n  \nSchedule (pdf) \nMonday\, May 18\, 2026 \n9:00–9:30 am\nBreakfast \n9:30–10:45 am\nTutorial: Yang Li\, Cambridge University (via Zoom Webinar)\nTitle: On the metric SYZ conjecture\nAbstract: For a polarised degeneration family of Calabi-Yau manifolds near the large complex structure limit\, the metric SYZ conjecture asks for a special Lagrangian torus fibration on the generic region of the Calabi-Yau manifolds. I will summarize the progress on the metric SYZ conjecture so far\, emphasizing on some more recent progress. \n10:45–11:15 am\nBreak \n11:15 am–12:30 pm\nTutorial: Robert McCann\, University of Toronto\nTitle: A geometric approach to apriori estimates for optimal transport maps\nAbstract: A key inequality which underpins the regularity theory of optimal transport for costs satisfying the Ma-Trudinger-Wang condition is the Pogorelov second derivative bound. This translates to an a priori interior modulus of the differential estimate for smooth optimal maps. We describe a new derivation of this estimate with Brendle\, Leger and Rankin which relies in part on Kim\, McCann\, and Warren’s observation that the graph of an optimal map becomes a volume maximizing non-timelike submanifold when the product of the source and target domains is endowed with a suitable pseudo-Riemannian geometry that combines both the marginal densities and the cost. This unexpected links optimal transport to the plateau problem in geometry with split signature\, and shows the key difficulty is showing the maximizing non-timelike submanifold is in fact (uniformly) spacelike. J. Reine Angew. Math. 817 (2024) 251-266 doi.org/10.1515/crelle-2024-0071 arXiv 2311.10208 \n12:30–2:00 pm\nLunch (catered) \n2:00–3:15 pm\nTalk: Nam Le\, Indiana University\nTitle: Variational approach to degenerate Monge-Ampère equations with mixed measures and monotonicity\nAbstract: In this talk\, we will discuss the solvability and uniqueness for several degenerate Monge-Ampère equations including the Monge-Ampère eigenvalue problem in real Euclidean spaces that involve singular Borel measures. Our approach systematically analyzes the Monge-Ampère energy from the variational point of view and appropriately exploits monotonicity arguments. We will examine several essential tools: the mixed Monge-Ampère measure\, Aleksandrov-Blocki-Jerison type maximum principles\, convex envelope\, comparison principles for subcritical equations\, and integration by parts whose failure leads to symmetry breaking and nonuniqueness phenomena. \n3:15–3:45 pm\nBreak \n3:45–5:00 pm\nTalk: Yueqiao Wu\, Johns Hopkins University\nTitle: Valuative aspects of complete Calabi-Yau metrics of Euclidean volume growth\nAbstract: The search of a complete Calabi-Yau metric on an affine variety X amounts to solving a complex Monge-Ampère equation subject to nice “boundary conditions” at infinity. In the case where X is the complement of an SNC anticanonical divisor on a Fano manifold\, generalizing the work of Tian-Yau\, Collins-Li showed that such boundary data can be extracted from solutions to certain real Monge-Ampère equations. If we require the metric to have Euclidean volume growth\, however\, it is understood that the boundary conditions should come from prescribing a Calabi-Yau asymptotic cone at infinity. This is the same as giving the algebro-geometric data of a valuation which induces a degeneration of X to a K-stable affine cone. In this talk\, we will explain that such valuations in fact always come from Fano type compactifications of X\, similar to the ones considered by Tian-Yau and Collins-Li. In addition\, K-semistability of the affine cone can be characterized intrinsically by a valuative criterion on X. Based on joint work with Mattias Jonsson. \n  \nTuesday\, May 19\, 2026 \n9:00–9:30 am\nBreakfast \n9:30–10:45 am\nTutorial: Robert McCann\, University of Toronto\nTitle: Trading linearity for ellipticity: A low regularity Lorentzian splitting theorem\nAbstract: While Einstein’s theory of gravity is formulated in a smooth setting\, the celebrated singularity theorems of Hawking and Penrose describe many physical situations in which this smoothness must eventually breakdown. It is thus of great interest to study the theory in low regularity settings. In the lecture\, we establish a low regularity splitting theorem by sacrificing linearity of the d’Alembertian to recover ellipticity. We exploit a negative homogeneity $p$-d’Alembert operator for this purpose. The same technique yields a simplified proof of Eschenberg (1988) Galloway (1989) and Newman’s (1990) confirmation of Yau’s (1982) conjecture\, bringing all three Lorentzian splitting results into a framework closer to the Cheeger-Gromoll splitting theorem from Riemannian geometry. Based on joint work with Mathias Braun\, Nicola Gigli\, Argam Ohanyan\, and Clemens Saemann: 1) arXiv 2501.00702 2) arXiv 2408.15968 3) arXiv 2410.12632 4) arXiv 2507.06836 \n10:45–11:15 am\nBreak \n11:15 am–12:30 pm\nTutorial: Yang Li\, Cambridge University (via Zoom Webinar)\nTitle: On the metric SYZ conjecture\nAbstract: For a polarised degeneration family of Calabi-Yau manifolds near the large complex structure limit\, the metric SYZ conjecture asks for a special Lagrangian torus fibration on the generic region of the Calabi-Yau manifolds. I will summarize the progress on the metric SYZ conjecture so far\, emphasizing on some more recent progress. \n12:30–2:00 pm\nLunch Break \n2:00–3:15 pm\nTalk: Young-Heon Kim\, University of British Columbia\nTitle: Trajectory Inference via Multi-marginal Schrödinger Bridges\nAbstract: Trajectory inference arises in important scientific problems. In particular\, biological development can be interpreted as a curve in the space of gene-expression distributions\, and the goal is to infer this trajectory from observed data. There has been progress by using optimal transport (OT) as a way to interpolate between distributions. More recently\, Schrödinger bridges\, a stochastic generalization of OT\, have been considered. In this talk\, we discuss stability of such OT-based methods. This is joint work with Geoffrey Schiebinger and Rentian Yao. \n3:15\nBreak \n6:30 pm\nDinner \n  \nWednesday\, May 20\, 2026 \n9:00–9:30 am\nBreakfast \n9:30–10:45 am\nTutorial: Yang Li\, Cambridge University (via Zoom Webinar)\nTitle: On the metric SYZ conjecture\nAbstract: For a polarised degeneration family of Calabi-Yau manifolds near the large complex structure limit\, the metric SYZ conjecture asks for a special Lagrangian torus fibration on the generic region of the Calabi-Yau manifolds. I will summarize the progress on the metric SYZ conjecture so far\, emphasizing on some more recent progress. \n10:45–11:15 am\nBreak \n11:15 am–12:30 pm\nTutorial: Robert McCann\, University of Toronto\nTitle: The monopolist’s free boundary problem in the plane: an excursion into the economic value of private information\nAbstract: The principal-agent problem is an important paradigm in economic theory for studying the value of private information: the nonlinear pricing problem faced by a monopolist is one example; others include optimal taxation and auction design. For multidimensional spaces of consumers (i.e. agents) and products\, Rochet and Chone (1998) reformulated this problem as a concave maximization over the set of convex functions\, by assuming agent preferences are bilinear in the product and agent parameters. This optimization corresponds mathematically to a convexity-constrained obstacle problem. The solution is divided into multiple regions\, according to the rank of the Hessian of the optimizer.\nIf the monopolists costs grow quadratically with the product type we show that a partially smooth free boundary delineates the region where it becomes efficient to customize products for individual buyers. We give the first complete solution of the problem on square domains\, and discover new transitions from unbunched to targeted and from targeted to blunt bunching as market conditions become more and more favorable to the seller.\nBased on works with Kelvin Shuangjian Zhang\, Cale Rankin\, and Lucas O’Brien in various combinations:\n1) Math. Models Methods Appl. Sci. 34 (2024) 2351-2394; 2) J. Convex Anal. (Rockafellar 90 Issue)\, 32 (2) (2025) 579-584; 3) arXiv 2303.04937; 4) arxiv 2412.15505; 5) arXiv 2603.14100. \n  \nThursday\, May 21\, 2026 \n9:00–9:30 am\nBreakfast \n9:30–10:45 am\nTalk: Gabor Szekelyhidi\, Northwestern University\nTitle: Nondegenerate Neck Pinches along the 2d Lagrangian mean curvature flow\nAbstract: The Thomas-Yau-Joyce conjecture predicts that the mean curvature flow can be used to decompose Lagrangian submanifolds in Calabi-Yau manifolds into special Lagrangian building blocks. The basic mechanism for this decomposition is given by neck pinches. I will discuss work on the behavior of such neck-pinch singularities\, in particular the class of nondegenerate neck pinches\, which satisfy certain properties conjectured by Joyce. The construction also relates to work of Neves on finite time singularity formation. \n10:45–11:15 am\nBreak \n11:15 am–12:30 pm\nTalk: Rolf Andreasson\, Chalmers University\, Sweden\nTitle: Optimal transport between boundaries of dual reflexive polytope\nAbstract: I will present an optimal transport problem between the boundaries of a pair of reflexive polytopes. Under a certain structural condition on its solution\, this problem is related the study of metric degenerations of families of Calabi–Yau hypersurfaces in the corresponding toric Fano variety. A better understanding of such solutions and their regularity would shed light on several aspects of the degeneration and conjectural Gromov–Hausdorff limit\, and I will present some open directions of research. This is based on joint work with Jakob Hultgren\, Mattias Jonsson\, Enrica Mazzon and Nicholas McCleerey. \n12:30–2:00 pm\nLunch Break \n2:00–3:15 pm\nTalk: Jakob Hultgren\, Umea University\, Sweden\nTitle: Affine mondoromy\, cost functions and real Monge-Ampère equations\nAbstract: Recent results of Blum-Liu and Y. Li show that the metric SYZ conjecture holds. The solution hinges on the existence of valuatively independent bases for spaces of sections of the polarising bundle. These bases induce a cost function\, providing a link between the non-Archimedean Monge-Ampère equation and optimal transport. The resulting SYZ-fibration is constructed on a large but non-explicit set. In order to better understand this set\, more information about the cost function is arguably needed. We propose a cost function explicitly computable from the monodromy of an affine structure on the essential skeleton. In the case of the Fermat family of cubic curves\, this cost function (as opposed to the one attained from the ambient projective space) can be shown to agree with the one attained from a valuatively independent basis. We conjecture that this equality of cost functions holds in general\, and demonstrate in examples how the explicit cost function can be used to directly produce solutions to real Monge-Ampère equations on the essential skeleton. Joint work with Sohaib Khalid. \n3:15–3:45 pm\nBreak \n3:45–5:00 pm\nTalk: Benjy Firester\, MIT\nTitle: Free boundary Monge-Ampere equations with applications to optimal transport and Calabi-Yau geometry\nAbstract: I will present a variational framework to solve a general class of free-boundary Monge-Ampère equations. This approach combines the classical first and second boundary value problems by imposing both the boundary data and the gradient image of the solution. I will explore applications to the Monge-Ampère eigenvalue problem\, convex reconstruction theorems\, and geometric problems including a hemispherical Minkowski problem\, Calabi-Yau metrics\, and free boundary toric Kähler–Einstein/Kähler-Ricci soliton metrics. I will also discuss the connection to the boundary regularity of optimal transport. \n  \nFriday\, May 22\, 2026 \n9:00–9:30 am\nBreakfast \n9:30–10:45 am\nTalk: Jiakun Liu\, University of Sydney\nTitle: Free boundary problems in optimal transportation\nAbstract: In this talk\, I will present some recent results on the regularity of free boundaries in optimal transportation\, including higher-order regularity\, global regularity\, and a model case involving multiple targets. These results are based on a series of joint works with Shibing Chen\, Xianduo Wang\, and Xu-Jia Wang. \n10:45–11:15 am\nBreak \n11:15 am–12:30 pm\nTalk: Arghya Rakshit\, University of Toronto\nTitle: Solutions to the Monge–Ampère equation with singular structures\nAbstract: We construct examples of solutions to the Monge–Ampère equation with point masses exhibiting polyhedral singular structures. We further analyze the stability of these singular sets under small perturbations of the data. In addition\, we construct solutions whose Monge–Ampère measure contains a singular component supported on lower-dimensional sets and we study the regularity of such solutions. \n  \n\n 
URL:https://cmsa.fas.harvard.edu/event/cymetrics/
LOCATION:CMSA 20 Garden Street Cambridge\, Massachusetts 02138 United States
CATEGORIES:Workshop
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DTSTART;TZID=America/New_York:20260427T090000
DTEND;TZID=America/New_York:20260430T170000
DTSTAMP:20260711T064210
CREATED:20250724T152524Z
LAST-MODIFIED:20260507T181943Z
UID:10003757-1777280400-1777568400@cmsa.fas.harvard.edu
SUMMARY:Mathematics and Biology II: Mathematics and Science of Behavior
DESCRIPTION:Mathematics and Biology II: Mathematics and Science of Behavior \nDates: April 27–30\, 2026 \nLocation: Harvard CMSA\, Room G10\, 20 Garden Street\, Cambridge MA \nYoutube Playlist \n\n\nThis meeting will explore the emerging mathematics and science of embodied cognition—the idea that behavior arises not solely from the brain but through the dynamic interaction of brain\, body\, and environment. Understanding how animals sense\, move\, decide\, and coordinate\, from individual sensorimotor loops to collective dynamics\, demands mathematical frameworks that integrate geometry\, dynamics\, stochastic processes\, control theory\, and multiscale physics. The meeting will bring together experimentalists studying behavior across species with theorists and engineers building mathematical models and bio-inspired machines\, to identify shared principles of adaptive behavior. \n\n\nCo-organizers: L. Mahadevan (Harvard)\, Francesco Mori (Harvard CMSA)\, Venkatesh Murthy (Harvard) \nSpeakers \n\nPulkit Agrawal\, MIT\nKristin Branson\, HHMI\nAntonio C. Costa\, Sorbonne University/Paris Brain Institute\nNoah Cowan\, Johns Hopkins University\nRobert Datta\, Harvard Medical School\nBen de Bivort\, Harvard University\nOfer Feinerman\, Weizmann Institute of Science\nDeborah Gordon\, Stanford University\nAlbert Kao\, UMass Boston\nAnn Kennedy\, Scripps Research Institute\nHungtang Ko\, Tufts University\nGeorge Lauder\, Harvard University\nBence Ölveczky\, Harvard University\nKirstin Petersen\, Cornell University\nPavan Ramdya\, EPFL\nElizabeth Tibbetts\, University of Michigan\nRobert Wood\, Harvard University\n\n  \n\nSchedule (pdf) \nMonday\, Apr. 27\, 2026 \n9:00–9:30 am: Breakfast \n9:30–10:15 am: Deborah Gordon\, Stanford University\nTitle: The dynamics of collective behavior in changing environments\nAbstract: Collective behavior operates without central control\, using interactions among participants adjust to changing conditions. There is enormous diversity in the dynamics of collective behavior\, including in the rate of response to conditions\, in feedback regimes that set whether interactions stimulate or inhibit activity\, and the extent of centralization or modularity of information flow. An ecological perspective suggests how this diversity of collective behavior reflects the dynamics of the environment\, including its stability\, the ratio of resources spent to resources gained\, and the distribution of resources in time and space.\nAs examples\, I will discuss field studies and modelling of the regulation of foraging behavior in two species of ants\, Harvester ant colonies in the desert regulate foraging to manage high costs\, in water loss\, to obtain scattered and stable resources. They use a centralized system\, with the default to remain inactive unless stimulated\, that is slow to adjust foraging activity. In contrast\, the turtle ant colonies form trail networks in the canopy of the tropical forest\, in unstable conditions where activity costs are low\, to find and collect ephemeral and patchy resources. They use a highly modular system\, with the default to sustain activity unless inhibited\, that can rapidly adjust trail networks to changing resources and conditions. \n10:15–10:30 am: Discussion \n10:30–11:00 am: Tea Break \n11:00–11:45 am: Hungtang Ko\, Tufts University\nTitle: Collective mechanical intelligence: how fluid environments mediate self-organization of swarms\nAbstract: Biological collectives across scales self-organize within fluid environments. The mechanical coupling between swarming agents and fluid fields provides opportunities for both passive self-assembly and active\, fluid-mediated communication. However\, while sporadic evidence of collective mechanical intelligence exists\, its underlying mechanisms remain elusive. In this talk\, I will focus on two key systems: fire ant rafts and fish schools. Using a combination of experiments and mathematical models\, I will show that fire ant rafts leverage passive interfacial forces for self-assembly and self-stabilization. Furthermore\, I will demonstrate how schools of giant danio utilize mechanically intelligent formations in 3D\, and discuss how swarm robotics may provide the key to future research in collective mechanical intelligence. \n11:45 am–12:00 pm: Discussion \n12:00–1:30 pm: Catered Lunch \n1:30–2:15 pm: Albert Kao\, UMass Boston\nTitle: The limits and potential of collective wisdom\nAbstract: Over the past several years\, many studies have demonstrated\, both in theory and in experiments\, the ability of groups to make better decisions than individuals — a phenomenon known as collective wisdom. However\, the task types and experimental paradigms used vary considerably\, making comparisons across studies\, and consequently a unified theory of collective wisdom\, difficult. Here\, I derive a measure called the effective group size which allows for such comparisons. I use this measure to demonstrate several limitations to collective wisdom\, including when groups are large\, when there are correlations in opinions in the group\, and when information is passed down in chains. In addition\, collective wisdom is fragile in the face of an individual who has a disproportionate amount of power\, even if far from being a dictator. \n2:15–2:30 pm: Discussion \n2:30–3:15 pm: Ann Kennedy\, Scripps Research Institute\nTitle: Neural mechanisms that gate the expression of motivated behaviors\nAbstract: In order to survive and reproduce\, animals must set and weigh off goals in a way that is adaptive and responsive to the context of the environment. Intense evolutionary pressure has wired these algorithms of survival into the connectivity and gene expression patterns of the brain. In this talk\, I will present our lab’s recent work on the structure of animal behavior and its neural correlates\, showing how theory and modeling can uncover the computational mechanisms by which the brain sets survival goals and weighs off competing survival needs. I will first show how at the macroscopic level\, models of behavior as a feedback control system can help uncover control principles by which pressing survival needs can override less urgent drives. Next\, I will present new work exploring how macroscopic drives are translated into moment-to-moment behavioral choices. \n  \n3:15–4:30 pm: Discussion \n4:30–5:30 pm: CMSA Colloquium: Ofer Feinerman\, Weizmann Institute of Science\nTitle: Enacted collective cognition: Brainless problem-solving in weaver ants\nAbstract: Unlike most ants\, weaver ants construct their nests by pulling together leaves. Because individual ants are small relative to the leaves\, they assemble their bodies into temporary tools that bend the leaves into a hollow structure\, later stabilized with larval silk. Remarkably\, they achieve functional nests across a wide range of leaf shapes and configurations\, suggesting that this distributed system is capable of solving complex\, open-ended problems.\nTo understand how this is possible\, we performed laboratory experiments using controlled leaf configurations. In simple cases\, we show that ants can rely on a zipping heuristic that produces closed nests\, and we use differential geometry to demonstrate how flexible leaves are transformed into rigid structures. Crucially\, this zipping behavior forms a feedback loop in which ants continuously read and modify the evolving structure. In this sense\, the nest itself functions as a shared physical information system.\nThis suggests that cognition in this system is not located within individual ants\, but is enacted through the co-dynamics of the colony and the structure it builds. We present preliminary experiments with more complex leaf configurations\, showing that this process can solve increasingly challenging construction problems. Together\, these results point to a distributed\, brainless\, and enactive form of cognition. \n  \nTuesday\, April 28\, 2026 \n9:00–9:30 am: Breakfast \n9:30–10:15 am: Ben de Bivort\, Harvard University\nTitle: Bayesian Inference on biophysical models of connectomes\nAbstract: Recent progress in connectomics has opened new frontiers for understanding the underlying principles of neural circuits. By leveraging high-resolution maps of synaptic connections\, computational models can simulate neural dynamics with unprecedented detail. However\, it remains challenging to parsimoniously integrate circuit activity data with connectomic information to make biological in- sights. We propose a Bayesian framework as a principled method for bringing to bear existing data\, enabling uncertainty quantification for inferring parameters of interest\, as well as for predicted circuit outputs. To demonstrate this approach\, we implement a simple spiking neuron model using leaky- integrate-and-fire dynamics in the Drosophila olfactory circuit\, incorporating available firing rate data. We evaluate how models with varying levels of biological detail fit experimental data and examine how training on different subsets of data influences model predictions. \n10:15–10:30 am: Discussion \n10:30–11:00 am: Trainee talk: Yasuko Isoe\, Harvard University\nTitle: Divergent spatiotemporal integration of whole-field visual motion in medaka and zebrafish larvae\nAbstract: Cross-species comparisons offer powerful leverage for identifying conserved and divergent neural computations underlying innate behavior. Visual motion integration is a fundamental operation that stabilizes an animal’s position relative to its environment\, yet how its underlying algorithms vary across closely related vertebrate brains remains poorly understood. We investigated how zebrafish (Danio rerio) and medaka (Oryzias latipes) larvae implement visual motion integration using both free-swimming behavioral assays and head-fixed\, tail-free preparations\, the latter allowing us to confirm and extend our findings under precise stimulus control. Using whole-field motion stimuli\, we found that the two species employ distinct spatiotemporal filtering strategies. Medaka pool motion signals over larger visual fields and weight peripheral inputs more strongly\, whereas zebrafish rely more on motion signals directly beneath the body. Temporally\, zebrafish respond robustly to brief stimuli\, while medaka require longer stimulus durations and sustain motion-driven activity well after stimulus offset. Decomposition of turning behavior revealed separable control modules for large and small corrective maneuvers\, with species differences arising primarily from prolonged temporal integration in medaka. Together\, our results demonstrate how alterations in basic computational motifs — spatiotemporal pooling\, gain\, and persistence — can generate divergent visuomotor strategies across closely related vertebrate brains\, offering a window into the evolutionary diversification of sensorimotor computation. \n11:00–11:30 am: Trainee Talk: Siddharth Jayakumar\, Harvard University\nTitle: Mice follow scent trails using predictive policies\nAbstract: Animals must extract reliable information from noisy sensory signals. In olfaction\, this is especially challenging\, since cues are sparse and must be actively sampled. We asked how mice navigate odor trails under these conditions. Using an “infinite” paper treadmill\, we find that mice rapidly learn to track trails with high precision. Disrupting bilateral sampling introduces systematic\, lateralized errors\, consistent with a comparison of signals across the two sides. Individual inhalations near the trail trigger rapid corrective movements.\nInterestingly\, we find that mice do not follow trails purely reactively: deviations in tracking at unexpected trail bends reflected recent history\, indicating the use of short-term memory. We have begun to investigate the neural substrates of this behavior\, focusing on how sensory signals and predictive information are represented in the brain. Broadly\, our results suggest that odor-guided navigation depends on combining immediate sensory input with a short-term internal estimate\, enabling reliable tracking despite sparse cues. \n11:30 am–12:00 pm: Discussion \n12:00–1:30 pm: Catered Lunch \n1:30–2:15 pm: Noah Cowan\, Johns Hopkins University\nTitle: Toward a Control Theory for Active Sensing\nAbstract: Active sensing is often defined as “movement for the purpose of sensing.” Here\, I take a different perspective—that active sensing in biological systems is not a distinct class of behaviors\, but rather a set of movement phenomena that arise from a control objective. Biological sensors adapt to persistent stimuli\, acting like high-pass filters that tend to block “DC.” Such “change-detecting” sensors can support efficient coding with a high dynamic range\, and in engineering\, bio-inspired event cameras are similar: they transmit information only when a pixel changes and\, as such\, are extremely fast and make efficient use of bandwidth for the right applications. However\, such “AC” sensors pose technical challenges for control. Specifically\, event-like biological sensors can cause a nonlinear system (1) to lose local linear observability\, and (2) to become impossible to stabilize about an equilibrium point (Biswas\, Sontag\, Cowan\, Eur J Control\, 2025). Active sensing behaviors must emerge for stable control\, even in the somewhat paradoxical setting where the task-level goal is to remain stationary. Here\, I will discuss my lab’s progress in analyzing how animals use active sensing behaviors to format sensory information\, enhancing observability and control. I will also present our efforts to formalize controller synthesis with event-like sensors. \n2:15–2:30 pm: Discussion \n2:30–3:15 pm: Robert Datta\, Harvard Medical School\nTitle: Unveiling structure in natural behavior\nAbstract: Ethologists describing animals in the wild have long appreciated that naturalistic\, self-motivated behavior is built from modules that are linked together over time into predictable sequences. Many such sequences are built to extract information from the environment.\nAnd yet\, it remains unclear how the brain regulates the selection of individual behavioral modules for expression at any given moment\, or how it dynamically composes these modules into the fluid behaviors observed when animals act of their own volition\, and in the absence of experimental restraint\, task structure or explicit reward. Here we use novel methods for characterizing spontaneous mouse behavior to reveal mechanisms used by the brain to create the architecture of self-guided behavior. \n3:15–4:30 pm: Discussion \n  \nWednesday\, April 29\, 2026 \n9:00–9:30 am: Breakfast \n9:30–10:15 am: Kristin Branson\, HHMI\nTitle: How can generative AI help us understand animal behavior?\nAbstract: Understanding animal behavior at an algorithmic level — what animals attend to\, how they form internal world models\, goals\, and plans\, and how state maps to action — remains a central challenge in neuroethology. Large-scale behavioral experiments now produce trajectory datasets of extraordinary scale and complexity\, but existing approaches necessarily compress this complexity to just a few dimensions. We argue that generative AI offers a path toward rich\, query-able models of the data. We adapt transformer-based sequence modeling to multi-agent animal keypoint trajectories\, treating behavior forecasting as analogous to next-token prediction. Our agent-based network inputs biologically-motivated sensory representations and outputs the distribution of future pose velocities. We show that the model captures statistical properties of the behavioral distribution. We have built a Python library that encapsulates the complexity of transforms relating raw keypoints and model inputs and outputs to make these tools extensible by the NeuroAI community and accessible to theorists and experimentalists. Finally\, we argue that mechanistic interpretability methods allow us to query trained models through the natural framework of artificial neuroethology experiments. \n10:15–10:30 am: Discussion \n10:30–11:00 am: Tea Break \n11:00–11:30 am: Trainee talk: Golnar Gharooni Fard\, Harvard University\nTitle: The Geometry and Dynamics of Embodied Cognition: From Collective Architecture to Interspecies Navigation\nAbstract: Biological behavior is fundamentally an emergent property of the coupling between an agent’s physical form\, its environment\, and local interaction rules. In this talk\, I explore the mathematical principles of this “embodied cognition” across two distinct scales: the stigmergic spatial memory of honeybee collectives and the real-time dynamic coordination of human-bird mutualism. I’ll start by discussing static embodied intelligence through the lens of honeycomb construction. Using 3D-printed foundations to introduce controlled geometric frustration (including misalignment angles and lattice shifts) I demonstrate how honeybee collectives resolve structural mismatches through the adaptive placement of topological defects. I will show how these complex behavioral responses can be modeled as a physics-based potential minimization problem\, proving that the hive’s “intelligence” is a distributed response to local geometric cues. In the second part\, I transition to “dynamic” coordination by examining the mutualistic search for honeybee nests between humans and honeyguide birds in Africa. Unlike the persistent memory of the wax comb\, this interspecies cooperation requires real-time processing of noisy\, stochastic signals. I present a data-driven model of this interaction as a coupled tracking problem. By analyzing the interplay between human engagement and a leaky integrator memory constant\, I identify the sweet spots of temporal integration required to successfully filter bird behavior and maintain goal-oriented navigation. Together\, these two projects demonstrate that a data-driven physics-inspired modeling framework\, can uncover the fundamental rules of agent-environment coupling that drive adaptive behavior across biological scales. \n11:30 am–12:00 pm: Trainee talk: Wenyi Zhang\, Harvard University\nTitle: Mechanisms of Setpoint Control in Drosophila Navigation System\nAbstract: Navigation provides a powerful system for studying how animals balance behavioral persistence with flexibility. During navigation\, fruit flies often default to fast straight walking (or “menotaxis”) in a barren environment\, maintaining a stable heading setpoint over a long period of time. Conversely\, when the local environment is enriched with sensory stimuli\, flies often explore the environment with more frequent heading changes\, either through directed steering driven by a sequence of updating setpoints\, or through undirected turning driven by temporarily lifting the setpoint control. Although this framework suggests a central role for the setpoint in guiding navigation\, the neural mechanisms for flexible setpoint control remain unclear.\nHere we identified h∆A\, a central complex cell type involved in setpoint control. In an aversive heat paradigm\, hΔA played an important role in the fly’s sensory-driven deviation from the menotactic goal direction. We characterized hΔA population activity and found that it carries two separable activity components: a bump-like signal that encodes a slowly varying travel-direction-related setpoint\, and a spatially uniform signal associated with turning. We further identified modulatory inputs to hΔA that shape h∆A activity. Together\, these results support a model in which short- and long-timescale setpoints compete for steering control\, and suggest a circuit mechanism by which flies balance directional persistence with flexible reorientation under changing sensory conditions. \n12:00–1:30 pm: Catered Lunch \n1:30–2:15 pm: Bence Ölveczky\, Harvard University\nTitle: Using neuro-biomechanical simulations to probe neural control of learned skills\nAbstract: The goal of my lab is to decipher the circuit logic by which the brain learns and controls motor skills. The standard mechanistic approach is to dissect the underlying circuits brain area-by-brain area\, inferring function by relating recordings and perturbations within each to behavior. This runs into fundamental problems in highly recurrent systems\, where activity in any one node is shaped by the dynamics of the whole\, a problem compounded by the fact that the circuits we probe control a complex biomechanical body and not measurable features of behavior. I will discuss these challenges and present results suggesting that neuro-biomechanical simulation\, leveraging advances in physics simulation and AI\, can offer a powerful alternative window into the neural circuits underlying learned skills. \n2:15–2:30 pm: Discussion \n2:30–3:15 pm: Pavan Ramdya\, EPFL\nTitle: Object manipulation and affordance learning in Drosophila\nAbstract: Many animals must manipulate objects to perform tasks like pushing away debris when navigating over complex\, natural terrain. For previously unseen objects\, efficient manipulation requires that their affordances–the possible actions one can perform upon them–first be learned through experience. However\, the behavioral and neural mechanisms underlying the learning of object affordances remain largely unknown. To address this gap\, we show that adult Drosophila melanogaster flies can learn to push novel spherical objects without being given any explicit reward. To do this\, flies appear to learn the ball’s pushability affordance: pushing is delayed when animals are first exposed to an immobile ball\, and manipulating one ball accelerates pushing of a second one in a new context. Behavioral quantification of a large-scale neural silencing screen reveals that specific visual projection neurons and olfactory sensory neurons regulate initial reactions to the object while dopaminergic neurons and the mushroom bodies\, a center for learning and memory in insects\, are critical for generalizing object affordances. These findings open the door to a mechanistic understanding of object manipulation and affordance learning. \n3:15–4:30 pm: Discussion \n  \nThursday\, April 30\, 2026 \n9:00–9:30 am: Breakfast \n9:30–10:15 am: Pulkit Agrawal\, MIT\nTitle: What Robots Are Missing: Force Intelligence and Lifelong Learning\nAbstract: Modern robots can plan sophisticated motions\, yet they remain slow\, brittle\, and unreliable on tasks humans find effortless. The missing piece is not better planning\, but better force reasoning: knowing when\, where\, and how much force to apply under uncertainty and across diverse tasks. Force intelligence\, I argue\, is a unifying principle for scalable robotics—bridging dexterous manipulation and whole-body control. However\, even a force-aware robot that cannot learn from its own experience will remain brittle. Today’s systems are effectively frozen after training\, unable to adapt once deployed. Real-world autonomy instead demands learning in deployment: the ability to improve continuously from interactions\, failures\, and successes. In this talk\, I will present our lab’s recent work on lifelong learning and outline a future path for how combining it with force-centric design could enable reliable\, useful robots in the real world. \n10:15–10:30 am: Discussion \n10:30–11:00 am: Tea Break \n11:00–11:45 am: Antonio C. Costa\, Sorbonne University/Paris Brain Institute\nTitle: Unraveling the structure of behavioral variation: a dynamical approach to naturalistic data\nAbstract: Animal behavior varies widely\, both within the same individual over time and between individuals. While often overlooked\, this variation reflects hidden control variables and mechanisms that were shaped by evolution. For example\, variation in behavioral traits can help populations withstand environmental change\, while atypical motor patterns in neurological disorders may offer clues for personalized therapies. Comparing such complex behaviors is difficult. When dynamics are nonlinear and unfold over multiple timescales\, standard metrics based on summary statistics often miss meaningful differences. To address this\, we introduce a framework that encodes multiscale dynamics to compare behavior from data. By modeling nonlinear dynamics probabilistically (using transfer operators inferred from time-series data)\, we define a distance metric that captures behavioral differences across timescales. Tailored to finite\, noisy datasets\, our approach identifies principal axes of variation and enables rigorous clustering of individual trajectories. We demonstrate this framework in various biological systems\, including bacterial chemotaxis and larval zebrafish locomotion\, where the inferred axes of behavioral variation reflect underlying physiological variables and developmental histories. \n11:45 am–12:00 pm: Discussion \n12:00–1:30 pm: Catered Lunch \n1:30–2:15 pm: Elizabeth Tibbetts\, University of Michigan\nTitle: What paper wasps can teach us about the evolution of animal minds\nAbstract: Why do animals differ in their cognitive abilities? Some animals fail at apparently simple tasks\, while others have a remarkable capacity to collect\, retain\, and use information from the environment to guide their behavior. Although paper wasps brains are smaller than a grain of rice\, Tibbetts will show that wasps can perform seemingly complex behaviors like individual face recognition\, transitive inference\, social eavesdropping\, and concept learning. She will also describe experiments that take advantage of natural variation in behavior within and among wasp species to test how social interactions shape the development and evolution of cognitive abilities. \n2:15–2:30 pm: Discussion \n2:30–3:15 pm: Robert Wood\, Harvard University\nTitle: The Mechanical Side of Artificial Intelligence\nAbstract: Artificial Intelligence research typically focuses on perception\, learning\, and control methods to enable autonomous agents\, including robots\, to make and act on decisions in real-world scenarios. However\, even the most capable AI without a well-designed physical structure is of minimal use for canonical robotics tasks. Our research is focused on the design\, mechanics\, materials\, and manufacturing of novel robot platforms that make perception\, control\, or action easier or more robust for natural\, unstructured\, and often unpredictable environments. Key principles in this pursuit include bioinspired designs\, smart materials for novel sensors and actuators\, and the development of multi-scale\, multi-material manufacturing methods. This talk will illustrate this philosophy by highlighting the creation of three classes of robots with unique hardware challenges: bioinspired microrobots\, soft-bodied robots for manipulation\, and robots for interacting with delicate marine life. \n3:15–4:00 pm: Discussion \n4:00–5:00 pm: George Lauder\, Harvard University\nTitle: Fish schooling behavior from kinematics to hydrodynamics to energetics\nAbstract: Do fish moving in a school reduce their energetic costs compared to swimming alone? If so\, how does collective motion reduce the energy needed to move? Only within the last two years have experimental studies directly demonstrated that fish swimming in a group have lower energy expenditure than solitary locomotion. Most studies of how fish move in a collective have focused on understanding the potential benefits of swimming in fixed relative positions. But recent experiments on fish schooling behavior have revealed that fish within the school are nearly constantly rearranging their relative positions. In this talk I will show how fish in a school can save energy even if they do not maintain fixed positions. Analyses of water flow patterns within fish schools have been used to resolve this “paradox” and show that fish movement within a school creates hydrodynamic shelters with zones of reduced flow velocity that nearby fish can take advantage of. \n  \n 
URL:https://cmsa.fas.harvard.edu/event/bioshape2_2026/
LOCATION:CMSA 20 Garden Street Cambridge\, Massachusetts 02138 United States
CATEGORIES:Programs
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BEGIN:VEVENT
DTSTART;TZID=America/New_York:20260415T080000
DTEND;TZID=America/New_York:20260416T170000
DTSTAMP:20260711T064210
CREATED:20250502T183823Z
LAST-MODIFIED:20260423T163805Z
UID:10003751-1776240000-1776358800@cmsa.fas.harvard.edu
SUMMARY:Swampland and our Universe
DESCRIPTION:Swampland and our Universe \nDates: April 15–16\, 2026 \nLocation: Harvard CMSA\, Room G10\, 20 Garden Street\, Cambridge MA \nThe swampland program has inspired a range of new ideas in both cosmology and neutrino physics. This workshop brings together experts in neutrino physics\, dark energy\, dark matter\, early-universe cosmology\, and string theory to share insights on these developments and to discuss current and future experimental tests. \nSpeakers \n\nIgnatios Antoniadis\, IAS\, Princeton\nAlek Bedroya\, Princeton\nMike Boylan-Kolchin\, UT Austin\nM.C. Gonzalez-Garcia\, YITP Stony Brook & ICREA U. Barcelona\nMustapha Ishak-Boushaki\, UT Dallas\nMarc Kamionkowski\, Johns Hopkins\nMiguel Montero\, Institute of Theoretical Physics\, Madrid\nGeorges Obied\, U Chicago\nMatt Reece\, Harvard\nTracy Slatyer\, MIT\n\nOrganizers: Luis Anchordoqui (CUNY Lehman College)\, Sonia Paban (Harvard Physics)\, and  Cumrun Vafa (Harvard Physics) \n  \n \n  \n  \nVideos are available on the CMSA Youtube Swampland Playlist \nSchedule\n(download pdf) \nWednesday\, Apr. 15\, 2026 \n8:00–9:00 am\nBreakfast \n9:00–10:00 am\nMarc Kamionkowski\, Johns Hopkins: Dark-matter dynamics and new physics \nAbstract: Galactic halos that are spherical\, stationary\, and composed of collisionless dark matter are easy to describe mathematically. If dark matter decays or interacts or there is some departure from equilibrium or time evolution of the system\, all bets are off. In this case costly N-body simulations are required. If\, however\, one retains the assumption of spherical symmetry\, these systems can be evolved numerically with a far simpler algorithm that is easily coded run in a matter of minutes on a laptop\, rather than a day on a supercomputer. I will describe this approach and illustrate with simulations of self-interacting dark matter\, decaying dark matter (with and without anisotropic velocity distributions\, supermassive-black-hole growth\, tidal stripping\, mixed SIDM/CDM models. Come prepared with your own non-standard dark-matter model; we’ll see if we can simulate it during the talk! \n10:00–10:30 am\nCoffee Break \n10:30–11:30 am\nTracy Slatyer\, MIT: What (more) the CMB can teach us about dark matter \nAbstract: Observations of the cosmic microwave background have already provided critical evidence for dark matter\, but can also stringently constrain a range of dark matter properties. I will outline CMB constraints on dark matter properties based on purely gravitational effects\, and then discuss in more detail how both CMB anisotropies and the blackbody spectrum can be used to test dark matter interactions with the Standard Model. \n11:30 am–1:00 pm\nLunch Break (catered) \n1:00–2:00 pm\nAlek Bedroya\, Princeton: How Quantum Gravity Constrains Physics on the Largest Length Scales \nAbstract: I will review the hierarchy of energy scales in quantum gravity\, from the Hubble scale in the IR to the quantum-gravity cutoff and the Planck scale in the UV\, and emphasize the nontrivial UV/IR relations that connect them. I will discuss conjectures constraining scalar potentials and explain how they are related to the behavior of the other energy scales\, together with bottom-up arguments based on general principles of quantum gravity such as holography. In particular\, I will explain how well-motivated holographic assumptions lead to strong restrictions on scalar potentials\, ruling out parametrically prolonged accelerated expansion for positive potentials and obstructing parametric scale separation for negative potentials associated with AdS vacua. Title:
How Quantum Gravity Constrains Physics on the Largest Length Scales\nAbstract:
I will review the hierarchy of energy scales in quantum gravity\, from the Hubble scale in the IR to the quantum-gravity cutoff and the Planck scale in the UV\, and emphasize the nontrivial UV/IR relations that connect them. I will discuss conjectures constraining scalar potentials and explain how they are related to the behavior of the other energy scales\, together with bottom-up arguments based on general principles of quantum gravity such as holography. In particular\, I will explain how well-motivated holographic assumptions lead to strong restrictions on scalar potentials\, ruling out parametrically prolonged accelerated expansion for positive potentials and obstructing parametric scale separation for negative potentials associated with AdS vacua. \n2:00–2:30 pm\nCoffee Break \n2:30–3:30 pm\nMustapha Ishak-Boushaki\, UT Dallas: Persistent and serious challenge to the ΛCDM throne: Evidence for dynamical dark energy rising from combinations of different types of datasets \nAbstract: We derive multiple constraints on dark energy and compare dynamical dark energy models with a time-varying equation of state (w0waCDM) versus a cosmological constant model (LCDM). We use Baryon Acoustic Oscillation (BAO) from DESI and DES\, Cosmic Microwave Background from Planck with and without lensing from Planck and ACT (noted CMBL and CMB\, respectively)\, supernovae(SN)\, and cross-correlations between galaxy positions and galaxy lensing from DES. We use pairs or triplets of datasets where we exclude one type of dataset each time and categorize them as “NO SN”\, “NO CMB” and “NO BAO” combinations. In all cases\, we find that the combinations favor the w0waCDM model over LCDM\, with significance ranging from 2.0 to 3.0-sigma. The persistence of this pattern across various dataset combinations even when any of the datasets is excluded supports an overall validation of this trending result regardless of any specific dataset. Next\, we use larger combinations of these datasets after verifying their mutual consistency within the w0waCDM model. We find combinations that give robust significance levels\, with DESI+DESY6BAO+CMBL+SN giving 3.4-sigma. In sum\, while we need to remain cautious\, the trend and pattern of these results beyond any single type of dataset and their associated systematics presents a compelling overall portrait not in favor of the LCDM and constitutes a serious challenge to the model’s reign. A few other cosmological results will be provided. \n3:30–4:00 pm\nCoffee Break \n4:00–5:00 pm\nGeorges Obied\, U Chicago: The Dark Dimension and its interplay with DESI data \nAbstract: In this talk\, I will discuss the motivation for considering an extra mesoscopic Dark Dimension of length l ~ 1 – 10 microns\, taking into account theoretical and observational arguments. I will then talk about cosmological aspects of the Dark Dimension. In particular this scenario leads\, by the universal coupling of the Standard Model sector to bulk gravitons\, to massive spin 2 KK excitations of the graviton in the Dark Dimension (the “dark gravitons”) as an unavoidable dark matter candidate. Observations allow such an extra dimension of size in the micron range. Finally\, I will discuss how this scenario can naturally accommodate features recently observed by the DESI survey such as an effective dark energy equation of state that is smaller than -1. \n   \nThursday\, Apr. 16\, 2026 \n8:00–8:30 am\nBreakfast \n8:30–9:30 am\nMC Gonzalez-Garcia\, YITP Stony Brook & ICREA U. Barcelona: Massive Neutrinos in 2026: What we know\, what we do not know (yet?)\, and what we do not understand \nAbstract: In this talk I will present an update of the current understanding (and some not understanding) of the neutrino masses and the lepton mixing and some other minimal SM extensions as derived from direct scrutiny of the results of neutrino flavour oscillation experiments\, some other laboratory probes\, and the cosmos. \n9:30–10:00 am\nCoffee Break \n10:00–11:00 am\nMiguel Montero\, IFT\, Madrid: Neutrinos and B-L symmetry in the Dark Dimension scenario \nAbstract: The Dark Dimension proposes the existe of a micrometer-sized large extra dimension\, whose size is tied to the observed small vacuum energy. I will review the scenario\, and then discuss how to embed the B-L global symmetry of the SM\, focusing on one possibility which leads to an explanation of the observed coincidence between neutrino mass scale and the  vacuum energy scale\, while leading to 3 light species of right-handed neutrinos. I will also briefly discuss potential opportunities for detection of the resulting neutrino oscillations. \n11:00–11:30 am\nCoffee Break \n11:30 am–12:30 pm\nIgnatios Antoniadis\, IAS\, Princeton: Searching for the dark dimension in neutrino experiments \nAbstract: Micron size extra dimensions offer a possibility to explain the smallness of neutrino masses if the right-handed neutrino propagates in the higher dimensional bulk. I will discuss the theoretical framework and the experimental signatures of this proposal in present and future experiments of KATRIN prototype\, aiming to measure the magnitude of neutrino masses and to search for extra sterile-type species. \n12:30–1:30 pm\nLunch Break (catered) \n1:30–2:30 pm\nMike Boylan-Kolchin\, UT Austin: Galaxies as Tracers of the Matter Density Field \nAbstract: Galaxy formation is often (rightly) thought of as involving a complex interplay of messy astrophysical processes\, but it also traces the nonlinear evolution of the matter density in the Universe. Remarkably\, it appears that properties of this nonlinear field are intimately connected to properties of the initial linear fluctuations and some basic physics of dark matter interactions. I will explore some of these connections\, with applications that include the surprisingly fast evolution of early galaxy formation as revealed by JWST and properties of the lowest-mass dark matter clumps capable of hosting galaxies in the local Universe.\n2:30–3:00 pm\nCoffee Break \n3:00–4:00 pm\nMatt Reece\, Harvard: Axions from String Theory\, and String Theory from Axions \nAbstract: String theory compactifications contain the right ingredients to produce axion fields that might solve the Strong CP problem or contribute to dark matter or dynamical dark energy in our universe. After briefly reviewing some of these ingredients\, I will frame the inverse question: suppose that an axion is discovered\, and its decay constant is measured in an experiment. Could this help us to locate ourselves in the string landscape? In particular\, I will discuss how an axion could give us clues about the fundamental string scale and the scale of supersymmetry breaking. \n  \n  \n  \n  \n 
URL:https://cmsa.fas.harvard.edu/event/swampland2026/
LOCATION:CMSA 20 Garden Street Cambridge\, Massachusetts 02138 United States
CATEGORIES:Workshop
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END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/New_York:20260223T090000
DTEND;TZID=America/New_York:20260311T170000
DTSTAMP:20260711T064210
CREATED:20250502T183538Z
LAST-MODIFIED:20260325T134313Z
UID:10003750-1771837200-1773248400@cmsa.fas.harvard.edu
SUMMARY:Mathematics and Biology I: Morphometry\, Morphogenesis and Mathematics
DESCRIPTION:Mathematics and Biology I: Morphometry\, Morphogenesis and Mathematics \nDates: February 23–March 11\, 2026 \nLocation: Harvard CMSA\, Room G10\, 20 Garden Street\, Cambridge MA \nMathematics\, Morphometry and Morphogenesis is a 3-week program at the Harvard CMSA\, which will bring together researchers from a few different communities with a common aim—to understand shape and its development and evolution in living matter. \nThe aim is to bring together those interested in evolutionary and developmental biology\, soft and active matter physics\, and differential\, discrete and computational geometry and topology\, especially with a statistical bent. Although each of these fields has developed powerful tools and deep insights into form\, function\, and dynamics\, opportunities for them to meet and interact are rare. This workshop aims to foster dialogue and discovery across these disciplinary boundaries—where paleontologists\, developmental biologists\, physicists\, computer scientists and mathematicians can exchange ideas\, identify shared challenges\, and spark new collaborations. We envision this as a chance not only to showcase exciting advances within each domain\, but also to chart new directions together at the intersection of evolution\, development\, and geometry. \nThe first week will have a few tutorials on developmental and evolutionary aspects of morphology\, computational geometry\, statistics and dynamical systems\, along with a workshop-style meeting with research talks\, setting the stage for longer stays and new collaborations over the following weeks. \nPlease note that this is an in-person event. \n  \nWeek 1: Feb 23–27\, 2026: Teaching lectures and research seminars \nThe teaching lectures span a broad range of topics\, including statistical shape and morphometric analysis\, dynamical systems\, differential geometry\, and current themes in morphogenesis\, developmental biology\, and evolutionary developmental biology in Week 1. \n  \nWeek 2-3: March 2–5 & 10–11\, 2026: Research seminars and discussion \nWeeks 2 and 3 will cover development\, regeneration\, and evolution from quantitative\, morphometric\, and mathematical perspectives. \n  \nTopics include: \n🧬 Organoids & Tissue-Engineered Models \n🔬 Computational Imaging & Geometry \n⚙️ Biophysics\, Mechanics & Theory \n🌱 Developmental Biology & Evo-Devo \n  \nSpeakers: \n\nSalem al-Mosleh\, University of Maryland Eastern Shore\nVanessa Barone\, Stanford University\nYohannes Bellaiche\, Institut Curie\nAlain Chedotal\, Institut de la Vision\nGary P.T. Choi\, Chinese University of Hong Kong\nStefano Di Talia\, Duke University\nPaul Francois\, McGill University\nJianping Fu\, University of Michigan\nThomas Gregor\, Pasteur Institute & Princeton\nSahand Hormoz\, Harvard\nHelen James\, Smithsonian Institution\nPurnati Khuntia\, Harvard\nAllon Klein\, Harvard Medical School\nElena Kramer\, Harvard University\nThomas Lecuit\, College de France & IBDM\nDaniel Lew\, MIT\nL. Mahadevan\, Harvard\nM. Lisa Manning\, Syracuse\nAdam Martin\, MIT\nSean Megason\, Harvard\nNoah Mitchell\, University of Chicago\nAkankshi Munjal\, Duke\nNipam Patel\, MBL Woods Hole\nOlivier Pourquié\, Harvard Medical School\nAdrienne Roeder\, Cornell University\nMattia Serra\, UC San Diego\nSuraj Shankar\, University of Michigan\nAnuj Srivastava\, Johns Hopkins\nSebastian Streichan\, UC Santa Barbara\nBerta Verd\, University of Oxford\n\n  \nOrganizers: \n\nSalem al-Mosleh\, University of Maryland Eastern Shore\nVanessa Barone\, Stanford\nL. Mahadevan\, Harvard\nAkankshi Munjal\, Duke\nOlivier Pourquie\, Harvard\n\n  \nVideos from the program are available at the CMSA Youtube Channel. \nMathematics and Biology Playlist \nWeek 1: Feb 23–27\, 2026 – Workshop \nMonday\, 2/23/26 \n\n9:00–9:30 am: Breakfast\n\n9:30–10:30 am: Tutorial: Anuj Srivastava (Johns Hopkins) — Advances in Statistical Shape Analysis of Biological Structures\n\n10:30–11:00 am: Tea Break\n\n11:00 am–12:00 pm: Tutorial: Anuj Srivastava (Johns Hopkins) — Advances in Statistical Shape Analysis of Biological Structures\n\n12:00–1:30 pm: Lunch: CMSA Common Room\, catered\n\n1:30–2:15 pm: Research Talk: Noah Mitchell (University of Chicago) — Mechanical canalization of 3D chiral morphogenesis\n\nTuesday\, 2/24/26 \n\n9:00–9:30 am: Breakfast\n\n9:30–10:30 am: Tutorial: Mattia Serra (UCSD) —Tissue Flows\, Morphogen Transport and Positional Information: A Dynamical Systems Framework \n\n10:30–11:00 am: Tea Break\n\n11:00 am–12:00 pm: Tutorial: Mattia Serra (UCSD) — Tissue Flows\, Morphogen Transport and Positional Information: A Dynamical Systems Framework \n\n12:00–1:30 pm: Lunch Break\n\n1:30–2:15 pm: Research Talk: Paul Francois (McGill) — Waddington Landscapes in the Age of Machine Learning\n\nWednesday\, 2/25/26 \n\n9:00–9:30 am: Breakfast\n\n9:30–10:30 am: Tutorial: Olivier Pourquie (Harvard) — Segmentation and body axis\n\n10:30–11:00 am: Tea Break\n\n11:00 am–12:00 pm: Tutorial: Akankshi Munjal (Duke) — Principles of Tissue Morphogenesis\n\n12:00–1:30 pm: Lunch: CMSA Common Room\, catered\n\n1:30–2:15 pm: Research Talk: Allon Klein (Harvard) — Stochastic Cell State Transitions\n2:15–3:00 pm: Research Talk: Salem Al-Mosleh (University of Maryland) — Linking Geometry\, Evolution\, & Development of Bird Beaks\n\nThursday\, 2/26/26 \n\nIn-person discussions\n\nFriday\, 2/27/26 \n\n9:00–9:30 am: Breakfast\n\n9:30–10:30 am: Tutorial: Vanessa Barone (Stanford)\n\n10:30–11:00 am: Tea Break\n\n11:00 am–12:00 pm: Tutorial: Vanessa Barone (Stanford)\n\n12:00–1:30 pm: Lunch Break\n\n1:30–2:15 pm: Research Talk: Alain Chedotal (Institut de la Vision) — Tridimensional analysis of human development\n\n2:15–3:00 pm: Research Talk: Jianping Fu (University of Michigan) — Bioengineering Human Embryo and Organ Models\n\n\n  \nWeek 2: March 2–5\, 2026 \nMonday\, 3/2/26 \n\n9:00–9:30 am: Breakfast\n\n9:30–10:30 am: Research Talk: Thomas Lecuit (Collège de France) —Encoding neuronal shape in the stochastic dynamics of branching processes\n\n10:30–11:00 am: Tea Break\n\n11:00 am–12:00 pm: Research Talk: Danny Lew (MIT) — Tuning the Cell Polarity Circuit: location and number of polarity sites\n12:00–1:30 pm: Lunch: CMSA Common Room\, catered\n\n1:30–2:15 pm: Research Talk: Suraj Shankar (University of Michigan)\n2:15–2:50 pm: Trainee Research Talk: Wenhui Tang (Harvard) — Wetting dynamics and mechanics in human vertebrate somite formation\n2:50–3:05 pm: Tea Break\n3:05–3:35 pm: Trainee Research Talk: Ludwig Hoffmann (Harvard) — Shape deformations through mechanochemical feedback\n4:30–5:30 pm: CMSA Colloquium: L Mahadevan (Harvard) — Inverse problems in soft and active matter\n\nTuesday\, 3/3/26 \n\n9:00–9:30 am: Breakfast\n\n9:30–10:30 am: Research Talk: Adrienne Roeder (Cornell) — Mechanisms generating robustness in flower morphogenesis\n\n10:30–11:00 am: Tea Break\n\n11:00 am–12:00 pm: Research Talk: Gary Choi (Chinese University of Hong Kong) — Quantifying shape variation using quasi-conformal geometry\n\n12:00–1:30 pm: Lunch Break\n\n1:30–2:15 pm: Research Talk: Akankshi Munjal (Duke) — Shaping the inner ear from the Outside in\n\n2:15–2:50 pm: Trainee Research Talk: Sean McGeary (Harvard) — Uncovering principles of tissue organization with massively parallel cell-interaction assays\n2:50–3:05 pm: Tea Break\n3:05–3:35 pm: Trainee Research Talk: Oliver Inge (Harvard) —Combinatorial BMP4 and activin direct the choice between alternate routes to endoderm in a stem cell model of human gastrulation\n3:40–4:10 pm: Trainee Research Talk: Mehrana Raeisian Nejad (Harvard) — Stress-shape misalignment in confluent cell layers\n\nWednesday\, 3/4/26 \n\n9:00–9:30 am: Breakfast\n\n9:30–10:15 am: Research Talk: Nipam Patel (Marine Biology Lab\, Woods Hole) — Cellular Morphogenesis at the Nanoscale: Structural color in butterflies\n\n10:15–11:00 am: Tea Break\n\n11:00 am–12:00 pm: Research Talk: M. Lisa Manning (Syracuse) — Sparse mesenchymal cell networks as a fluid under tension (and possibly as tunable matter)\n\n12:00–1:30 pm: Lunch Break: CMSA Common Room\, catered\n\n1:30–2:30 pm: Research Talk: Research Talk: Stefano Di Talia (Duke) — Encoding Geometric Memory During Zebrafish Appendage Regeneration \n\n2:30–3:00 pm: Trainee Research Talk: Suhrid Ghosh (Harvard) — One Cell After Another: Mechanical Counting in Reproductive Evolution\n\n3:00–3:15 pm: Tea Break\n3:15–4:05 pm: Research Talk: Sean Megason (Harvard) — Algorithms for Creating Form: How multiscale control systems make development robust\n4:10–4:40 pm: Trainee Research Talk: Alexandru Bacanu (Harvard) — Forcing tissues into shape: mechanical development in the early human brain\n\nThursday\, 3/5/26 \n\n9:00–9:30 am: Breakfast\n\n9:30–10:30 am: Research Talk: Sebastian Streichen (UCSB) — Physics of Living Systems: From embryos to structured active matter\n\n10:30–11:00 am: Tea Break\n\n11:00 am–12:00 pm: Research Talk: Berta Verd (University of Oxford) — Evolving phenotypic diversity \n\n12:00–1:30 pm: Lunch Break\n\n1:30–2:15 pm: Research Talk: Elena Kramer (Harvard) — Life in a box: Generating developmental complexity while bound by cell walls\n\n2:20–2:50 pm: Trainee Research Talk: Beatrice Steinert (Brown) — Grids and Folds: Morphogenetic Mechanisms of Body Plan Organization\n\n2:50–3:05 pm: Tea Break\n3:05–3:35 pm: Trainee Research Talk: Rikki Garner (Harvard)\n3:40–4:10 pm: Trainee Research Talk: Chaitra Prabhakara (Harvard) — One Morphogen\, Diverse Patterns: Unraveling Muscle Formation Across the Embryonic Gut Axis\n\n  \nWeek 3: March 10–11\, 2026 \nTuesday\, 3/10/26 \n\n9:00–9:30 am: Breakfast\n\n9:30–10:30 am: Research talk: Adam Martin (MIT) — Getting in shape: geometry\, mechanics\, and signaling in living epithelia\n\n10:30–11:00 am: Tea break\n\n11:00 am–12:00 pm: Research talk: Yohannes Bellaiche (Institut Curie) — How do cells and tissues sense their size to tailor their dynamics during development?\n12:00–1:30 pm: Lunch Break\n\n1:30–2:15 pm: Research talk: Sahand Hormoz (Harvard) — Learning the rules of morphogenesis\n\nWednesday\, 3/11/26 \n\n9:00–9:30 am: Breakfast\n\n9:30–10:15 am: Research talk: Thomas Gregor (Pasteur Institute & Princeton) — From Fluctuations to Form: Empirical Laws and Scaling Principles in Development\n\n10:15–11:00 am: Research talk: Allison Kann (Harvard) — How to rebuild an organ: The cellular choreography of whole-body regeneration\n\n11:00–11:30 am: Tea Break\n11:30 am–12:00 pm: Research talk: Chandra Kuyyamudi Ashwinikumar (Harvard)\n12:00–12:30 pm: Research talk: Purnati Khuntia (Harvard) — Role of Nucleus in Building Epithelial Tissues \n1:00 pm: Lunch: CMSA Common Room\, catered\n\n\n\n  \n  \n  \n  \n  \n 
URL:https://cmsa.fas.harvard.edu/event/bioshape_2026/
LOCATION:CMSA 20 Garden Street Cambridge\, Massachusetts 02138 United States
CATEGORIES:Programs
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END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/New_York:20251117T090000
DTEND;TZID=America/New_York:20251119T170000
DTSTAMP:20260711T064210
CREATED:20250502T182846Z
LAST-MODIFIED:20251215T145740Z
UID:10003749-1763370000-1763571600@cmsa.fas.harvard.edu
SUMMARY:Conference on Geometry and Statistics
DESCRIPTION:Conference on Geometry and Statistics \nDates: November 17–19\, 2025 \nLocation: CMSA G10\, 20 Garden Street\, Cambridge MA & via Zoom \n  \nSpeakers \n\nCharles Fefferman\, Princeton University\nStephan Huckemann\, Georg-August Universität Göttingen\nSungkyu Jung\, Seoul National University\nKei Kobayashi\, Keio University\nClément Levrard\, Université de Rennes\nKer-Chau Li\, University of California\, Los Angeles\nRong Ma\, Harvard University\nSteve Marron\, University of North Carolina\nEzra Miller\, Duke University\nHans-Georg Müller\, University of California\, Davis\nWilderich Tuschmann\, Karlsruhe Institute of Technology\nMelanie Weber\, Harvard University\nAndrew Wood\, Australian National University\nHorng-Tzer Yau\, Harvard University\n\nOrganizer: Zhigang Yao\, National University of Singapore \n  \nYoutube Playlist \n  \nSCHEDULE \ndownload pdf \nMonday\, Nov. 17\, 2025 \n9:00–9:25 am\nMorning refreshments \n9:25–9:30 am\nIntroductions \n9:30–10:30 am\nSpeaker: Stephan Huckemann\, Georg-August Universität Göttingen\nTitle: The Probability of the Cut Locus of a Fréchet Mean\nAbstract: We show that the cut locus of a Fréchet mean of a random variable on a connected and complete Riemanian manifold has zero probability\, a result known previously in special cases (Le and Barden\, 2014) and conjectured in general. The proof is based on first order and second order considerations\, where the latter are based on a recent result by Générau (2020) on “Laplacians in the barrier sense”. This generalizes to Fréchet p-means for p > 2. The former allow also to rule out stickiness on Riemannian manifolds\, and for generalization to 1 <= p < 2\, with a conjecture. We close with discussing and conjecturing extensions to noncomplete manifolds and more general metric spaces. This is joint work with Alexander Lytchak. \n\nGénérau\, F. (2020). Laplacian of the distance function on the cut locus on a Riemannian manifold. Nonlinearity 33(8)\, 3928.\nLe\, H. and D. Barden (2014).  On the measure of the cut locus of a Fréchet mean. Bulletin of the London Mathematical Society 46(4)\, 698–708.\nLytchak\, A. and S. F. Huckemann (2025). Zero mass at the cut locus of a Fréchet mean on a Riemannian manifold. arXiv preprint arXiv:2508.00747.\n\n10:30–10:45 am\nbreak \n10:45 am–11:45 am\nSpeaker: Hans-Georg Müller\, University of California\, Davis\nTitle: Conformal Inference for Random Objects\nAbstract: The underlying probability measure of random objects\, i.e.\, metric-space-valued random variables\, can be probed by distance profiles. These are one-dimensional distributions of probability mass falling into balls of increasing radius. In a regression setting with Euclidean covariates X and responses Y that are random objects\, one can consider conditional Fréchet means that can be implemented with Fréchet regression and also conditional distance profiles\, conditioning on X. Conditional distance profiles can then be leveraged to obtain conditional average transport costs\, the expected cost for transporting a fixed conditional distance profile to a randomly selected conditional distance profile. The conditional average transport costs can then be utilized to obtain conditional conformity scores. In conjunction with the split conformal algorithm these scores lead to conditional prediction sets located in the object space with asymptotic conditional validity and attractive finite sample behavior. Based on joint work Hang Zhou (UNC). \n11:45 am–1:15 pm\nLunch (Catered) \n1:15–2:15 pm\nSpeaker: Horng-Tzer Yau\, Harvard\nTitle: Ramanujan property of random regular graphs and delocalization of random band matrices\nAbstract: In this lecture\, we review recent works on random matrices. The first result is about the normalized adjacency matrix of a random $d$-regular graph on $N$ vertices with any fixed degree $d\geq 3$ and denote its eigenvalues as $\lambda_1=d/\sqrt{d-1}\geq \lambda_2\geq\lambda_3\cdots\geq \lambda_N$. We establish the edge universality for random $d$-regular graphs\, namely\, the distributions of $\lambda_2$ and $-\lambda_N$ converge to the Tracy-Widom$_1$ distribution associated with the Gaussian Orthogonal Ensemble. As a consequence\, for sufficiently large $N$\, approximately $69\%$ of $d$-regular graphs on $N$ vertices.\nare Ramanujan\, meaning $\max\{\lambda_2\,|\lambda_N|\}\leq 2$. This resolves a conjecture by Sarnak and Miller-Novikoff-Sabelli\nThe second result concerns $ N \times N$ Hermitian $d$-dimensional random band matrices with band width $W$. In the bulk of the spectrum and in the large $ N $ limit\, we prove that all $ L^2 $- normalized eigenvectors are delocalized in all dimensions under suitable conditions on $W$ and $N$. In addition\, we proved that the eigenvalue statistics are given by those of the Gaussian unitary ensemble. \n2:15–2:45 pm\nbreak with refreshments \n2:45–3:45 pm\nSpeaker: Clément Levrard\, Université de Rennes\nTitle: Optimal reach estimation\nAbstract: The reach of an embedded submanifold\, a notion that dates back to the famous work Curvature measures of H. Federer\, may be understood as a scale under which the submanifold is flat enough so that traditional Euclidean techniques in statistics locally apply\, up to some approximation. I will expose several ways to estimate the reach from sample (on the submanifold)\, some of them being optimal from the point of view of minimax estimation theory. Along the way\, intermediate estimation problems of local and global quantities will arise (curvature estimation\, weak feature size estimation\, distance estimation\, etc.)\, for which various phenomenons can occur from a statistical point of view (different convergence rates\, inconsistency). This will be an opportunity to provide a selective overview of the state of the art on these issues. \n4:30–5:30 pm\nCMSA Colloquium\nSpeaker: Zhigang Yao (National University of Singapore)\nTitle: Interaction of Statistics and Geometry: A New Landscape for Data Science\nAbstract:  Classical statistics views data as real numbers or vectors in Euclidean space\, but modern challenges increasingly involve data with intrinsic geometric structures. A central problem in this direction is manifold fitting\, with origins in H. Whitney’s work of the 1930s. The Geometric Whitney Problems ask: given a set\, when can we construct a smooth 𝑑-dimensional manifold that approximates it\, and how accurately can we estimate it?\nIn this talk\, I will discuss recent progress on manifold fitting and its role in bridging geometry and data science. While many existing methods rely on restrictive assumptions\, the manifold hypothesis—that data often lie near non-Euclidean structures—remains fundamental in modern statistical learning. I will highlight both theoretical insights and algorithmic challenges\, drawing on recent works with\, as well as ongoing research. \nYoutube video \n  \nTuesday\, Nov. 18\, 2025 \n9:00–9:30 am\nMorning refreshments \n9:30–10:30 am\nSpeaker: Charles Fefferman\, Princeton University (via Zoom)\nTitle: Extrinsic and intrinsic manifold learning\, old and new\nAbstract: The talk will include an exposition of the old paper “Testing the manifold hypothesis”\, joint work with S. Mitter and H. Narayanan\, on extrinsic manifold learning (the manifold to be learned is assumed to be embedded in a high-dimensional Euclidean space). The talk will also include a new result on intrinsic manifold learning (the manifold to be learned is not assumed to be embedded\, and the data consist of intrinsic distances corrupted by noise)\, provided the result is proven by the time of the conference. \n10:30–10:45 am\nbreak \n10:45 am–11:45 am\nSpeaker: Steve Marron\, University of North Carolina\nTitle: Data Integration Via Analysis of Manifolds (DIVAM)\nAbstract: A major challenge in the age of Big Data is the integration of disparate data types into a single data analysis. That was tackled by Data Integration Via Analysis of Subspaces (DIVAS) in the context of data blocks measured on a common set of experimental cases. Joint variation was defined in terms of modes of variation having identical scores across data blocks. DIVAS allowed mathematically rigorous formulation of individual variation within each data block in terms of individual modes. The goal of DIVAM is to intrinsically extend the DIVAS approach to data objects lying in manifolds\, such as shape data. \n11:45 am–1:15 pm\nLunch Break \n1:15–2:15 pm\nSpeaker: Ker-Chau Li\, University of California\, Los Angeles\nTitle: Investigation of Data clouds: From Galton’s Ellipses to Explainable AI (XAI)\, modeling or molding?\nAbstract: Francis Galton’s seminal 1886 visualization of regression toward the mean in trait inheritance is arguably the first and most influential example of geometric thinking applied to statistical modeling. The pioneering geometric insight driving Galton’s use of elliptical contours to discover the bivariate normal distribution laid down the foundation for classic multivariate analysis (e.g.\, PCA\, canonical correlation) and profoundly impacts modern methods like diffusion models.\nStatistical models\, particularly those based on parsimony\, are effective for characterizing data distribution and facilitating scientific rule induction. However\, the rise of unstructured big data (like images) has challenged these parsimonious approaches\, necessitating the use of deep learning models. These models\, containing billions of parameters\, sacrifice transparency to excel in prediction. Seeking solutions to this “black-box” dilemma is now the heart of Explainable AI (XAI).\nLeveraging the simplicity of elementary geometric concepts\, this talk will present a new path toward interpretable and parsimonious XAI. Unstructured big data is highly plastic. Our approach moves beyond the standard data modeling perspective—which answers what the data is—and introduces a novel data molding perspective. This shift is key to unlocking the full potential of data’s plasticity\, allowing us to effectively answer the crucial question: what the data can be used for.\nI will first discuss a connection between manifold learning and my earlier works\, helical confounding and liquid association. I will then turn to the data molding perspective and present two novel notions: mold-compliance and artificial-trait configurative-generation (ATCG). These notions guide our recent efforts in formulating novel algorithms for image data investigation\, addressing issues like prediction validity and within-class heterogeneity. Data molding entails a dramatically different feature space extraction\, which consequently shifts the subsequent investigation on the data clouds from out-of-distribution (OOD) to mold-violation\, and from UMAP clustering to ATCG-induced hierarchical clustering. \n2:15–2:45 pm\nbreak with refreshments \n2:45–3:45 pm\nSpeaker: Andrew Wood\, Australian National University\nTitle: Empirical likelihood methods for Fréchet means on open books\nAbstract: The open book is a simple example of a stratified space that captures some (but not all) of the properties of stratified spaces. Central limit theory for open books plus relevant background is given by Hotz et al. (2013\, Annals of Applied Probability). In this talk I will describe some basic inference procedures for Fréchet means in open books based on empirical likelihood (Owen\, book\, 2001). Empirical likelihood (EL) is a type of nonparametric likelihood that can be useful for many types of data\, including manifold-valued data and data from stratified spaces. An EL approach to basic inference for Fréchet means will be described. In particular\, it will be shown how the non-regularity in the geometry of open books can result in non-regular behaviour in Wilks’s theorem (i.e. the large sample likelihood ratio test). The talk will also discuss difficulties in extending the EL inference theory from open books to more general stratified spaces\, where the difference in dimension of adjacent strata can be 2 or more. For discussion of more general stratified spaces than open books\, see the orthant spaces discussed in Barden and Le (2018\, Proc of London Math Society) and the general stratified space setting considered by Mattingly et al. (2023\, arxiv). \n3:45–4:00 pm\nbreak \n4:00–5:00 pm\nSpeaker: Wilderich Tuschmann\, Karlsruhe Institute of Technology\nTitle: A Spectator’s Perspective on the Manifold Hypothesis\nAbstract: At its core\, the Manifold Hypothesis asserts that real-world\, high-dimensional data is not uniformly or randomly distributed throughout its high-dimensional “ambient” space\, but concentrated on or near a low-dimensional manifold (or a collection of manifolds) embedded within that high-dimensional ambient space.\nIn my talk\, I will discuss reasons and facts that speak for as well as against this hypothesis and also address geometric alternatives. \n  \nWednesday\, Nov. 19\, 2025 \n9:00–9:30 am\nMorning refreshments \n9:30–10:30 am\nSpeaker: Melanie Weber\, Harvard University\nTitle: Ricci Curvature\, Ricci Flow\, and the Geometry of Learning\nAbstract: Geometric structure in data plays a crucial role in machine learning. In this talk\, we study this observation through the lens of Ricci curvature and its associated Ricci flow. We start by reviewing a discrete notion of Ricci curvature introduced by Ollivier and the geometric flow that it induces. We further discuss the relationship between discrete Ricci curvature and its continuous counterpart via discrete-to-continuum consistency results\, which imply that discrete Ricci curvature can provably characterize the geometry of a data manifold based on a finite sample. This provides a theoretical foundation for several applications of discrete Ricci curvature in machine learning\, two of which we discuss in the remainder of this talk. First\, we analyze learned feature representations in deep neural networks and show that they transform during training in ways that closely resemble a discrete Ricci flow. Our analysis reveals that nonlinear activations shape class separability and suggests geometry-informed training principles such as early stopping and depth selection. Second\, we turn to deep learning on graphs\, where we address representational limitations of state of the art graph neural networks through curvature-based data augmentations. We show that augmenting input graphs with geometric information provably increases the representational power of such models and yields performance gains in practice. \n10:30–10:45 am\nbreak \n10:45 am–11:45 am\nSpeaker: Ezra Miller\, Duke University\nTitle: Extracting bar lengths from multiparameter persistent homology\nAbstract: Persistent homology in one parameter can be summarized using bar codes or persistence diagrams\, which are elementary gadgets with many features amenable to vectorization and hence statistical analysis. For example\, early work with Bendich\, Marron\, Pieloch\, and Skwerer showed how to extract meaningful statistics from the top 100 bar lengths in persistent homology summaries of brain arteries. The story for persistent homology with multiple parameters\, on the other hand\, is still developing. Although it has the potential to be much more flexible and informative\, multipersistence has structural issues that present fundamental mathematical challenges. There is no consensus on what might be meant by a “bar”\, let alone “the top 100 bar lengths”. This talk recalls the basics of single and multiparameter persistent homology and discusses some of the mathematical issues\, including obstacles and potential routes forward. \n11:45 am–1:15 pm\nLunch Break \n1:15–2:15 pm\nSpeaker: Kei Kobayashi\, Keio University\nTitle: Metric Transformations of Data Spaces: Curvature Control and Related Developments\nAbstract: We present our proposed method of increasing the accuracy of data analysis by means of two transformations of the metric of the data space. The first transformation is based on the curve length defined by the integral of the power of the density function\, which can be computed approximately using an empirical graph; the second transformation can be interpreted as the extrinsic distance when the data space is embedded in a metric cone. The advantage of both distance transformations is that the hyperparameters allow the curvature to be monotonically transformed in a specific sense. Some statistical applications of these transformations and theoretical justifications are presented. Detailed analyses of the geodesics obtained by this method for several simple probability distributions will also be presented. The main part of this work is based on joint works with Henry P. Wynn. \n2:15–2:45 pm\nbreak with refreshments \n2:45–3:45 pm\nSpeaker: Sungkyu Jung\, Seoul National University\nTitle: Generalized Frechet means with random minimizing domains and its strong consistency\nAbstract: In this talk\, I will discuss a novel extension of Frechet means\, referred to as generalized  Frechet  means\, as a comprehensive framework for describing the characteristics of random elements. The generalized Frechet mean is defined as the minimizer of a cost function\, and the framework encompasses various extensions of Frechet means that have appeared in the literature. The most distinctive feature of the proposed framework is that it allows the domain of minimization for the empirical generalized Frechet means to be random and different from that of its population counterpart. This flexibility broadens the applicability of the Frechet mean framework to various statistical scenarios\, including sequential dimension reduction for non-Euclidean data. We establish a strong consistency theorem for generalized Frechet means. Applications such as verifying the consistency of principal geodesic analysis on the hypersphere\, compositional principal component analysis on the composition space\, and k-medoids clustering for data on a metric space will be discussed. \n3:45–4:00 pm\nbreak \n4:00–5:00 pm\nSpeaker: Rong Ma\, Harvard University\nTitle: Modern Nonlinear Embedding Methods Unpacked\nAbstract: Learning and representing low-dimensional structures from noisy\, high-dimensional data is a cornerstone of modern data science. Stochastic neighbor embedding algorithms\, a family of nonlinear dimensionality reduction and data visualization methods\, with t-SNE and UMAP as two leading examples\, have become very popular in recent years. Yet despite their wide applications\, these methods remain subject to points of debate\, including limited theoretical understanding\, ambiguous interpretations\, and sensitivity to tuning parameters. In this talk\, I will present our recent efforts to decipher and improve these nonlinear embedding approaches. Our key results include a rigorous theoretical framework that uncovers the intrinsic mechanisms\, large-sample limits\, and fundamental principles underlying these algorithms; a set of theory-informed practical guidelines for their principled use in trustworthy biological discovery; and a collection of new algorithms that address current limitations and improve performance in areas such as bias reduction and stability. Throughout the talk\, I will highlight how these advances not only deepen our theoretical understanding but also open new avenues for scientific discovery.
URL:https://cmsa.fas.harvard.edu/event/geostat_2025/
LOCATION:CMSA 20 Garden Street Cambridge\, Massachusetts 02138 United States
CATEGORIES:Conference
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END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/New_York:20251105T140000
DTEND;TZID=America/New_York:20251105T150000
DTSTAMP:20260711T064210
CREATED:20251027T142022Z
LAST-MODIFIED:20251027T144043Z
UID:10003826-1762351200-1762354800@cmsa.fas.harvard.edu
SUMMARY:Discovery of unstable singularity with machine precision
DESCRIPTION:New Technologies in Mathematics Seminar \nSpeaker: Yongji Wang\, NYU Courant Institute of Mathematical Sciences \nTitle: Discovery of unstable singularity with machine precision \nAbstract: Whether singularities can form in fluids remains a foundational unanswered question in mathematics. This phenomenon occurs when solutions to governing equations\, such as the 3D Euler equations\, develop infinite gradients from smooth initial conditions. Historically\, numerical approaches have primarily identified stable singularities. However\, these are not expected to exist for key open problems\, such as the boundary-free Euler and Navier-Stokes cases\, namely the Millennium Prize problem. For these problems\, the true challenge lies in finding unstable singularities\, which are exceptionally elusive\, as any tiny perturbation can divert the system from its blow-up trajectory. \nIn this talk\, I will present a new computational framework which has led to the first systematic discovery of new families of unstable singularities in various fluid equations. Our approach merges curated machine learning architectures with a multi-stage training scheme and a high-precision Gauss-Newton optimizer\, creating a powerful tool for navigating the complex landscape of nonlinear PDEs. Beyond discovering these singularities\, the precision of this method is another key breakthrough\, achieving unprecedented accuracies on the order of $O(10^{-13})$—a level constrained only by the round-off errors of the GPU hardware. This level of precision meets the stringent requirements for rigorous mathematical validation of the discovered solution via computer-assisted proofs\, offering a new pathway to resolving long-standing challenges in mathematical physics. \n 
URL:https://cmsa.fas.harvard.edu/event/newtech_11525/
LOCATION:CMSA 20 Garden Street Cambridge\, Massachusetts 02138 United States
CATEGORIES:New Technologies in Mathematics Seminar
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END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/New_York:20251006T090000
DTEND;TZID=America/New_York:20251010T170000
DTSTAMP:20260711T064210
CREATED:20250502T180256Z
LAST-MODIFIED:20260422T160144Z
UID:10003747-1759741200-1760115600@cmsa.fas.harvard.edu
SUMMARY:Mathematical foundations of AI
DESCRIPTION:Mathematical foundations of AI \nDate: October 6–10\, 2025 \nLocation: Harvard CMSA\, Room G10\, 20 Garden Street\, Cambridge MA & via Zoom \nArtificial intelligence (AI) has achieved unprecedented advances\, yet our theoretical understanding lags significantly behind. This gap poses a significant obstacle to improving AI’s safety and reliability. Since the classical tools of learning theory have proven insufficient for understanding AI\, researchers are now drawing insights from a vast array of fields—including functional analysis\, probability theory\, optimal transport\, optimization\, PDEs\, information theory\, geometry\, statistics\, electrical engineering\, and ergodic theory. Those interdisciplinary efforts are gradually shedding light on the underlying principles governing modern AI. This workshop centers around these mathematical and interdisciplinary developments. It will feature a series of talks from people in various subfields. Open problem and small-group sessions will help foster new connections and new research avenues. \n  \n Speakers \n\nJason Altschuler\, University of Pennsylvania\nGuy Bresler\, MIT\nSinho Chewi\, Yale University\nLenaic Chizat\, EPFL\nNabarun Deb\, University of Chicago\nEdgar Dobriban\, University of Pennsylvania\nAhmed El Alaoui\, Cornell University\nZhou Fan\, Yale University\nBoris Hanin\, Princeton University\nJason Klusowski\, Princeton University\nTengyu Ma\, Stanford University\nAlexander Rakhlin\, MIT\nYuting Wei\, University of Pennsylvania\nTijana Zrnic\, Stanford University\n\nOrganizer: Morgane Austern\, Harvard Statistics \n  \nSchedule \nMonday\, Oct. 6\, 2025 \n\n\n\n8:30–9:00 am\nMorning refreshments\n\n\n9:00–10:00 am\nYuting Wei\, U Penn \nTo Intrinsic Dimension and Beyond: Efficient Sampling in Diffusion Models \nThe denoising diffusion probabilistic model (DDPM) has become a cornerstone of generative AI. While sharp convergence guarantees have been established for DDPM\, the iteration complexity typically scales with the ambient data dimension of target distributions\, leading to overly conservative theory that fails to explain its practical efficiency. This has sparked recent efforts to understand how DDPM can achieve sampling speed-ups through automatic exploitation of intrinsic low dimensionality of data. This talk explores two key scenarios: (1) For a broad class of data distributions with intrinsic dimension k\, we prove that the iteration complexity of the DDPM scales nearly linearly with k\, which is optimal under the KL divergence metric; (2) For mixtures of Gaussian distributions with k components\, we show that DDPM learns the distribution with iteration complexity that grows only logarithmically in k. These results provide theoretical justification for the practical efficiency of diffusion models.\n\n\n10:00–10:30 am\nBreak\n\n\n10:30–11:30 am\nJason Klusowski\, Princeton \nThe Value of Side Information in Unlabeled Data \nPractitioners often work in settings with limited labeled data and abundant unlabeled data. During training\, they may even have access to extra side information (some labeled\, some not) that won’t be available once the model is deployed. When can this side information actually improve performance? I’ll present a simple framework where a rich-view model that sees the extra features generates pseudo-labels on the large unlabeled data\, and a deployment model that only sees the standard features is trained on both real and pseudo-labels. The two are trained iteratively: each deployment model update calibrates the next round of pseudo-labels\, and those refined pseudo-labels in turn guide the deployment model. Our theory shows that side information helps precisely when the rich-view and deployment models make different kinds of errors. We formalize this with a decorrelation score that quantifies how independent those errors are; the more independent\, the greater the performance gains.\n\n\n11:3 0am–12:00 pm\nBreak\n\n\n12:00–1:00 pm\nGuy Bresler\, MIT \nGlobal Minimizers of Sigmoid Contrastive Loss \nThe meta-task of obtaining and aligning representations through contrastive pre-training is steadily gaining importance since its introduction in CLIP and ALIGN. In this paper we theoretically explain the advantages of synchronizing with trainable inverse temperature and bias under the sigmoid loss\, as implemented in the recent SigLIP models of Google DeepMind. Temperature and bias can drive the loss function to zero for a rich class of configurations that we call (m\,b)-Constellations. (m\,b)-Constellations are a novel combinatorial object related to spherical codes and are parametrized by a margin m and relative bias b. We use our characterization of constellations to theoretically justify the success of SigLIP on retrieval\, to explain the modality gap present in SigLIP\, and to identify the necessary dimension for producing high-quality representations. We also propose a reparameterization of the sigmoid loss with explicit relative bias\, which appears to improve training dynamics. Joint work with Kiril Bangachev\, Iliyas Noman\, and Yury Polyanskiy.\n\n\n\n  \nTuesday\, Oct. 7\, 2025 \n\n\n\n8:30–9:00 am\nMorning refreshments\n\n\n9:00–10:00 am\nLénaïc Chizat\, EPFL \nThe Hidden Width of Deep ResNets \nWe present a mathematical framework to analyze the training dynamics of deep ResNets that rigorously captures practical architectures (including Transformers) trained from standard random initializations. Our approach combines stochastic approximation of ODEs with propagation-of-chaos arguments. It yields three main insights:\n– Depth begets width: infinite-depth ResNets of any hidden width behave throughout training as if they were infinitely wide;\n– Unified phase diagram: the phase diagram of Transformers mirrors that of two-layer perceptrons\, once the appropriate substitutions are made;\n– Optimal shape scaling: for a given parameter budget P\, a Transformer with optimal shape converges to its limiting dynamics at rate P^{-1/6}.\nThis is based on https://arxiv.org/abs/2509.10167\n\n\n10:00–10:30 am\nBreak \n \n\n\n10:30–11:30 am\nBoris Hanin\, Princeton \nKernel Learning on Manifolds \nThis talk concerns the L_2 risk of minimum norm interpolation with n samples in the RKHS of a kernel K. Unlike most prior work in this space our kernels will be defined on any close d-dimensional Riemannian manifold\, and we require only that the kernels are trace class and elliptic. With these assumptions we get nearly sharp L_2 risk bounds with high probability over the data. Like prior work on round spheres our results essentially say that the number of samples n\, the dimension of the manifold\, and some details of the kernel determine a natural spectral cutoff \lambda(n\,d\,K) and that minimal norm interpolation essentially learns exactly the projection of the data generating process onto the eigenfunctions of the Laplacian with frequency at most \lambda(n\,d\,K). Joint work with Mengxuan Yang.\n\n\n11:30–12:00\nBreak\n\n\n12:00–1:00\nZhou Fan\, Yale \nDynamical mean-field analysis of adaptive Langevin diffusions \nIn many applications of statistical estimation via sampling\, one may wish to sample from a high-dimensional target distribution that is adaptively evolving to the samples already seen. We study an example of such dynamics\, given by a Langevin diffusion for posterior sampling in a Bayesian linear regression model with i.i.d. regression design\, whose prior continuously adapts to the Langevin trajectory via a maximum marginal-likelihood scheme. Using techniques of dynamical mean-field theory (DMFT)\, we provide a precise characterization of a high-dimensional asymptotic limit for the joint evolution of the prior parameter and law of the Langevin sample. We then carry out an analysis of the equations that describe this DMFT limit\, under conditions of approximate time-translation-invariance which include\, in particular\, settings where the posterior law satisfies a log-Sobolev inequality. In such settings\, we show that this adaptive Langevin trajectory converges on a dimension-independent time horizon to an equilibrium state that is characterized by a system of replica-symmetric fixed-point equations\, and the associated prior parameter converges to a critical point of a replica-symmetric limit for the model free energy. We explore the nature of the free energy landscape and its critical points in a few simple examples\, where such critical points may or may not be unique.\n\n\n\n  \nWednesday\, Oct. 8\, 2025 \n\n\n\n8:30–9:00 am\nMorning refreshments\n\n\n9:00–10:00 am\nJason Altschuler\, U Penn \nNegative Stepsizes Make Gradient-Descent-Ascent Converge \nSolving min-max problems is a central question in optimization\, games\, learning\, and controls. Arguably the most natural algorithm is Gradient-Descent-Ascent (GDA)\, however since the 1970s\, conventional wisdom has argued that it fails to converge even on simple problems. This failure spurred the extensive literature on modifying GDA with extragradients\, optimism\, momentum\, anchoring\, etc. In contrast\, we show that GDA converges in its original form by simply using a judicious choice of stepsizes. The key innovation is the proposal of unconventional stepsize schedules that are time-varying\, asymmetric\, and (most surprisingly) periodically negative. We show that all three properties are necessary for convergence\, and that altogether this enables GDA to converge on the classical counterexamples (e.g.\, unconstrained convex-concave problems). The core intuition is that although negative stepsizes make backward progress\, they de-synchronize the min/max variables (overcoming the cycling issue of GDA) and lead to a slingshot phenomenon in which the forward progress in the other iterations is overwhelmingly larger. This results in fast overall convergence. Geometrically\, the slingshot dynamics leverage the non-reversibility of gradient flow: positive/negative steps cancel to first order\, yielding a second-order net movement in a new direction that leads to convergence and is otherwise impossible for GDA to move in. Joint work with Henry Shugart.\n\n\n10:00–10:30 am\nBreak\n\n\n10:30–11:30 am\nNabarun Deb\, U Chicago \nGenerative Modeling via Parabolic Monge-Ampère PDEs \nWe introduce a novel generative modeling framework based on a discretized parabolic Monge-Ampère PDE\, which emerges as a continuous limit of the Sinkhorn algorithm commonly used in optimal transport. Our method performs iterative refinement in the space of Brenier maps using a mirror gradient descent step. We establish theoretical guarantees for generative modeling through the lens of no-regret analysis\, demonstrating that the iterates converge to the optimal Brenier map under a variety of step-size schedules. As a technical contribution\, we derive a new Evolution Variational Inequality tailored to the parabolic Monge-Ampère PDE\, connecting geometry\, transportation cost\, and regret. Our framework accommodates non-log-concave target distributions\, constructs an optimal sampling process via the Brenier map\, and integrates favorable learning techniques from generative adversarial networks and score-based diffusion models.\n\n\n11:30–12:00\nBreak\n\n\n12:00–1:00\nSinho Chewi\, Yale \nDiscretization and distribution learning in diffusion models \nFirst\, I will review some literature on discretization of diffusion models\, focusing on the use of randomized midpoints for deterministic vs. stochastic samplers. Then\, I will argue that such sampling guarantees reduce distribution learning\, in the form of learning to generate a sample\, to score matching. To complement this result\, we reduce other forms of distribution learning (parameter estimation and density estimation) to score matching as well. This leads to new consequences for diffusion models\, such as asymptotic efficiency of a DDPM-based parameter estimator and algorithms for Gaussian mixture density estimation\, as well as to a general approach for establishing cryptographic hardness results for score estimation.\n\n\n\n  \nThursday\, Oct. 9\, 2025 \n\n\n\n8:30–9:00 am\nMorning refreshments\n\n\n9:00–10:00 am\nAhmed El Alaoui\, Cornell \nHow abundant are good interpolators? \nWe consider classifying labelled data in the interpolation regime where there exist linear classifiers (with possibly negative margin) correctly classifying all points in the dataset. Under the logistic model with gaussian features\, we derive the large deviation rate function of the event that an interpolator chosen uniformly at random achieves a given generalization error. This describes the proportion of interpolators having any desired performance. We remark that in a wide regime of parameters\, the vast majority of interpolators have inferior performance than the one found via a simple linear programming procedure\, showing that the latter algorithm produces an atypically good classifier.\nThis is based on joint work with August Chen.\n\n\n10:00–10:30 am\nbreak\n\n\n10:30–11:30 am\nTengyu Ma\, Stanford \nSelf-play LLM Theorem Provers with Iterative Conjecturing and Proving \nI will discuss some works on using RL for theorem proving\, especially in the possible future regime where we ran out of high-quality training data. To keep improving the models with limited data\, we draw inspiration from mathematicians\, who continuously develop new results\, partly by proposing novel conjectures or exercises (which are often variants of known results) and attempting to solve them. We design the Self-play Theorem Prover (STP) that simultaneously takes on two roles\, conjecturer and prover\, each providing training signals to the other. The model achieves state-of-the-art performance among whole-proof generation methods on miniF2F-test (65.0%\, pass@3200)\, Proofnet-test (23.9%\, pass@3200) and PutnamBench (8/644\, pass@3200). \n \n\n\n11:30–12:00\nbreak\n\n\n12:00–1:00\nEdgar Dobriban\, U Penn \nLeveraging synthetic data in statistical inference \nThe rapid proliferation of high-quality synthetic data — generated by advanced AI models or collected as auxiliary data from related tasks — presents both opportunities and challenges for statistical inference. This paper introduces a GEneral Synthetic-Powered Inference (GESPI) framework that wraps around any statistical inference procedure to safely enhance sample efficiency by combining synthetic and real data. Our framework leverages high-quality synthetic data to boost statistical power\, yet adaptively defaults to the standard inference method using only real data when synthetic data is of low quality. The error of our method remains below a user-specified bound without any distributional assumptions on the synthetic data\, and decreases as the quality of the synthetic data improves. This flexibility enables seamless integration with conformal prediction\, risk control\, hypothesis testing\, and multiple testing procedures\, all without modifying the base inference method. We demonstrate the benefits of our method on challenging tasks with limited labeled data\, including AlphaFold protein structure prediction\, and comparing large reasoning models on complex math problems.\n\n\n\n  \nFriday\, Oct. 10\, 2025 \n\n\n\n8:30–9:00 am\nMorning refreshments\n\n\n9:00–10:00 am\nTijana Zrnic\, Stanford \nProbably Approximately Correct Labels \nObtaining high-quality labeled datasets is often costly\, requiring either extensive human annotation or expensive experiments. We propose a method that supplements such “expert” labels with AI predictions from pre-trained models to construct labeled datasets more cost-effectively. Our approach results in probably approximately correct labels: with high probability\, the overall labeling error is small. This solution enables rigorous yet efficient dataset curation using modern AI models. We demonstrate the benefits of the methodology through text annotation with large language models\, image labeling with pre-trained vision models\, and protein folding analysis with AlphaFold. This is joint work with Emmanuel Candes and Andrew Ilyas.\n\n\n10:00–10:30 am\nBreak\n\n\n10:30–11:30 am\nAlexander Rakhlin\, MIT \nElements of Interactive Decision Making \nMachine learning methods are increasingly deployed in interactive environments\, ranging from dynamic treatment strategies in medicine to fine-tuning of LLMs using reinforcement learning. In these settings\, the learning agent interacts with the environment to collect data and necessarily faces an exploration-exploitation dilemma. We present a general framework for interactive decision making that subsumes multi-armed bandits\, contextual bandits\, structured bandits\, and reinforcement learning. We focus on both the statistical aspect of learning—aiming to develop a tight characterization of sample complexity in terms of properties of the class of models—and on the basic algorithmic primitives.\n\n\n\n  \n  \n\n  \n 
URL:https://cmsa.fas.harvard.edu/event/mathai/
LOCATION:CMSA 20 Garden Street Cambridge\, Massachusetts 02138 United States
CATEGORIES:Workshop
ATTACH;FMTTYPE=image/jpeg:https://cmsa.fas.harvard.edu/media/MathAI.5.jpg
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/New_York:20250915T090000
DTEND;TZID=America/New_York:20250918T170000
DTSTAMP:20260711T064210
CREATED:20250710T134311Z
LAST-MODIFIED:20250930T154307Z
UID:10003755-1757926800-1758214800@cmsa.fas.harvard.edu
SUMMARY:The Geometry of Machine Learning
DESCRIPTION:The Geometry of Machine Learning \nDates: September 15–18\, 2025 \nLocation: Harvard CMSA\, Room G10\, 20 Garden Street\, Cambridge MA 02138 \nDespite the extraordinary progress in large language models\, mathematicians suspect that other dimensions of intelligence must be defined and simulated to complete the picture. Geometric and symbolic reasoning are among these. In fact\, there seems to be much to learn about existing ML by considering it from a geometric perspective\, e.g. what is happening to the data manifold as it moves through a NN?  How can geometric and symbolic tools be interfaced with LLMs? A more distant goal\, one that seems only approachable through AIs\, would be to gain some insight into the large-scale structure of mathematics as a whole: the geometry of math\, rather than geometry as a subject within math. This conference is intended to begin a discussion on these topics. \nSpeakers \n\nMaissam Barkeshli\, University of Maryland\nEve Bodnia\, Logical Intelligence\nAdam Brown\, Stanford\nBennett Chow\, USCD & IAS\nMichael Freedman\, Harvard CMSA\nElliot Glazer\, Epoch AI\nJames Halverson\, Northeastern\nJesse Han\, Math Inc.\nJunehyuk Jung\, Brown University\nAlex Kontorovich\, Rutgers University\nYann Lecun\, New York University & META*\nJared Duker Lichtman\, Stanford  & Math Inc.\nBrice Ménard\, Johns Hopkins\nMichael Mulligan\, UCR & Logical Intelligence\nPatrick Shafto\, DARPA & Rutgers University\n\nOrganizers: Michael R. Douglas (CMSA) and Mike Freedman (CMSA) \n  \nGeometry of Machine Learning Youtube Playlist \n  \nSchedule \nMonday\, Sep. 15\, 2025 \n\n\n\n8:30–9:00 am\nMorning refreshments\n\n\n9:00–10:00 am\nJames Halverson\, Northeastern \nTitle: Sparsity and Symbols with Kolmogorov-Arnold Networks \nAbstract: In this talk I’ll review Kolmogorov-Arnold nets\, as well as new theory and applications related to sparsity and symbolic regression\, respectively.  I’ll review essential results regarding KANs\, show how sparsity masks relate deep nets and KANs\, and how KANs can be utilized alongside multimodal language models for symbolic regression. Empirical results will necessitate a few slides\, but the bulk will be chalk.\n\n\n10:00–10:30 am\nBreak\n\n\n10:30–11:30 am\nMaissam Barkeshli\, University of Maryland \nTitle: Transformers and random walks: from language to random graphs \nAbstract: The stunning capabilities of large language models give rise to many questions about how they work and how much more capable they can possibly get. One way to gain additional insight is via synthetic models of data with tunable complexity\, which can capture the basic relevant structures of real data. In recent work we have focused on sequences obtained from random walks on graphs\, hypergraphs\, and hierarchical graphical structures. I will present some recent empirical results for work in progress regarding how transformers learn sequences arising from random walks on graphs. The focus will be on neural scaling laws\, unexpected temperature-dependent effects\, and sample complexity.\n\n\n11:30 am–12:00 pm\nBreak\n\n\n12:00–1:00 pm\nAdam Brown\, Stanford \nTitle: LLMs\, Reasoning\, and the Future of Mathematical Sciences \nAbstract: Over the last half decade\, the mathematical capabilities of large language models (LLMs) have leapt from preschooler to undergraduate and now beyond. This talk reviews recent progress\, and speculates as to what it will mean for the future of mathematical sciences if these trends continue.\n\n\n\n  \nTuesday\, Sep. 16\, 2025 \n\n\n\n8:30–9:00 am\nMorning refreshments\n\n\n9:00–10:00 am\nJunehyuk Jung\, Brown University \nTitle: AlphaGeometry: a step toward automated math reasoning \nAbstract: Last summer\, Google DeepMind’s AI systems made headlines by achieving Silver Medal level performance on the notoriously challenging International Mathematical Olympiad (IMO) problems. For instance\, AlphaGeometry 2\, one of these remarkable systems\, solved the geometry problem in a mere 19 seconds! \nIn this talk\, we will delve into the inner workings of AlphaGeometry\, exploring the innovative techniques that enable it to tackle intricate geometric puzzles. We will uncover how this AI system combines the power of neural networks with symbolic reasoning to discover elegant solutions.\n\n\n10:00–10:30 am\nBreak\n\n\n10:30–11:30 am\nBennett Chow\, USCD and IAS \nTitle: Ricci flow as a test for AI\n\n\n11:30 am–12:00 pm\nBreak\n\n\n12:00–1:00 pm\nJared Duker Lichtman\, Stanford & Math Inc. and Jesse Han\, Math Inc. \nTitle: Gauss – towards autoformalization for the working mathematician \nAbstract: In this talk we’ll highlight some recent formalization progress using a new agent – Gauss. We’ll outline a recent Lean proof of the Prime Number Theorem in strong form\, completing a challenge set in January 2024 by Alex Kontorovich and Terry Tao. We hope Gauss will help assist working mathematicians\, especially those who do not write formal code themselves.\n\n\n5:00–6:00 pm\nSpecial Lecture: Yann LeCun\, Science Center Hall C\n\n\n\n  \nWednesday\, Sep. 17\, 2025 \n\n\n\n8:30–9:00 am\nRefreshments\n\n\n9:00–10:00 am\nMichael Mulligan\, UCR and Logical Intelligence \nTitle: Spontaneous Kolmogorov-Arnold Geometry in Vanilla Fully-Connected Neural Networks \nAbstract: The Kolmogorov-Arnold (KA) representation theorem constructs universal\, but highly non-smooth inner functions (the first layer map) in a single (non-linear) hidden layer neural network. Such universal functions have a distinctive local geometry\, a “texture\,” which can be characterized by the inner function’s Jacobian\, $J(\mathbf{x})$\, as $\mathbf{x}$ varies over the data. It is natural to ask if this distinctive KA geometry emerges through conventional neural network optimization. We find that indeed KA geometry often does emerge through the process of training vanilla single hidden layer fully-connected neural networks (MLPs). We quantify KA geometry through the statistical properties of the exterior powers of $J(\mathbf{x})$: number of zero rows and various observables for the minor statistics of $J(\mathbf{x})$\, which measure the scale and axis alignment of $J(\mathbf{x})$. This leads to a rough phase diagram in the space of function complexity and model hyperparameters where KA geometry occurs. The motivation is first to understand how neural networks organically learn to prepare input data for later downstream processing and\, second\, to learn enough about the emergence of KA geometry to accelerate learning through a timely intervention in network hyperparameters. This research is the “flip side” of KA-Networks (KANs). We do not engineer KA into the neural network\, but rather watch KA emerge in shallow MLPs.\n\n\n10:00–10:30 am\nBreak\n\n\n10:30–11:30 am\nEve Bodnia\, Logical Intelligence \nTitle: \nAbstract: We introduce a method of topological analysis on spiking correlation networks in neurological systems. This method explores the neural manifold as in the manifold hypothesis\, which posits that information is often represented by a lower-dimensional manifold embedded in a higher-dimensional space. After collecting neuron activity from human and mouse organoids using a micro-electrode array\, we extract connectivity using pairwise spike-timing time correlations\, which are optimized for time delays introduced by synaptic delays. We then look at network topology to identify emergent structures and compare the results to two randomized models – constrained randomization and bootstrapping across datasets. In histograms of the persistence of topological features\, we see that the features from the original dataset consistently exceed the variability of the null distributions\, suggesting that the observed topological features reflect significant correlation patterns in the data rather than random fluctuations. In a study of network resiliency\, we found that random removal of 10 % of nodes still yielded a network with a lesser but still significant number of topological features in the homology group H1 (counts 2-dimensional voids in the dataset) above the variability of our constrained randomization model; however\, targeted removal of nodes in H1 features resulted in rapid topological collapse\, indicating that the H1 cycles in these brain organoid networks are fragile and highly sensitive to perturbations. By applying topological analysis to neural data\, we offer a new complementary framework to standard methods for understanding information processing across a variety of complex neural systems.\n\n\n11:30 am–12:00 pm\nBreak\n\n\n12:00–1:00 pm\nAlex Kontorovich\, Rutgers University \nTitle: The Shape of Math to Come \nAbstract: We will discuss some ongoing experiments that may have meaningful impact on what working in research mathematics might look like in a decade (if not sooner).\n\n\n5:00–6:00 pm\nMike Freedman Millennium Lecture: The Poincaré Conjecture and Mathematical Discovery (Science Center Hall D)\n\n\n\n  \nThursday\, Sep. 18\, 2025 \n\n\n\n8:30–9:00 am\nMorning refreshments\n\n\n9:00–10:00 am\nElliott Glazer\, Epoch AI \nTitle: FrontierMath to Infinity \nAbstract: I will discuss FrontierMath\, a mathematical problem solving benchmark I developed over the past year\, including its design philosophy and what we’ve learned about AI’s trajectory from it. I will then look much further out\, speculate about what a “perfectly efficient” mathematical intelligence should be capable of\, and discuss how high-ceiling math capability metrics can illuminate the path towards that ideal.\n\n\n10:00–10:30 am\nBreak\n\n\n10:30–11:30 am\nBrice Ménard\, Johns Hopkins \nTitle:Demystifying the over-parametrization of neural networks \nAbstract: I will show how to estimate the dimensionality of neural encodings (learned weight structures) to assess how many parameters are effectively used by a neural network. I will then show how their scaling properties provide us with fundamental exponents on the learning process of a given task. I will comment on connections to thermodynamics.\n\n\n11:30 am–12:00 pm\nBreak\n\n\n12:00–12:30 pm\nPatrick Shafto\, Rutgers \nTitle: Math for AI and AI for Math \nAbstract: I will briefly discuss two DARPA programs aiming to deepen connections between mathematics and AI\, specifically through geometric and symbolic perspectives. The first aims for mathematical foundations for understanding the behavior and performance of modern AI systems such as Large Language Models and Diffusion models. The second aims to develop AI for pure mathematics through an understanding of abstraction\, decomposition\, and formalization. I will close with some thoughts on the coming convergence between AI and math.\n\n\n12:30–12:45 pm\nBreak\n\n\n12:45–2:00 pm\nMike Freedman\, Harvard CMSA \nTitle: How to think about the shape of mathematics \nFollowed by group discussion \n \n\n\n\n  \n  \n  \nSupport provided by Logical Intelligence. \n \n  \n 
URL:https://cmsa.fas.harvard.edu/event/mlgeometry/
LOCATION:CMSA 20 Garden Street Cambridge\, Massachusetts 02138 United States
CATEGORIES:Conference,Event
ATTACH;FMTTYPE=image/jpeg:https://cmsa.fas.harvard.edu/media/GML_2025.7-scaled.jpg
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/New_York:20250908T090000
DTEND;TZID=America/New_York:20250910T170000
DTSTAMP:20260711T064210
CREATED:20250502T174228Z
LAST-MODIFIED:20260422T141418Z
UID:10003660-1757322000-1757523600@cmsa.fas.harvard.edu
SUMMARY:Math and Machine Learning Reunion Workshop
DESCRIPTION:Math and Machine Learning Reunion Workshop \nDates: September 8–10\, 2025 \nLocation: Harvard CMSA\, Room G10\, 20 Garden Street\, Cambridge MA \nMachine learning and AI are increasingly important tools in all fields of research. In the fall of 2024\, the CMSA Mathematics and Machine Learning Program hosted 70 mathematicians and machine learning experts\, ranging from beginners to established leaders in their field\, to explore ML as a research tool for mathematicians\, and mathematical approaches to understanding ML. More than 20 papers came out of projects started and developed during the program. The MML Reunion workshop will be an opportunity for the participants to share their results\, review subsequent developments\, and develop directions for future research. \nInvited Speakers \n\nAngelica Babei\, Howard University\nGergely Bérczi\, Aarhus University\nJoanna Bieri\, University of Redlands\nGiorgi Butbaia\, University of New Hampshire\nRandy Davila\, RelationalAI\, Rice University\nAlyson Deines\, IDA/CCR La Jolla\nSergei Gukov\, Caltech\nYang-Hui He\, University of Oxford\nMark Hughes\, Brigham Young University\nKyu-Hwan Lee\, University of Connecticut\nEric Mjolsness\, UC Irvine\nMaria Prat Colomer\, Brown University\nSébastien Racanière\, Google DeepMind\nEric Ramos\, Stevens Institute of Technology\nTamara Veenstra\, IDA-CCR La Jolla\n\nOrganizer:Michael Douglas\, CMSA \n\nSchedule \nMonday Sep. 8\, 2025 \n\n\n\n9:00–9:30 am\nMorning refreshments\n\n\n9:30–9:45 am\nIntroductions\n\n\n9:45–10:45 am\nAngelica Babei\, Howard University\nTitle: Predicting Euler factors of elliptic curves\nAbstract: Two non-isogenous elliptic curves will have distinct traces of Frobenius at a large enough prime\, and a finite set of $a_p(E)$ values determines all others. However\, even when enough $a_p(E)$ values are provided to uniquely identify the isogeny class\, no efficient algorithm is known for determining the remaining $a_p(E)$ values from this finite set. Preliminary results show that ML models can learn to predict the next trace of Frobenius with a surprising degree of accuracy from relatively few nearby entries. We investigate some possible reasons for this performance. Based on joint work with François Charton\, Edgar Costa\, Xiaoyu Huang\, Kyu-Hwan Lee\, David Lowry-Duda\, Ashvni Narayanan\, and Alexey Pozdnyakov.\n\n\n10:45–11:00 am\nBreak\n\n\n11:00 am–12:00 pm\nKyu-Hwan Lee\, University of Connecticut\nTitle: Machine learning mutation-acyclicity of quivers\n\n\n12:00–1:30 pm\nLunch\n\n\n1:30–2:30 pm\nGergely Bérczi\, Aarhus University\nTitle: Diffusion Models for Sphere Packings\n\n\n2:30–2:45 pm\nBreak\n\n\n2:45–3:45 pm\nRandy Davila\, RelationalAI\, Rice University\nTitle: Recent Developments in Automated Conjecturing\nAbstract: The dream of a machine capable of generating deep mathematical insight has inspired decades of research—from Fajtlowicz’s Graffiti program in graph theory and chemistry to DeepMind’s neural breakthroughs in knot theory. In this talk\, we briefly trace the evolution of automated conjecturing systems and present recent advances that deepen our understanding of what it means for machines to conjecture—a pursuit long embodied by our system\, TxGraffiti. Building on this legacy\, we introduce a new framework that integrates optimization\, enumeration\, and convex geometric methods with creative heuristics and symbolic translation. This extended system produces not only conjectured inequalities\, but also necessary and sufficient condition statements\, which can then be automatically ranked by IRIS (Inequality Ranking and Inference System) model and translated into Lean 4 for formal verification. The result is a flexible architecture capable of generating precise\, human-readable\, and logically rigorous conjectures with minimal manual intervention.\nWe showcase results across a range of mathematical areas\, including graph theory\, polyhedral theory\, number theory\, and—for the first time—conjectures in string theory\, derived from the dataset of complete intersection Calabi–Yau (CICY) threefolds. Together\, these developments suggest that with the right blend of structure\, strategy\, and aesthetic\, machines can generate conjectures that not only withstand scrutiny but invite it—offering a glimpse into a future where AI contributes meaningfully to the creative process of mathematics.\n\n\n3:45–4:00 pm\nBreak\n\n\n4:00–5:00 pm\nEric Ramos\, Stevens Institute of Technology\nTitle: An AI approach to a conjecture of Erdos\nAbstract: Given a graph G\, its independence sequence is the integral sequence a_1\,a_2\,…\,a_n\, where a_i is the number of independent sets of vertices of size i. In the 90’s Erdos and coauthors showed that this sequence need not be unimodal for general graphs\, but conjectured that it is always unimodal whenever G is a tree. This conjecture was then naturally generalized to claim that the independence sequence of trees should be log concave\, in the sense that a_i^2 is always above a_{i-1}a_{i+1}. This stronger version of the conjecture was shown to hold for all trees of at most 25 vertices. In 2023\, however\, using improved computational power and a considerably more efficient algorithm\, Kadrawi\, Levit\, Yosef\, and Mirzrachi proved that there were exactly two trees on 26 vertices whose independence sequence was not log concave. They also showed how these two examples could be generalized to create two families of trees whose members are all not log concave. Finally\, in early 2025\, Galvin provided a family of trees with the property that for any chosen positive integer k\, there is a member T of the family where log concavity breaks at index alpha(T) – k\, where alph(T) is the independence number of T. Outside of these three families\, not much else was known about what causes log concavity to break.In this presentation\, I will discuss joint work of myself and Shiqi Sun\, where we used the PatternBoost architecture to train a machine to find counter-examples to the log concavity conjecture. We will discuss the successes of this approach – finding tens of thousands of new counter-examples with vertex set sizes varying from 27 to 101 – and some of its fascinating failures.\n\n\n\n  \nTuesday\, Sep. 9\, 2025 \n\n\n\n9:00–9:30 am\nMorning refreshments\n\n\n9:30–10:30 am\nMaria Prat Colomer\, Brown University\nTitle: From PINNs to Computer-Assisted Proofs for Fluid Dynamics\nAbstract: Physics-Informed Neural Networks (PINNs) have emerged as an alternative to traditional numerical methods for solving partial differential equations (PDEs). We apply PINNs to the study of low regularity problems in fluid dynamics\, focusing on the incompressible 2D Euler equations. In particular\, we study V-states\, which are a class of weak\, non-smooth solutions for which the vorticity is the characteristic function of a domain that rotates with constant angular velocity. We have obtained an approximate solution of a limiting V-state using a PINN and we are currently working on a rigourous proof of the existence of a nearby solution through a computer-assisted proof. Our PINN-based numerical approximation significantly improves on traditional methods\, a key factor being the integration of prior mathematical knowledge of the problem to effectively explore the solution space.\n\n\n10:30–11:00 am\nBreak\n\n\n11:00 am–12:00 pm\nSebastian Racaniere\, Google DeepMind\nTitle: Generative models and high dimensional symmetries: the case of Lattice QCD\nAbstract: Applying normalizing flows\, a machine learning technique for mapping distributions\, to Lattice QCD offers a promising route to enhance simulations and overcome limitations of traditional methods like Hybrid Monte Carlo. LQCD aims to compute expectation values of observables from an intractable distribution defined over a lattice of fields. Normalizing flows can learn this complex distribution and generate new configurations\, improving efficiency and addressing challenges such as critical slowing down and topological freezing. Topological freezing\, in particular\, traps simulations in local minima and prevents exploration of the full configuration space\, affecting accuracy. This approach incorporates the symmetries of LQCD through gauge equivariant flows\, leading to successful definitions and good effective sample sizes on smaller lattices. Beyond accelerating configuration generation\, normalizing flows also find application in variance reduction for observable calculation and exploring phenomena at different scales within LQCD. While further research is needed to apply these methods at the scale of state-of-the-art LQCD calculations\, these advancements hold significant potential to improve the accuracy\, efficiency\, and reach of future simulations.\n\n\n12:00–1:30 pm\nLunch break\n\n\n1:30–2:30 pm\nSergei Gukov\, Caltech\nTitle: On sparse reward problems in mathematics\nAbstract: An alternative title for this talk could be “Learning Hardness.” To see why\, we will explore some long-standing open problems in mathematics and examine what makes them hard from a computational perspective. We will argue that\, in many cases\, the difficulty arises from a highly uneven distribution of hardness within families of related problems\, where the truly hard cases lie far out in the tail. We will then discuss how recent advances in AI may provide new tools to tackle these challenges. Based in part on the recent work with A.Shehper\, A.Medina-Mardones\, L.Fagan\, B.Lewandowski\, A.Gruen\, Y.Qiu\, P.Kucharski\, and Z.Wang.\n\n\n2:30–2:45 pm\nBreak\n\n\n2:45–3:45 pm\nAlyson Deines\, IDA-CCR La Jolla; Tamara Veenstra\, IDA-CCR La Jolla; Joanna Bieri\, University of Redlands\nTitle: Machine learning $L$-functions\nAbstract: We study the vanishing order of rational $L$-functions and Maass form $L$-functions from a data scientific perspective. Each $L$-function is represented by finitely many Dirichlet coefficients\, the normalization of which depends on the context. We observe murmurations by averaging over these datasets. For rational $L$-functions\, we find that PCA clusters rational $L$-functions by their vanishing order and record that LDA and neural networks may accurately predict this quantity. For Maass form $L$-functions\, while PCA does not cluster these $L$-functions\, we still find that LDA and neural networks may accurately predict this quantity.\n\n\n3:45–4:00 pm\nBreak\n\n\n4:00–5:00 pm\nMark Hughes\, Brigham Young University\nTitle: Modelling the concordance group via contrastive learning\nAbstract: The concordance group of knots in 3-space is an abelian group formed by the equivalence classes of knots under the relation of concordance\, where two knots are concordant if they are the boundary of a smooth annulus properly embedded in the 4-dimensional product space S^3 x I. Though studied since 1966\, properties of the concordance groups (and even the recognition problem of deciding when a knot is null-concordant\, or slice) are difficult to study. In this talk I will outline ongoing attempts to model the concordance group using contrastive learning. This is joint work with Onkar Singh Gujral.\n\n\n\n  \n  \nWednesday Sep. 10\, 2025 \n\n\n\n9:00–9:30 am\nMorning refreshments\n\n\n9:30–10:30 am\nYang-Hui He\, University of Oxford (Via Zoom)\nTitle: AI for Mathematics: Bottom-up\, Top-Down\, Meta-\nAbstract: We argue how AI can assist mathematics in three ways: theorem-proving\, conjecture formulation\, and language processing. Inspired by initial experiments in geometry and string theory in 2017\, we summarize how this emerging field has grown over the past years\, and show how various machine-learning algorithms can help with pattern detection across disciplines ranging from algebraic geometry to representation theory\, to combinatorics\, and to number theory. At the heart of the programme is the question how does AI help with theoretical discovery\, and the implications for the future of mathematics.\n\n\n10:30–11:00 am\nBreak\n\n\n11:00 am–12:00 pm\nGiorgi Butbaia\, University of New Hampshire\nTitle: Computational String Theory using Machine Learning\nAbstract: Calabi-Yau compactifications of the $E_8\times E_8$ heterotic string provide a promising route to recovering the four-dimensional particle physics described by the Standard Model. While the topology of the Calabi-Yau space determines the overall matter content in the low-energy effective field theory\, further details of the compactification geometry are needed to calculate the normalized physical couplings and masses of elementary particles. In this talk\, we present novel numerical techniques for computing physically normalized Yukawa couplings in a number of heterotic models in the standard embedding using geometric machine learning and equivariant neural networks. We observe that the results produced using these techniques are in excellent agreement with the expected values for certain special cases\, where the answers are known. In the case of the Tian-Yau manifold\, which defines a model with three generations and has $h^{2\,1}>1$\, we provide a first-of-its-kind calculation of the normalized Yukawa couplings. As part of this work\, we have developed a Python library called cymyc\, which streamlines calculation of the Calabi-Yau metric and the Yukawa couplings on arbitrary Calabi-Yau manifolds that are realized as complete intersections and provides a framework for studying the differential geometric properties\, such as the curvature.\n\n\n12:00–1:30 pm\nLunch break\n\n\n1:30–2:30 pm\nEric Mjolsness\, UC Irvine\nTitle: Graph operators for science-applied AI/ML\nAbstract: Scalable\, structured graphs play a central role in mathematical problem definition for scientific applications of artificial intelligence and machine learning. Qualitatively diverse kinds of operators are necessary to bring these graphs to life. Continuous-time processes govern the evolution of spatial graph embeddings and other graph-local differential equation systems\, as well as the flow of probability between locally similar graph structures in a probabilistic Fock space\, according to rules in a dynamical graph grammar (DGG). Both kinds of dynamics have biophysical application eg. to dynamic cytoskeleton\, and both obey graph-centric time-evolution operators in an operator algebra that can be differentiated for learning. On the other hand coarse-scale discrete jumps in graph structure such as global mesh refinement can be modeled with a “graph lineage”: a sequence of sparsely interrelated graphs whose size grows roughly exponentially with level number. Graph lineages permit the definition of substantially more cost-efficient skeletal graph products\, as versions of classic binary graph operators such as the Cartesian product and direct product of graphs\, with analogous but not identical properties. Application to deep neural networks and to multigrid numerical methods are shown.\nThese two graph operator frameworks are interrelated. Further graph lineage operators allow the definition of graph frontier spaces\, accommodating graph grammars and supporting the definition of skeletal graph-graph function spaces. In return\, “confluent” graph grammars e.g. for adaptive mesh generation permit the definition of graph lineages through iteration. I will also sketch the design of compatible AI for Science systems that may exploit DGGs.\nJoint work with Cory Scott and Matthew Hur.\n\n\n2:30–3:00 pm\nBreak\n\n\n3:00–5:00 pm\nPanel and Discussion Group: Jordan Ellenberg\, Tamara Veenstra\, Sébastien Racaniere\, Kyu-Hwan Lee\, Sergei Gukov\n\n\n\n  \n\n  \n  \n 
URL:https://cmsa.fas.harvard.edu/event/mml_2025/
LOCATION:CMSA 20 Garden Street Cambridge\, Massachusetts 02138 United States
CATEGORIES:Event,Workshop
ATTACH;FMTTYPE=image/jpeg:https://cmsa.fas.harvard.edu/media/MML_Reunion_poster.2.jpg
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/New_York:20250903T160000
DTEND;TZID=America/New_York:20250903T173000
DTSTAMP:20260711T064210
CREATED:20250729T195223Z
LAST-MODIFIED:20250805T182154Z
UID:10003758-1756915200-1756920600@cmsa.fas.harvard.edu
SUMMARY:Fall CMSA Welcome Event
DESCRIPTION:Fall CMSA Welcome Event \nDate: September 3\, 2025 \nTime: 4:00 pm \nLocation: CMSA Common Room\, 20 Garden Street\, Cambridge MA \n  \nAll CMSA and Math affiliates are invited. \n 
URL:https://cmsa.fas.harvard.edu/event/welcome925/
LOCATION:CMSA 20 Garden Street Cambridge\, Massachusetts 02138 United States
CATEGORIES:Event
ATTACH;FMTTYPE=image/jpeg:https://cmsa.fas.harvard.edu/media/CMSA_Wwlecome-2023-IMG_9367.jpg
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/New_York:20250602T090000
DTEND;TZID=America/New_York:20250604T170000
DTSTAMP:20260711T064210
CREATED:20241107T214041Z
LAST-MODIFIED:20250605T193626Z
UID:10003619-1748854800-1749056400@cmsa.fas.harvard.edu
SUMMARY:Summer School in Total Positivity and Quantum Field Theory
DESCRIPTION:Summer School in Total Positivity and Quantum Field Theory \nDates: June 2–4\, 2025 \nLocation: CMSA\, 20 Garden Street\, Cambridge MA \n\n\nIn the past decade\, there has been a great deal of interest and progress in the study of algebro-combinatorial and geometric structures appearing across diverse areas of physics\, from particle physics to cosmology. As these research programs expand\, there is an ever-growing need for mathematicians and physicists to collaborate effectively and build a shared language. Join us at Harvard University’s Center of Mathematical Sciences and Applications for a week-long summer school dedicated to addressing these interdisciplinary connections. The school welcomes graduate students\, postdocs\, and early-career researchers drawn to the intersection of mathematics and physics. Whether you are an algebraic combinatorialist looking for a better grasp on the physics\, a high energy theorist trying to figure out the math\, or a newcomer to both fields\, this summer school offers an ideal opportunity for you to learn. \n\n\nCourses taught by both mathematicians and physicists will connect ideas from total positivity\, matroid theory\, discrete geometry\, and real algebraic geometry with fundamental questions in quantum field theory. Topics will include amplituhedra\, cluster algebras\, and positive geometry as they relate to scattering amplitudes and cosmological correlators in high-energy physics. Our courses are designed to be accessible to a varied audience; speakers will be mindful of the diverse backgrounds of the participants from both fields. \nAmid this exciting period of collaboration between mathematicians and physicists\, we look forward to exploring these rich\, cutting-edge topics with you. \n\nCourses: \n\nPositive Grassmannian and Cluster Algebras\, Lara Bossinger (Instituto de Matemáticas Universidad Nacional Autónoma de México)\nslides  | exercises\n\n  \n\nPositive Geometry and Canonical Forms\, Simon Telen (MPI Leipzig)\nslides\n\n  \n\nScattering Amplitudes and Amplituhedra\, Marcus Spradlin (Brown)\nexercises\n\n  \n\nCosmology and Cosmological Polytopes\, Nima Arkani-Hamed (IAS)\n\n  \n\nOrganizers:  Jonathan Boretsky (McGill University) |  Matteo Parisi (Harvard CMSA and IAS Princeton) | Lauren Williams (Harvard University) \n\nYoutube Playlist \nSchedule  \nMonday\, June 2\, 2025 \n\n\n\n8:30–9:00 am\nMorning Reception\n\n\n9:00–10:00 am\nLara Bossinger: Positive Grassmannian and Cluster Algebras I\n\n\n10:00–10:30 am\nCoffee Break\n\n\n10:30–11:10 am\nExercises\n\n\n11:10 am–12:10 pm\nNima Arkani-Hamed: Cosmology and Cosmological Polytopes I\n\n\n12:10–2:00 pm\nLunch Break\n\n\n2:00–3:00 pm\nNima Arkani-Hamed: Cosmology and Cosmological Polytopes II\n\n\n3:00–3:30 pm\nCoffee Break\n\n\n3:30–4:10 pm\nExercises\n\n\n4:10–5:10 pm\nNima Arkani-Hamed: Cosmology and Cosmological Polytopes III\n\n\n\n  \nTuesday\, June 3\, 2025 \n\n\n\n8:30–9:00 am\nMorning Reception\n\n\n9:00–10:00 am\nLara Bossinger: Positive Grassmannian and Cluster Algebras II\n\n\n10:00–10:30 am\nCoffee Break\n\n\n10:30–11:30 am\nMarcus Spradlin: Scattering Amplitudes and Amplituhedra I\n\n\n11:30 am–12:10 pm\nExercises\n\n\n12:10–2:00 pm\nLunch Break\n\n\n2:00–3:00 pm\nSimon Telen: Definitions and first examples of positive geometries\n\n\n3:00–3:30 pm\nCoffee Break\n\n\n3:30–4:30 pm\nLightning Talks\n\n\n4:30–5:30 pm\nSimon Telen: Positive geometry of polytopes\n\n\n\n  \nWednesday\, June 4\, 2025 \n\n\n\n8:30–9:00 am\nMorning Reception\n\n\n9:00–10:00 am\nLara Bossinger: Positive Grassmannian and Cluster Algebras III\n\n\n10:00–10:30 am\nCoffee Break\n\n\n10:30–11:10 am\nExercises\n\n\n11:10 am–12:10 pm\nMarcus Spradlin: Scattering Amplitudes and Amplituhedra II\n\n\n12:10–2:00 pm\nLunch Break\n\n\n2:00–3:00 pm\nMarcus Spradlin: Scattering Amplitudes and Amplituhedra III\n\n\n3:00–3:30 pm\nCoffee Break\n\n\n3:30–4:30 pm\nSimon Telen: Positive geometry of polypols\n\n\n4:30–5:10 pm\nExercises\n\n\n\n  \n\nImage credit: Annabel Ma (Harvard College)
URL:https://cmsa.fas.harvard.edu/event/positivityqft/
LOCATION:CMSA 20 Garden Street Cambridge\, Massachusetts 02138 United States
CATEGORIES:Event,Workshop
ATTACH;FMTTYPE=image/jpeg:https://cmsa.fas.harvard.edu/media/SummerSchool_poster_11x17_v2-scaled.jpg
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/New_York:20250514T120000
DTEND;TZID=America/New_York:20250514T130000
DTSTAMP:20260711T064210
CREATED:20250501T182905Z
LAST-MODIFIED:20250502T173004Z
UID:10003746-1747224000-1747227600@cmsa.fas.harvard.edu
SUMMARY:Report on the Perimeter Institute Theory+AI Workshop
DESCRIPTION:Conference Reports  \nSpeaker: Hugo Cui\, Harvard CMSA \nTitle: Report on the Perimeter Institute Theory+AI Workshop \nAbstract: I will give a survey and brief summary of some of the talks given at the Theory+AI Workshop: Theoretical Physics for AI event organized by Perimeter Institute in April\, on approaches to machine learning theory inspired from physics. \nLink : https://events.perimeterinstitute.ca/event/993/
URL:https://cmsa.fas.harvard.edu/event/confrep_51425/
LOCATION:CMSA 20 Garden Street Cambridge\, Massachusetts 02138 United States
CATEGORIES:Conference Reports
ATTACH;FMTTYPE=image/png:https://cmsa.fas.harvard.edu/media/CMSA-Conference-Reports-5.14.2025.png
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/New_York:20250324T090000
DTEND;TZID=America/New_York:20250524T170000
DTSTAMP:20260711T064210
CREATED:20240228T180801Z
LAST-MODIFIED:20250514T204248Z
UID:10002883-1742806800-1748106000@cmsa.fas.harvard.edu
SUMMARY:Program on Classical\, quantum\, and probabilistic integrable systems - novel interactions and applications
DESCRIPTION:Program on Classical\, quantum\, and probabilistic integrable systems – novel interactions and applications \nDates: March 24–May 24\, 2025  \nLocation: CMSA\, 20 Garden Street\, Cambridge MA 02138 \nExactly solvable models have played pivotal roles in mathematics and physics throughout their history. The program is dedicated to exploring and developing a more recent wave of their influence in stochastic models together with accompanying combinatorial\, classical\, and quantum integrable systems. Topics will include: \n\nColored and uncolored interacting particle systems with associated vertex models and line ensembles\nYang-Baxter integrability and its applications in algebraic combinatorics\, quantum systems\, and conformal field theory\nQuantum stochastic models\, quantum exclusion processes\, and free probability\nEmerging new aspects of classical and quantum integrable systems – hydrodynamics\, large deviations of stochastic models\, and random surface models\n\nOrganizers: \n\nAmol Aggarwal\, Columbia University & Clay Mathematics Institute\nGuillaume Barraquand\, École normale supérieure\, Paris\nAlexei Borodin\, MIT\nIvan Corwin\, Columbia University\nPierre Le Doussal\, École normale supérieure\, Paris\nMichael Wheeler\, University of Melbourne\n\nParticipants \n\nDenis Bernard\, Ecole Normale Supérieure Paris\nAlexey Bufetov\, University of Leipzig\nPasquale Calabrese\, SISSA Trieste\nSylvie Corteel\, UC Berkeley\nCesar Cuenca\, Ohio State University\nJan De Gier\, University of Melbourne\nAndrea De Luca\, CNRS\, Cergy Paris University\nBenjamin Doyon\, King’s College London\nPatrik Ferrari\, University of Bonn\nVadim Gorin\, UC Berkeley\nTamara Grava\, SISSA\nJimmy He\, Ohio State University\nJiaoyang Huang\, University of Pennsylvania\nKurt Johansson\, KTH Stockholm\nRichard Kenyon\, Yale\nAlexandre Krajenbrink\, Cambridge Quantum Computing & Quantinuum\nAtsuo Kuniba\, University of Tokyo\nMatteo Mucciconi\, National University of Singapore\nGreta Panova\, University of Southern California\nLeonid Petrov\, University of Virginia\nSylvain Prolhac\, Université Paul Sabatier\, Toulouse\nTomaž Prosen\, University of Ljubljana\nTomohiro Sasamoto\, Tokyo Institute of Technology\nHerbert Spohn\, Technical University of Munich\nLi-Cheng Tsai\, University of Utah\n\nSchedule \nWeek 1\nMonday\, March 24th \n11:00am – 12:00pm Room G-10\, Lecture 1 of 4: Denis Bernard\, École normale supérieure de Paris: Quantum Exclusion Processes for (and by) Amateurs \n12:00 – 2:00pm Common Room: Program Lunch \n4:00 – 4:30pm Common Room: CMSA colloquium tea \n4:30 – 5:30pm Common Room\, CMSA colloquium: Amol Aggarwal\, Columbia University: The Toda Lattice as a Soliton Gas \n  \nTuesday\, March 25th \n3:30 – 4:00pm Common Room: Program tea \n4:00 – 5:00pm Room G-10\, Seminar: Patrik Ferrari\, Universität Bonn: Decoupling and decay of two-point functions in a two-species TASEP \n  \nWednesday\, March 26th \n11:00am – 12:00pm Room G-10\, Lecture 1 of 3: Atsuo Kuniba\, University of Tokyo: Multispecies ASEP and t-PushTASEP on a ring and a strange five vertex model \n3:00 – 4:00pm Room G-10\, Lecture 2 of 4: Denis Bernard\, École normale supérieure de Paris: Quantum Exclusion Processes for (and by) Amateurs \n4:30 – 5:30pm Common Room: Program wine and cheese reception \n  \nThursday\, March 27th \n11:00am – 12:00pm Room G-10\, Lecture 1 of 2: Benjamin Doyon\, King’s College London: The equations of generalised hydrodynamics\, and their unusual diffusve corrections \nAbstract: I will discuss the hydrodynamics of one-dimensional many-body integrable models. At the Euler scale\, this is given by “generalised hydrodynamics”\, whose equations only depend on the asymptotic state content and the two-body scattering shift of the model. I will explain how these equations arise\, and mention some of their properties: Hamiltonian structure\, exact solutions\, absence of shocks. At the diffusive scale\, generic one-dimensional models with state-dependent currents display super-diffusion. However\, integrable models are in a special class of “linearly degenerate systems”\, where there is no superdiffusion\, and one might expect a standard derivative expansion. I will explain how the diffusive corrections to the Euler equations are not given by a derivative expansion\, but instead governed by long-range correlations coming from an Euler-scale fluctuation theory. I will give the general ideas behind this fluctuation theory\, where initial fluctuations are deterministically transported by the Euler equation. I will finally provide arguments for the conjecture that\, once long-range correlations are accounted for\, there is no emergent stochasticity at all scales of hydrodynamics in integrable systems. \n3:30 – 4:00pm Common Room: Program tea \n4:00 – 5:00pm Room G-10\, Seminar: Sylvie Corteel\, University of California at Berkeley: Crystal Skeletons \n  \nFriday\, March 28th \n12:00 – 1:00 pm Common Room: Lunch with CMSA Member Seminar \n2:00 – 3:00pm Room G-10\, Lecture 3 of 4 : Denis Bernard\, École normale supérieure de Paris: Quantum Exclusion Processes for (and by) Amateurs \n3:30 – 4:00 pm Common Room: Program tea \n  \n\n \nWeek 2\nMonday\, March 31 \n11:00am – 12:00pm Room G-10\, Lecture 2 of 2: Benjamin Doyon\, King’s College London: The equations of generalised hydrodynamics\, and their unusual diffusve corrections \nAbstract: I will discuss the hydrodynamics of one-dimensional many-body integrable models. At the Euler scale\, this is given by “generalised hydrodynamics”\, whose equations only depend on the asymptotic state content and the two-body scattering shift of the model. I will explain how these equations arise\, and mention some of their properties: Hamiltonian structure\, exact solutions\, absence of shocks. At the diffusive scale\, generic one-dimensional models with state-dependent currents display super-diffusion. However\, integrable models are in a special class of “linearly degenerate systems”\, where there is no superdiffusion\, and one might expect a standard derivative expansion. I will explain how the diffusive corrections to the Euler equations are not given by a derivative expansion\, but instead governed by long-range correlations coming from an Euler-scale fluctuation theory. I will give the general ideas behind this fluctuation theory\, where initial fluctuations are deterministically transported by the Euler equation. I will finally provide arguments for the conjecture that\, once long-range correlations are accounted for\, there is no emergent stochasticity at all scales of hydrodynamics in integrable systems. \n12:00 – 2:00pm Common Room: Program Lunch \n2:00 – 3:00pm Room G-10\, Lecture 2 of 3: Atsuo Kuniba\, University of Tokyo: Solutions of tetrahedron and 3D reflection equations from quantum cluster algebras \n\nAbstract: Tetrahedron and 3D equations are three-dimensional generalizations of the Yang-Baxter and the reflection equations. I will explain how quantum cluster algebras lead to solutions that generalize and unify many known solutions.  \n\n3:30 – 4:00pm Program tea \n  \nTuesday\, April 1 \n11:00am – 12:00pm Room G-10\, Lecture 1 of 2: Kurt Johansson\, KTH Stockholm: Extremal particles in uniform random Gelfand-Tsetlin patterns \nAbstract: I will report on joint work with Elnur Emrah on edge fluctuations in uniform random interlacing patterns with fixed top configuration. The goal is to describe all possible limit processes that can occur\, and the conditions under which they occur. \n3:30pm – 4:00pm\, Common Room: Program tea \n  \nWednesday\, April 2 \n11:00am – 12:00pm Room G-10\, Lecture 4 of 4: Denis Bernard\, École normale supérieure de Paris: Quantum Exclusion Processes for (and by) Amateurs \n3:00 – 4:00pm Room G-10\, Lecture 3 of 3: Atsuo Kuniba\, University of Tokyo: Box-ball systems \nAbstract: Box-ball systems are one-dimensional integrable cellular automata introduced in 1990. This talk surveys major developments that have unfolded consistently over the decades\, enriching the scope of the theory. Topics include ultradiscretization\, crystal theory in quantum groups\, the combinatorial and thermodynamic Bethe ansatz\, as well as generalized hydrodynamics. \n4:30 – 5:30pm Common Room: Program wine and cheese reception \n  \nThursday\, April 3 \n11:00am – 12:00pm Room G-10\, Lecture 2 of 2: Kurt Johansson\, KTH Stockholm: Extremal particles in uniform random Gelfand-Tsetlin patterns \nAbstract: I will report on joint work with Elnur Emrah on edge fluctuations in uniform random interlacing patterns with fixed top configuration. The goal is to describe all possible limit processes that can occur\, and the conditions under which they occur. \n3:30pm – 4:00pm Common Room: Program tea \n  \nFriday\, April 4 \n12:00 – 1:00pm Common Room: CMSA Member Seminar and Lunch \n3:30 – 4:00pm Common Room: Program tea \n  \n\n \nWeek 3\nMonday\, April 7 \n12:00 – 2:00pm Common Room: Program lunch \n4:00 – 4:30pm Tea with CMSA colloquium \n4:30 – 5:30pm CMSA Colloquium: Ben Webster\, University of Waterloo and Perimeter Institute: 3-D Mirror Symmetry \n  \nTuesday\, April 8 \n11:00am – 2:00pm Room G-10\, Pierre Le Doussal\, École normale supérieure de Paris: Exact results for the macroscopic fluctuation theory of the 1D weakly asymmetric exclusion process. \n3:30 – 4:00pm Common Room: Program tea  \n  \nWednesday\, April 9 \n12:00 – 1:00pm Common Room\, CMSA Q&A Seminar and lunch: Eric Maskin\, Harvard Economics: The Mathematics of Voting \n4:30 – 5:30pm Common Room: Program wine and cheese reception \n  \nThursday\, April 10 \n3:30 – 4:00pm Common Room: Program tea  \n  \nFriday\, April 11 \n12:00 – 1:00pm Common Room: CMSA member seminar and lunch \n3:30 – 4:00pm Common Room: Program tea \n  \n\nWeek 4\nMonday\, April 14 \n12:00 – 2:00pm Common Room: Program lunch \n4:00 – 4:30pm Tea with CMSA colloquium \n4:30 –5:30pm CMSA colloquium: Andrey Smirnov\, University of North Carolina at Chapel Hill: Quantum K-theory at roots of unity \n  \nTuesday\, April 15 \n11:00 am – 12:00pm Room G-10\, Ivan Corwin\, Columbia University: How Yang-Baxter unravels Kardar-Parisi-Zhang \nAbstract: Over the past few decades\, physicists and then mathematicians have sought to uncover the (conjecturally) universal long time and large space scaling limit for the so-called Kardar-Parisi-Zhang (KPZ) class of stochastically growing interfaces in (1+1)-dimensions. Progress has been marked by several breakthroughs\, starting with the identification of a few free-fermionic integrable models in this class and their single-point limiting distributions\, widening the field to include non-free-fermionic integrable representatives\, evaluating their asymptotics distributions at various levels of generality\, constructing the conjectural full space-time scaling limit\, known as the directed landscape\, and checking convergence to it for a few of the free-fermion representatives. \nIn this talk\, I will describe a method that should prove convergence for all known integrable representatives of the KPZ class to this universal scaling limit. The method has been fully realized for the Asymmetric Simple Exclusion Process and the Stochastic Six Vertex Model. It relies on the Yang-Baxter equation as its only input and unravels the rich complexity of the KPZ class and its asymptotics from first principles. This is based on a few works involving Amol Aggarwal\, Alexei Borodin\, Milind Hegde\, Jiaoyang Huang and me. \n3:30 – 4:00pm Common Room: Program tea  \n  \nWednesday\, April 16 \n11:00am – 12:00pm Room G-10\, Tamara Grava\, University of Bristol: Random solitons and soliton gas \nAbstract: A soliton is a localised travelling wave solution of a nonlinear dispersive equation. When the equation is integrable the interaction of many solitons is elastic. We study the behaviour of a set of N solitons for the Korteweg de Vries equation in the limit N goes to infinity (soliton gas) and the interaction of the soliton gas with a distinct soliton that we call a tracer soliton. We show that the average velocity of the tracer soliton satisfies the Zakharov-El kinetic equations. We then consider a set of random N soliton solution q_N(x\,t) and its limiting soliton gas q(x\,t). We prove a central limit theorem for the difference q_N(x\,t)-q(x\,t) for values of x and t that are bounded by log(N). \n12:00 – 1:00pm Common Room: CMSA Q&A seminar and lunch: Noah Golowich\, MIT: What is length generalization in large language models? \n4:30 – 5:30pm Common Room: Program wine and cheese reception \n  \nThursday\, April 17 \n11:00am – 12:00pm Room G-10\, Guillaume Barraquand\, École normale supérieure de Paris: Large time cumulants of the open KPZ equation \n12:00 – 1:00pm Common Room: lunch with featured Yip Lecture speaker Scott Aaronson and CMSA residents \n3:30pm Common Room: Program tea  \n4:00 – 5:00pm Science Center Hall A: Fifth Annual Yip Lecture\, Scott Aaronson: How Much Math is Knowable? \n5:00 – 6:00pm Math Department Common Room at the Harvard Science Center: Yip Lecture reception \n  \nFriday\, April 18 \n12:00 – 1:00pm Common Room: CMSA Member Seminar and lunch: Han Shao\, Harvard CMSA\, Topic TBD \n3:30 – 4:00pm Common Room: Program tea \n  \n\nWeek 5\n  \nMonday\, April 21 \n11:00am – 12:00pm Room G-10\, Tomaz Prosen\, University of Ljubljana\, Lecture 1 of 3: On Integrable Quantum and Classical Circuits (with Stochastic Boundaries) \nAbstract: I will introduce Yang-Baxter integrable brickwork quantum circuit models and discuss their integrability structure\, specifically\, the transfer matrix\, conservation laws etc. A paradigmatic example\, XXZ or unitary 6-vertex circuits exhibit an unusual link to KPZ scaling at the isotropic (SU(2) symmetric) point. I will establish the link to corresponding classical integrable Landau-Lifshitz circuits and discuss some aspects of transport and full counting statistics. \n12:00 – 2:00pm Common Room: Program Lunch \n4:00 – 4:30pm Common Room: CMSA colloquium tea \n4:30 – 5:30pm  Common Room\, CMSA colloquium: Ila Fiete\, MIT: Modeling the emergence of complex cortical structure from simple precursors in the brain: maps\, hierarchies\, and modules \n  \nTuesday\, April 22 \n11:00am – 12:00pm Room G-10\, Tomohiro Sasamoto\, Tokyo Institute of Technology: Large deviation of symmetric models through classical integrable systems \n3:30pm Common Room: Program tea  \n  \nWednesday\, April 23 \n11:00am – 12:00pm Room G-10\, Tomaz Prosen\, University of Ljubljana: On Integrable Quantum and Classical Circuits (with Stochastic Boundaries) \nAbstract: I will discuss explicit matrix product solutions for quantum many-body Markov chains\, defined for a Yang-Baxter integrable quantum circuit with specific stochastic Kraus processes at its boundaries. In the continuous time limit\, these solutions correspond to steady states of boundary driven Lindbladian dynamics and often yield non-trivial quasi-local conservation laws of integrable spin chains. The specific case of XXZ and Hubbard chain will be discussed. \n12:00 – 1:00pm Common Room: CMSA Q&A seminar and lunch: Alexei Borodin\, MIT: Connections between physics and probability \n4:30 – 5:30pm Common Room: Program wine and cheese reception \n  \nThursday\, April 24 \n11:00am – 12:00pm Room G-10\, Sylvain Prolhac\, Université Paul Sabatier\, Toulouse: Approach to stationarity for KPZ fluctuations in finite volume \nAbstract: At late times $t$\, the cumulants of the height for the KPZ fixed point in finite volume behave as affine functions of time $c_k(t) = a_k t+b_k$\, up to exponentially small corrections. The constant term $b_k$ is the last remaining information about the initial state observable at long enough times. Two approaches allow us to compute this constant from the totally asymmetric exclusion process\, a discrete version of the KPZ fixed point. First\, an iterated version of the matrix product representation for the stationary state leads\, for arbitrary initial conditions\, to expressions involving extreme value statistics of Brownian paths. On the other hand\, Bethe ansatz leads to rather explicit expressions for simple initial conditions. Comparison between the two approaches then provides conjectures for some generating functions of Brownian paths. \n3:30pm Common Room: Program tea  \n  \nFriday\, April 25 \n11:00am – 12:00pm Room G-10\, Tomaz Prosen\, University of Ljubljana\, Lecture 3 of 3: On Integrable Quantum and Classical Circuits (with Stochastic Boundaries) \nAbstract: In the last lecture I will discuss the possibility of quantum integrability of many-body quantum Markov chain generators\, such as Lindbladians with bulk or boundary dissipation\, and the corresponding circuit (Kraus) counterparts. The paradigmatic example is the XX model with dephasing noise which can be mapped to a Hubbard model with imaginary interaction\, both in the Hamiltonian and circuit formulation. \n3:30 – 4:00pm Common Room: Program tea \n  \n\nWeek 6\n  \nMonday\, April 28 \n11:00am – 12:00pm Room G-10\, Herbert Spohn\, Technische Universitaet Muenchen\, Lecture 1 of 3: Integral many-body systems and GHD \n12:00 – 2:00pm Common Room: Program Lunch \n2:00 – 3:00 pm Room G-10\, Tomohiro Sasamoto\, Tokyo Institute of Technology\, Exact density profile and current fluctuations in a tight-binding chain with dephasing noise \nAbstract: We consider a tight-binding chain with dephasing noise\, whose time evolution is described by the quantum master equation called the Gorini-Kossakowski-Sudarhan-Lindblad (GKSL) equation. By using a connection of this model to the Hubbard model with imaginary coupling [1]\, we study the density profile [2] and the variance of the current [3] exactly for the model on the infinite line by writing down contour integral formulas using Bethe ansatz. The talk is based on collaborations with Taiki Ishiyama and Kazuya Fujimoto.  \n4:00 – 4:30pm Common Room: CMSA colloquium tea \n4:30 –5:30pm Room G-10\, CMSA colloquium: Peter Sarnak\, IAS and Princeton University\, Bass-Note Spectra of locally uniform geometries \n  \nTuesday\, April 29 \n11:00 am – 12:00pm Room G-10\, Pasquale Calabrese\, SISSA Trieste\, Lecture 1 of 3: Quantum Mpemba effect \n2:00 – 3:00 pm Room G-10\, Greta Panova\, University of Southern California\, Grothendieck shenanigans: permutons from pipe dreams via integrable probability \nAbstract: Pipe dreams are tiling models originally introduced to study objects related to the Schubert calculus and K-theory of the Grassmannian. They can also be viewed as ensembles of random lattice walks with various interaction constraints. In our quest to understand what the maximal and typical algebraic objects look like\, we revealed some interesting permutons. The proofs use the theory of the Totally Asymmetric Simple Exclusion Process (TASEP). Deeper connections with domino tilings of the Aztec diamond and its frozen boundary allow us to describe the extreme cases of the original algebraic problem. This is based on joint work with A. H. Morales\, L. Petrov\, D. Yeliussizov. \n3:30 – 4:00pm Common Room: Program tea  \n  \nWednesday\, April 30 \n11:00am – 12:00pm Herbert Spohn\, Technische Universitaet Muenchen\, Lecture 2 of 3: Integral many-body systems and GHD \n12:00 – 1:00pm (tentative) Common Room: CMSA Q&A seminar and lunch \n3:00 – 4pm Room G-10\, Pasquale Calabrese\, SISSA Trieste\, Entanglement evolution and quasiparticle picture 1 \n4:30 – 5:30pm Common Room: Program wine and cheese reception \n  \nThursday\, May 1 \n11:00am – 12:00pm Room G-10\, Herbert Spohn\, Technische Universitaet Muenchen\, Lecture 3 of 3: Integral many-body systems and GHD \n2:00 – 3:00 pm Room G-10\, Li-Cheng Tsai\, University of Utah\, Stochastic heat flow by moments \nAbstract: The Stochastic Heat Flow (SHF) is the scaling limit of the directed polymers in random environments and the noise-mollified Stochastic Heat Equation (SHE)\, at the critical dimension of two and near the critical temperature. The finite-dimensional distributions of the SHF was obtained by Caravenna\, Sun\, and Zygouras (2023) by proving that the discrete polymers converge to a universal (model-independent) limit. In this talk\, I will report a new approach based on axioms. We formulate the SHF as a two-parameter continuous measure-valued process that satisfies a set of axioms\, and prove the uniqueness in law under these axioms. The key feature of the axioms concerns the matching of the first four moments. As an application\, we prove the convergence of the noise-mollified SHE to the SHF\, which only requires moment estimates. \n3:30pm Common Room: Program tea  \n  \nFriday\, May 2 \n11:00am – 12:00pm Room G-10\, Pasquale Calabrese\, SISSA Trieste\, Lecture 3 of 3: Entanglement evolution and quasiparticle picture 2 \n12:00 – 1:00pm Common Room\, CMSA Member seminar and lunch \n2:00 – 3:00 pm Room G-10\, Leonid Petrov\, University of Virginia: Random Fibonacci Words \nAbstract: Fibonacci words are words of 1’s and 2’s\, graded by the total sum of the digits. They form a differential poset YF which is an estranged cousin of the Young lattice powering irreducible representations of the symmetric group. We introduce families of “coherent” measures on YF depending on many parameters\, which come from the theory of clone Schur functions (Okada 1994). We characterize parameter sequences ensuring positivity of the measures\, and we describe the large-scale behavior of some ensembles of random Fibonacci words. The subject has connections to total positivity of tridiagonal matrices\, Stieltjes moment sequences\, orthogonal polynomials from the (q-)Askey scheme\, and residual allocation (stick-breaking) models. Based on a joint work with Jeanne Scott. \n3:30 – 4:00pm Common Room: Program tea \n\nWeek 7\n  \nMonday\, May 5 \n11:00am – 12:00pm Room G-10\, Jan De Gier\, University of Melbourne\, Lecture 1 of 3: Pfaffian point process for TASEP on the half line \n12:00 – 2:00pm Common Room: Program Lunch \n2:00 – 3:00 pm  Jiaoyang Huang\, University of Pennsylvania: Ramanujan Property and Edge Universality of Random Regular Graphs \nAbstract: Extremal eigenvalues of graphs are of particular interest in theoretical computer science and combinatorics. Specifically\, the spectral gap—the difference between the largest and second-largest eigenvalues—measures the expansion properties of a graph. In this talk\, I will focus on random d-regular graphs.I will begin by providing background on the eigenvalues of random d-regular graphs and their connections to random matrix theory. In the second part of the talk\, I will discuss our recent results on eigenvalue rigidity and edge universality for these graphs. Eigenvalue rigidity asserts that\, with high probability\, each eigenvalue concentrates around its classical location as predicted by the Kesten-McKay distribution. Edge universality states that the second-largest eigenvalue and the smallest eigenvalue of random d-regular graphs converge to the Tracy-Widom distribution from the Gaussian Orthogonal Ensemble. Consequently\, approximately 69% of d-regular graphs are Ramanujan graphs. This work is based on joint work with Theo McKenzie and Horng-Tzer Yau. \n  \n4:00 – 4:30pm Common Room: CMSA colloquium tea \n4:30 –5:30pm Common Room\, CMSA colloquium: Ariel Procaccia\, Harvard University\, Thinking Outside the Ballot Box \n  \nTuesday\, May 6 \n11:00 am – 12:00pm Room G-10\, Jan De Gier\, University of Melbourne\, Lecture 2 of 3: Pfaffian point process for TASEP on the half line \n2:00 – 3:00 Richard Kenyon\, Yale University\, Multinomial dimers and 3d limit shapes \nAbstract: The “multinomial dimer model” on a graph G is the dimer model on the N-fold blow up of G (the graph obtained by replacing each vertex with N vertices and each edge with a complete bipartite graph K_{N\,N}). In the large N limit this model is tractable for general graphs: we find formulas for the partition function and limit shapes in some natural settings\, including a three-dimensional version of the Aztec Diamond. This is joint work with Catherine Wolfram (Yale). \n3:30 – 4:00pm Common Room: Program tea  \n  \nWednesday\, May 7 \n3:00 – 4pm Room G-10\, Jan De Gier\, University of Melbourne\, Lecture 3 of 3: Pfaffian point process for TASEP on the half line \n4:30 – 5:30pm Common Room: Program wine and cheese reception \n  \nThursday\, May 8: \n2:00 – 3:00 pm Room G-10\, Andrea De Luca\, CNRS Cergy Paris University\, Monitored quantum systems\, product of random matrices and permutations \n3:30pm Common Room: Program tea  \n  \nFriday\, May 9: \n12:00 – 1:00pm Common Room: CMSA Member Seminar and lunch\, Sergiy Verstyuk\, Harvard CMSA\, Title TBD \n2:00 – 3:00 pm Room G-10\, Cesar Cuenca\, Ohio State University\, Random partitions at high temperature \nAbstract: By using Fourier transforms based on Jack symmetric polynomials\, we study discrete particle ensembles x_1>x_2>…>x_N with the inverse temperature beta in the regime where beta tends to zero\, as the number of particles tends to infinity. We prove the LLN and characterize the limiting measure in terms of a moment problem. For fixed-time distributions of special Markov chains\, the limiting measures can be expressed in terms of the eigenvalues of certain Jacobi operators. \n3:30 – 4:00pm Common Room: Program tea \n\nWeek 8\n  \nMonday\, May 12 \n11:00am – 12:00pm Room G-10\, Jimmy He\, Ohio State University\, Symmetries of periodic and free boundary measures on partitions \nAbstract: The periodic and free boundary q-Whittaker measures are probability measures on partitions defined in terms of q-Whittaker functions and an additional parameter $u$ controlling the behavior of the system at the boundary. I will explain a hidden distributional symmetry of this model which exchanges the $u$ and $q$ parameters\, as well as related results on Hall-Littlewood measures. As a special case\, we recover identities of Imamura–Mucciconi–Sasamoto. This is joint work with Michael Wheeler. \n12:00 – 2:00pm Common Room: Program Lunch \n4:00 – 4:30pm Common Room: CMSA colloquium tea \n4:30 – 5:30pm Common Room\, CMSA colloquium: Anna Seigal\, Harvard University\, Factorizations for data analysis \n  \nTuesday\, May 13 \n3:30pm Common Room: Program tea  \n  \nWednesday\, May 14 \n12:00 – 1:00pm Common Room: CMSA Conference Reports seminar and lunch: Hugo Cui\, Harvard CMSA\, reporting on the Perimeter Institute Theory+AI Workshop \n3:00 – 4:00pm Room G-10\, Alexandre Krajenbrink\, Cambridge Quantum Computing and Quantinuum\, Unveiling the classical integrable structure of the weak noise theory of the KPZ class: example of the matrix Log–Gamma polymer and the q-TASEP \n4:30 – 5:30pm Common Room: Program wine and cheese reception \n  \nThursday\, May 15 \n11:00am – 12:00pm Room G-10: Roger Van Peski\, Columbia University\, Integrability in discrete random matrix theory \n\nAbstract: Integrable structure has been well-used in classical random matrix theory\, and recently is also enjoying application in the parallel world of discrete random matrices (over integers\, p-adic integers\, and finite fields). In this talk I will try to cover—at least briefly—the following:\n\n\nSome favorite probabilistic results (convergence of discrete random matrix local limits to a new integrable interacting particle system\, the ‘reflecting Poisson sea’)\,\nSome exact formulas with Hall-Littlewood polynomials that make these results possible\, and \nSome intriguing newer formulas (joint with Jiahe Shen) for Hermitian and antisymmetric p-adic matrices\, which naturally feature ‘formal’ Hall-Littlewood processes with negative t parameter.\n\n\n\n2:00 – 3:00 pm Room G-10\, Matteo Mucciconi\, National University Singapore\, Orthogonality of spin q-Whittaker polynomials \nAbstract: Spin q-Whittaker polynomials are a family of symmetric polynomials that can be defined as partition functions of a solvable lattice model. Their study reveals that they possess mysterious properties such as additional “unorthodox” symmetries\, eigenrelations with respect to difference operators and a self orthogonality that I will prove in the talk. A particular case of these results include a novel orthogonality for the Grothendieck polynomials from K-theory of Grassmannian. I will also discuss applications to exact solutions of directed random polymer models with Beta weights. \n3:30pm Common Room: Program tea  \n  \nFriday\, May 16 \n12:00 – 1:00pm Common Room: CMSA Member Seminar  and lunch: Samy Jelassi\, Echo Chamber: RL Post-training Amplifies Behaviors Learned in Pretraining \n3:30 – 4:00pm Common Room: Program tea \n\nVideos are available on the Youtube Playlist. \n\n 
URL:https://cmsa.fas.harvard.edu/event/integrablesystems2025/
LOCATION:CMSA 20 Garden Street Cambridge\, Massachusetts 02138 United States
CATEGORIES:Event,Programs
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BEGIN:VEVENT
DTSTART;TZID=America/New_York:20250130T160000
DTEND;TZID=America/New_York:20250130T173000
DTSTAMP:20260711T064210
CREATED:20240710T194728Z
LAST-MODIFIED:20241218T212836Z
UID:10003399-1738252800-1738258200@cmsa.fas.harvard.edu
SUMMARY:CMSA/MATH Welcome Back Gathering
DESCRIPTION:Thursday\, Jan. 30\, 2025 \n4:00 pm \nAll CMSA and Math affiliates are invited. \n 
URL:https://cmsa.fas.harvard.edu/event/cmsa-math_13025/
LOCATION:CMSA 20 Garden Street Cambridge\, Massachusetts 02138 United States
CATEGORIES:Event
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/New_York:20250121T090000
DTEND;TZID=America/New_York:20250124T170000
DTSTAMP:20260711T064210
CREATED:20240710T140404Z
LAST-MODIFIED:20250213T211311Z
UID:10003397-1737450000-1737738000@cmsa.fas.harvard.edu
SUMMARY:Workshop on Symmetries and Gravity
DESCRIPTION:Workshop on Symmetries and Gravity \nDates: January 21-24\, 2025 \nLocation: Harvard CMSA\, 20 Garden Street\, Cambridge\, MA 02138 \nOrganizers: Ibrahima Bah (Johns Hopkins University)\, Patrick Jefferson (Johns Hopkins University)\, Yiming Chen (Stanford University) \nDescription: There is a widespread belief\, that has its origins in work from the 70s\, that a theory of quantum gravity cannot admit global symmetries. Traditionally\, this was seen only as a qualitative statement about ordinary symmetries\, but there have since been a number of developments that have both widened its scope and sharpened its implications. Recent work has greatly broadened the definition of global symmetries\, and characterizes them in terms of topological operators in quantum systems. Concurrently\, insights from quantum gravity have suggested ways to quantify the extent of global symmetry violation. Additionally\, advances in the swampland program\, along with amplitudes and bootstrap techniques\, have shown ways to turn high-energy statements into constraints on low-energy effective field theories. In string theory\, there are more concrete statements on charge violation in gravity\, with proofs in limited context. In general\, however\, “no global symmetries in quantum gravity” continues to be an open conjecture with broad implications on the nature of quantum gravity and low-energy effective field theory. The main goal of the meeting is to bring together experts in the various arenas of research above\, to reassess and develop new strategies for making progress on this long-standing open problem. Some objectives include understanding the violation of various generalized and categorical symmetries in gravity more cohesively\, and putting concrete bounds on global charge-violating amplitudes at low energies. \nPartially funded by the Simons Collaboration on Global Categorical Symmetries. \n  \nConfirmed Participants \n\nTom Banks\, Rutgers\nFederico Bonetti\, Durham University\nChristian Copetti\, Oxford\nHector Parra De Freitas\, Harvard\nDamian van de Heisteeg\, Harvard CMSA\nMatilda Delgado\, IFT\nMichele Del-Zotto\, Uppsala University\nMuldrow Etheredge\, UMass Amherst\nIñaki Garcia-Etxebarria\, Durham University\nEduardo Garcia-Valdecasas\, SISSA\, Trieste\nNaomi Gendler\, Harvard\nKelian Haring\, CERN\nDaniel Harlow\, MIT\nJonathan Heckman\, University of Pennsylvania\nBen Heidenreich\, UMass Amherst\nAidan Herderschee\, IAS\nMax Huebner\, Uppsala University\nJesús Huertas\, Instituto de Física Teórica\nTheo Johnson-Freyd\, Dalhousie University\nHo Tat Lam\, MIT\nAdam Levine\, MIT\nYue-Zhou Li\, Princeton\nJacob McNamara\, Caltech\nRuben Minasian\, Institute of Theoretical Physics Saclay\nAmineh Mohseni\, Harvard\nMiguel Montero\, IFT\nGregory Moore\, Rutgers\nLeonardo Rastelli\, Stony Brook\nMatt Reece\, Harvard University\nGrant Remmen\, New York University\nDiego Rodriguez-Gomez\, University of Oviedo\nKonstantinos Roumpedakis\, Johns Hopkins\nTom Rudelius\, Durham University\nVivek Saxena\, Stony Brook and Rutgers\nEdgar Shaghoulian\, UC Santa Cruz\nShu-Heng Shao\, Stony Brook and MIT\nAdar Sharon\, Simons Center for Geometry and Physics\, Stony Brook\nIrene Valenzuela\, IFT and CERN\nThomas Waddleton\, Johns Hopkins\nHao Xu\, University of Göttingen\nXingyang Yu\, Virginia Tech\n\n  \nSchedule  \nTuesday\, Jan. 21\, 2025 \n9:00 – 9:30 am\nBreakfast \n9:30 – 11:00 am\nReview\nLeonardo Rastelli\, Stony Brook University\nYoutube Video \n11:00 – 11:15 am\nBreak \n11:15 am– 12:00 pm\nKelian Haring\, CERN\nTitle: S-matrix bootstrap and black hole production\nAbstract: I will review the expected effects of black hole production in scattering amplitudes. I will consider both symmetry-breaking and elastic amplitudes. I will argue that\, in the elastic case\, this input can be computationally useful. Then\, I will discuss an example of a symmetry-breaking Wilson coefficient as a concrete target for the bootstrap.\nYoutube Video \n12:00 – 1:45 pm\nLunch Break \n1:45 – 2:30 pm\nHo Tat Lam\, MIT\nTitle: Global Aspects of Exactly Marginal Current-Current Deformations\nAbstract: Conformal field theories connected by exactly marginal deformations form conformal manifolds. In two dimensions\, a large class of conformal manifolds is generated by bilinears of currents\, known as current-current deformations. In this talk\, we will revisit these deformations and prove that a dense set of points on the conformal manifolds are related to the seed theory through discrete gauging. This perspective enables us to connect the topology of the conformal manifolds with the anomalies of the currents and to show that enhanced invertible symmetries reorganized into non-invertible symmetries away from the symmetry enhanced points. We will also discuss how current-current deformation can be understood from the recently proposed continuous abelian symmetry topological field theory.\nYoutube Video \n2:30 – 3:15 pm\nTom Banks\, Rutgers University\nTitle: Symmetries in the Hilbert Bundle Formulation of Quantum Gravity\nAbstract: Results of Jacobson\, Carlip and Solodukhin from the 1990s\, as extended by Banks and Zurek in 2021\, point to a solution of Einstein’s equations as a hydrodynamic approximation to a quantum gravitational system\, which determines the density matrix assigned to each subsystem corresponding to a hydrodynamic causal diamond in terms of the Virasoro generator of a cut off 1 + 1 dimensional CFT. The full quantum system can be viewed as a Hilbert bundle over the space of timelike geodesics on the hydrodynamic background. Isometries of the background generically map one fiber of the bundle to another and don’t act on a fixed Hilbert space. Time evolution along each geodesic is given by an analog of “one sided modular flow in QFT”\, which in this context is a sequence of unitary embeddings of smaller diamond Hilbert spaces into larger ones. A full unitary map on the entire Hilbert space of a fiber requires a “Quantum Principle of Relativity” equating the entanglement spectrum of the density matrix of the largest diamond in the overlap between diamonds on different geodesics. In principle\, this implies asymptotic symmetries for spacetimes which have them. For the case of asymptotically AdS space\, this can be worked out in a hand waving way by using the Tensor Network Renormalization Group of Evenbly and Vidal. For asymptotically flat space we probably require a better non-perturbative definition of the space of asymptotic states to understand the action of the Poincare group. For de Sitter space there is no sense in which the de Sitter group acts on any set of asymptotic observables. Ironically\, there IS an approximate de Sitter invariance of at least low point inflationary correlation functions\, but I will not have time to discuss that.\nYoutube Video \n3:15 – 3:45 pm\nBreak \n3:45 – 4:30 pm\nChristian Copetti\, Oxford University\nTitle: Non-Invertible Symmetries\, Generalized Gauging and Factorization\nAbstract: We analyze a toy model for low dimensional holography\, in which the dual theory is an ensemble over 2d RCFTs. This simple model lacks factorization on multi-boundary geometries and at the same time has a (generalized) bulk global symmetry. We show that both problems are solved if the path integral prescription is modified by a generalized gauging operation\, which can also be interpreted as the insertion of (topological) EOW branes.\nYoutube Video \n4:30 – 5:00 pm\nFree Discussion \nWednesday\, Jan. 22\, 2025 \n9:00 – 9:30 am\nBreakfast \n9:30 – 11:00 am\nReview\nDaniel Harlow\, MIT\nYoutube Video \n11:00 – 11:15 am\nBreak \n11:15 am– 12:00 pm\nJacob McNamara\, Caltech\nTitle: Conserved Charges of Closed Universes\nAbstract: In quantum gravity\, while our standard notions of symmetry operator become hard to define\, the notion of conserved charge continues to make sense. After a general discussion of conserved charges in quantum gravity\, I will present a new kinematic invariant of a gravitational path integral that refines the cobordism groups of quantum gravity: the (higher) category of closed universe charges. By categorifying an argument of Coleman\, Giddings\, and Strominger\, I will argue that conserved charges in quantum gravity of any form degree arise only due to a categorical version of ensemble holography.\nYoutube Video \n12:00 – 1:45 pm\nLunch Break \n1:45 – 2:30 pm\nFederico Bonetti\, Durham University\nTitle: Aspects of Categorical Symmetries for Branes\nYoutube Video \n2:30 – 3:15 pm\nKonstantinos Roumpedakis\, Johns Hopkins University\nTitle: Symmetry Operators and Gravity\nAbstract: It is widely believed that there are no conserved charges in a theory of gravity\, based on arguments involving black holes. Moreover\, the modern approach to study global symmetries is the language of topological operators. In this talk\, I will revisit the absence of global symmetries in a theory of gravity from the perspective of topological operators. More specifically\, I will argue that topological operators for continuous symmetries written in terms of currents need regularization\, which effectively gives them a small but finite width. The regulated operator is a finite tension object which fluctuates. In the zero-width limit these fluctuations freeze\, recovering the properties of a topological operator. When gravity is turned on\, the zero-width limit becomes ill-defined\, thereby prohibiting the existence of topological operators. This talk is based on work in collaboration with Ibrahima Bah\, Patrick Jefferson\, and Thomas Waddleton.\nYoutube Video \n3:15 – 3:45 pm\nBreak \n3:45 – 4:30 pm\nIñaki Garcia-Etxebarria\, Durham University\nTitle: Some aspects of symmetry descent\nAbstract: SymTFTs allow us to encode the symmetry structure of Quantum Field Theories in a convenient way. For those QFTs that arise in geometric engineering\, or holography\, we expect to be able to derive the SymTFT from the geometric data of the string background. This talk will describe some recent progress in this direction\, together with S. Hosseini and with F. Gagliano.\nYoutube Video \n4:30 – 5:00 pm\nFree Discussion \n6:00 pm\nDinner at Changsho Restaurant \nThursday\, Jan. 23\, 2025 \n9:00 – 9:30 am\nBreakfast \n9:30 – 11:00 am\nReview\nIrene Valenzuela\, IFT and CERN\nTitle: Breaking of Symmetries in Gravity\nAbstract: Global symmetries are expected to be broken (or gauged) in quantum gravity. However\, we can still learn a lot from understanding the mechanisms by which quantum gravity avoids them and quantifying their breaking. Remarkably\, several Swampland constraints can be reinterpreted as consequences of breaking global symmetries. I will first focus on quantifying the minimal symmetry violation of axionic shift symmetries\, and show how the bottom-up expectation based on black holes seems to hold in string theory examples. I will then discuss how this symmetry violation changes as we move in the moduli space\, implying a drop-off of the quantum gravity cut-off when the symmetry is approximate. Finally\, I will discuss the fate of non-invertible symmetries in string theory\, and how they are typically broken at loop level. Nevertheless\, these approximate non-invertible symmetries are still useful to fill in the gaps in the worldsheet proofs of some Swampland conjectures. \n11:00 – 11:15 am\nBreak \n11:15 am– 12:00 pm\nTom Rudelius\, Durham University\nTitle: A Symmetry-Centric Perspective on the Geometry of the Landscape and the Swampland\nAbstract: As famously observed by Ooguri and Vafa nearly twenty years ago\, scalar field moduli spaces in quantum gravity appear to exhibit various universal features. For instance\, they seem to be infinite in diameter\, have trivial fundamental group\, and feature towers of massive particles that become light in their asymptotic limits. In this talk\, I will explain how these features can be reformulated in more modern language using generalized notions of global symmetries. Such symmetries are ubiquitous in non-gravitational quantum field theories\, but it is widely believed that they must be either gauged or broken in quantum gravity. We will see that the observations of Ooguri and Vafa can be understood as consequences of such gauging or breaking. \n12:00 – 1:45 pm\nLunch Break \n1:45 – 2:30 pm\nMiguel Montero\, IFT\nTitle: Parity symmetry breaking and the membrane Weak Gravity Conjecture\nAbstract: Symmetries are expected to be broken or gauged in any consistent theory of quantum gravity\, and this also applies to spacetime symmetries such as parity. I will argue that\, in the context of 4d N=1 AdS vacua of string theory\, the Weak Gravity Conjecture for membranes case only holds if the vacuum has an exact (i.e. gauged) parity symmetry of Pin+ type. I will give top-down examples of M-theory vacua illustrating this\, and show that in the DGKT scenario (a putative massive IIA vacuum with scale separation\, whose full consistency is the subject of some debate in the literature) there is no parity symmetry\, and the membrane WGC is violated. Thus\, there is either a pathology in DGKT\, or the membrane WGC is wrong. Both possibilities would have interesting consequences\, and I will outline ongoing work to figure out which one is it. \n2:30 – 3:15 pm\nMatilda Delgado\, IFT\nTitle: Dualities\, Defects and Duality Defects\nAbstract: I will outline how duality symmetries in quantum gravity theories naturally predict the existence of defects associated with duality transformations. While some of these objects are well-understood and extensively studied\, others remain enigmatic; I will discuss this with examples. I will conclude by discussing the potential role of dualities in characterising the UV defects predicted by cobordism conjecture (and more generally by the no global symmetries conjecture). Based on: [2412.03640] \n3:15 – 3:45 pm\nBreak \n3:45 – 4:30 pm\nMax Hübner\, Uppsala University\nTitle: Metric Isometries\, Holography\, and Continuous Symmetry Operators\nAbstract: In the AdS/CFT correspondence\, a topological symmetry operator of the boundary CFT is dual to a dynamical brane in the gravitational AdS bulk. Said differently\, this predicts a dynamical brane for every global symmetry of the boundary CFT. We analyze this correspondence for continuous symmetries which arise from a consistent truncation of isometries on the “internal” factor X of AdS × X. We discuss how this perspective can be used to both derive properties of the topological symmetry operators and non-topological properties of their bulk duals. \n4:30 – 5:00 pm\nFree Discussion \n  \nFriday\, Jan. 24\, 2025 \n9:00 – 9:30 am\nBreakfast \n9:30 – 10:15 am\nJonathan Heckman\, University of Pennsylvania\nTitle: Cobordism Utopia\nAbstract: On general grounds one expects that global symmetries are absent in quantum gravity. We discuss some aspects of this issue\, focusing on the recently proposed Swampland Cobordism Conjecture\, and related conjectures connected with completeness of the spectrum of states charged under symmetries. In particular\, the U-dualities of M-theory provide an excellent arena both for testing aspects of these conjectures\, as well as predicting the existence of new dynamical objects. We also comment on how this approach connects to related top down and holographic approaches to constructing and studying gauging and breaking symmetries in quantum gravity. Based on joint work to appear with Braeger\, Debray\, Dierigl\, and Montero. \n10:15 – 11:00 am\nNaomi Gendler\, Harvard University \n11:00 – 11:15 am\nBreak \n11:15 am – 12:00 pm\nDiego Rodriguez-Gomez\, University of Oviedo\nTitle: Non-BPS branes as holographic symmetry operators\nAbstract: We propose a holographic description of the operators implementing continuous global symmetries that are dual to superstring gauge fields in terms of non-BPS D- branes\, and consider some possible further extensions. \n12:00 – 12:45 pm\nGreg Moore\, Rutgers University\nTitle: Summing Over Bordisms In 2d TQFT: Déjà Vu\nAbstract: This is basically a rerun of a talk I gave on zoom for the CMSA on March 16\, 2022. I will review the contents of a paper I wrote with Anindya Banerjee 2201.00903\, but including a few minor updates. I will describe a construction in Topological Field Theory (TFT) which was motivated by developments in the quantum gravity community. The goal is to provide an interpretation of a model discussed by D. Marolf and H. Maxfield 2002.08950 aimed at fitting their model within the functorial framework of Quantum Field Theory (QFT). Given a TFT one can consider – formally – the sum over all bordisms between fixed ingoing and outgoing spatial slices (with appropriate weight factors for the bordisms) of the amplitudes associated to the bordism by the TFT. This construction leads to convergent sums in d\leq 2 dimensions\, at least for for generic parameters of the TFT. I will describe a curious splitting property satisfied by the total amplitude. I view the splitting property as an alternative to ensemble-type interpretations. There will be a cameo appearance of a very interesting paper by Daniel Friedan 2306.00019 which purports to give an axiomatic framework for Euclidean Quantum Gravity (EQG) analogous to the functorial formalism of QFT. I will also note\, in passing\, that these extremely simple\, low-dimensional\, baby baby baby models of EQG admit global symmetries and continuous parameters. \n1:00 pm\nFarewell Lunch \n 
URL:https://cmsa.fas.harvard.edu/event/symmetries/
LOCATION:CMSA 20 Garden Street Cambridge\, Massachusetts 02138 United States
CATEGORIES:Workshop
ATTACH;FMTTYPE=image/png:https://cmsa.fas.harvard.edu/media/Poster_Workshop-on-Symmetries-and-Gravity_2.png
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/New_York:20240905T160000
DTEND;TZID=America/New_York:20240905T170000
DTSTAMP:20260711T064210
CREATED:20240710T192944Z
LAST-MODIFIED:20241212T195515Z
UID:10003398-1725552000-1725555600@cmsa.fas.harvard.edu
SUMMARY:CMSA/Math Fall Gathering
DESCRIPTION:September 5\, 2024 \n4:00 pm \nCMSA Common Room\, 20 Garden Street\, Cambridge MA \nAll CMSA and Math affiliates are invited.
URL:https://cmsa.fas.harvard.edu/event/fallgathering2024/
LOCATION:CMSA 20 Garden Street Cambridge\, Massachusetts 02138 United States
CATEGORIES:Event
ATTACH;FMTTYPE=image/jpeg:https://cmsa.fas.harvard.edu/media/CMSA-2-600x338-1-1.jpg
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/New_York:20180827T090000
DTEND;TZID=America/New_York:20190505T170000
DTSTAMP:20260711T064210
CREATED:20230904T082011Z
LAST-MODIFIED:20250303T193339Z
UID:10000010-1535360400-1557075600@cmsa.fas.harvard.edu
SUMMARY:PROGRAM ON TOPOLOGICAL ASPECTS OF CONDENSED MATTER
DESCRIPTION:During Academic year 2018-19\, the CMSA will be hosting a Program on Topological Aspects of Condensed Matter. New ideas rooted in topology have recently had a big impact on condensed matter physics\, and have highlighted new connections with high energy physics\, mathematics and quantum information theory. Additionally\, these ideas have found applications in the design of photonic systems and of materials with novel mechanical properties. The aim of this program will be to deepen these connections by foster discussion and seeding new collaborations within and across disciplines. \nAs part of the Program\, the CMSA will be hosting two workshops: \n\nWorkshop on Topology and Quantum Phases of Matter (August 27-28\, 2018)\nWorkshop on Topological Aspects of Condensed Matter (September 10-11\, 2019)\n\n. \nAdditionally\, a weekly Topology Seminar will be held on Mondays from 10:00-11:30pm in CMSA room G10. \n\nHere is a partial list of the mathematicians who have indicated that they will attend part or all of this special program\n\n\n\n\n\nName\nTentative Visiting Dates\n\n\n\n\n\nJason Alicea \n\n11/12/2018-11/16/2018\n\n\nMaissam Barkeshli\n4/22/2019 – 4/26/2019\n\n\nXie Chen\n4/15-17/2019 4/19-21/2019 4/24-30/2019\n\n\n\nLukasz Fidkowski \n\n1/7/2019-1/11/2019\n\n\n\nZhengcheng Gu \n\n8/15/2018-8/30/2018 & 5/9/2019-5/19/2019\n\n\n\nYin Chen He \n\n10/14/2018-10/27/2018\n\n\nAnton Kapustin\n8/26/2018-8/30/2018 & 3/28/2019-4/5/2019\n\n\n\nMichael Levin \n\n3/11/2019-3/15/2019\n\n\nYuan-Ming Lu\n4/29/2019-6/01/2019\n\n\n\nAdam Nahum \n\n4/2/2019- 4/19/2019\n\n\n\nMasaki Oshikawa \n\n4/22/2019-5/22/2019\n\n\nChong Wang\n 10/22/2018-11/16/2018\n\n\n\nJuven Wang \n\n4/1/2019-4/16/2019\n\n\nCenke Xu\n 8/26/2018-10/1/2018\n\n\n\nYi-Zhuang You \n\n4/1/2019-4/19/2019\n\n\n\nMike Zaletel \n\n5/1/2019-5/10/2019
URL:https://cmsa.fas.harvard.edu/event/topological-aspects-of-condensed-matter/
LOCATION:CMSA 20 Garden Street Cambridge\, Massachusetts 02138 United States
CATEGORIES:Programs
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/New_York:20170109T090000
DTEND;TZID=America/New_York:20170113T170000
DTSTAMP:20260711T064210
CREATED:20250305T194842Z
LAST-MODIFIED:20250305T194842Z
UID:10003717-1483952400-1484326800@cmsa.fas.harvard.edu
SUMMARY:Working Conference on Applications of Random Matrix Theory to Data Analysis\, January 9-13\, 2017
DESCRIPTION:The Center of Mathematical Sciences and Applications will be hosting a working Conference on Applications of Random Matrix Theory to Data Analysis\, January 9-13\, 2017.  The conference will be hosted in Room G10 of the CMSA Building located at 20 Garden Street\, Cambridge\, MA 02138. \nParticipants:\nGerard Ben Arous\, Courant Institute of Mathematical Sciences \nAlex Bloemendal\, Broad Institute \nArup Chakraburty\, MIT \n\n\n\nZhou Fan\, Stanford University \nAlpha Lee\, Harvard University \nMatthew R. McKay\, Hong Kong University of Science and Technology (HKUST) \nDavid R. Nelson\, Harvard University \nNick Patterson\, Broad Institute \nMarc Potters\, Capital Fund management \n\n\n\nYasser Roudi\, IAS \nTom Trogdon\, UC Irvine \nOrganizers: \n\n\n\nMichael Brenner\, Lucy Colwell\, Govind Menon\, Horng-Tzer Yau \nPlease click Program for a downloadable schedule with talk abstracts.\n\nSchedule: \n\n\n\nJanuary 9 – Day 1\n\n\n9:30am – 10:00am\nBreakfast & Opening remarks\n\n\n10:00am – 11:00am\nMarc Potters\, “Eigenvector overlaps and the estimation of large noisy matrices”\n\n\n11:00am – 12:00pm\nYasser Roudi\n\n\n12:00pm – 2:00pm\nLunch\n\n\n2:00pm\nAfternoon Discussion\n\n\nJanuary 10 – Day 2\n\n\n8:30am – 9:00am\nBreakfast\n\n\n9:00am – 10:00am\nArup Chakraburty\, “The mathematical analyses and biophysical reasons underlying why the prevalence of HIV strains and their relative fitness are simply correlated\, and pose the challenge of building a general theory that encompasses other viruses where this is not true.”\n\n\n10:00am – 11:00am\nTom Trogdon\, “On the average behavior of numerical algorithms”\n\n\n11:00am – 12:00pm\nDavid R. Nelson\, “Non-Hermitian Localization in Neural Networks”\n\n\n12:00pm – 2:00pm\nLunch\n\n\n2:00pm\nAfternoon Discussion\n\n\nJanuary 11 – Day 3\n\n\n8:30am – 9:00am\nBreakfast\n\n\n9:00am – 10:00am\nNick Patterson\n\n\n10:00am – 11:00am\nLucy Colwell\n\n\n11:00am – 12:00pm\nAlpha Lee\n\n\n12:00pm – 2:00pm\nLunch\n\n\n2:00pm-4:00pm\nAfternoon Discussion\n\n\n4:00pm\nGerard Ben Arous (Public Talk)\, “Complexity of random functions of many variables: from geometry to statistical physics and deep learning algorithms“\n\n\nJanuary 12 – Day 4\n\n\n8:30am – 9:00am\nBreakfast\n\n\n9:00am – 10:00am\nGovind Menon\n\n\n10:00am – 11:00am\nAlex Bloemendal\n\n\n11:00am – 12:00pm\nZhou Fan\, “Free probability\, random matrices\, and statistics”\n\n\n12:00pm – 2:00pm\nLunch\n\n\n2:00pm\nAfternoon Discussion\n\n\nJanuary 13 – Day 5\n\n\n8:30am – 9:00am\nBreakfast\n\n\n9:00am – 12:00pm\nFree for Working\n\n\n12:00pm – 2:00pm\nLunch\n\n\n2:00pm\nFree for Working\n\n\n\n\n* This event is sponsored by CMSA Harvard University.
URL:https://cmsa.fas.harvard.edu/event/working-conference-on-applications-of-random-matrix-theory-to-data-analysis-january-9-13-2017/
LOCATION:CMSA 20 Garden Street Cambridge\, Massachusetts 02138 United States
CATEGORIES:Conference,Event
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/New_York:20151128T090000
DTEND;TZID=America/New_York:20161202T170000
DTSTAMP:20260711T064210
CREATED:20240212T084906Z
LAST-MODIFIED:20250304T172138Z
UID:10001895-1448701200-1480698000@cmsa.fas.harvard.edu
SUMMARY:Mini-workshop on SYZ and Homological Mirror Symmetry
DESCRIPTION:The Center of Mathematical Sciences and Applications will be hosting a 4-day workshop on SYZ and Homological Mirror Symmetry and related areas on November 28 – December 2\, 2016 at Harvard CMSA Building: Room G10\, 20 Garden Street\, Cambridge\, MA 02138. \nOrganizers:\nBong Lian (Brandeis University)\, Siu-Cheong Lau (Boston University)\, Shing-Tung Yau (Harvard University) \nSpeakers:\n\nConan Leung\, Chinese University of Hong Kong\nJunwu Tu\, University of Missouri\nJingyu Zhao\, Columbia University\nDavid Treumann\, Boston College\nHiro Lee Tanaka\, Harvard University\nFabian Haiden\, Harvard University\nHansol Hong\, Harvard CMSA/Brandeis University\nNetanel Blaier\, Harvard CMSA/Brandeis University\nGarret Alston\, The University of Oklahoma\n\nPlease click Workshop Program for a downloadable schedule with talk abstracts. \nConference Schedule:\n\n\n\nMonday\, November 28 – Day 1\n\n\n\n\n\n\n10:30am –11:30am\nHiro Lee Tanaka\, “Floer theory through spectra”\n\n\nLunch\n\n\n1:00pm – 2:30pm\nFabian Haiden\, “Categorical Kahler Geometry”\n\n\n 2:30pm-2:45pm\n Break\n\n\n2:45pm – 4:15pm\nFabian Haiden\, “Categorical Kahler Geometry”\n\n\n4:30pm – 5:15pm\nGarret Alston\, “Potential Functions of Non-exact fillings”\n\n\n\n\n\n\n\nTuesday\, November 29 – Day 2\n\n\n\n\n\n\n10:30am –11:30am\nConan Leung\, “Remarks on SYZ”\n\n\nLunch\n\n\n1:00pm – 2:30pm\nJingyu Zhao\, “Homological mirror symmetry for open manifolds and Hodge theoretic invariants”\n\n\n 2:30pm-2:45pm\n Break\n\n\n2:45pm – 4:15pm\nHiro Lee Tanaka\, “Floer theory through spectra”\n\n\n4:30pm – 5:15pm\nHansol Hong\, “Mirror Symmetry for punctured Riemann surfaces and gluing construction”\n\n\n\n\n\n\n\nWednesday\, November 30 – Day 3\n\n\n\n\n\n\n10:30am –11:30am\nJunwu Tu\, “Homotopy L-infinity spaces and mirror symmetry”\n\n\nLunch\n\n\n1:00pm – 2:30pm\nJingyu Zhao\, “Homological mirror symmetry for open manifolds and Hodge theoretic invariants”\n\n\n 2:30-2:45pm\n Break\n\n\n2:45pm – 4:15pm\nDavid Treumann\, “Invariants of Lagrangians via microlocal sheaf theory”\n\n\n\n\n\n\n\n\n\n\n\n\nThursday\, December 1 – Day 4\n\n\n\n\n\n\n10:30am –11:30am\nDavid Treumann\, “Some examples in three dimensions”\n\n\nLunch\n\n\n1:00pm – 2:30pm\nJunwu Tu\, “Homotopy L-infinity spaces and mirror symmetry”\n\n\n 2:30-2:45pm\n Break\n\n\n2:45pm – 3:30pm\nNetanel Blaier\, “The quantum Johnson homomorphism\, and the symplectic mapping class group of 3-folds”\n\n\n\n\n\n\n\n* This event is sponsored by the Simons Foundation and CMSA Harvard University.
URL:https://cmsa.fas.harvard.edu/event/mini-workshop-on-syz-and-homological-mirror-symmetry/
LOCATION:CMSA 20 Garden Street Cambridge\, Massachusetts 02138 United States
CATEGORIES:Event,Workshop
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/New_York:20150102T090000
DTEND;TZID=America/New_York:20151231T170000
DTSTAMP:20260711T064210
CREATED:20230904T081503Z
LAST-MODIFIED:20250228T180655Z
UID:10000051-1420189200-1451581200@cmsa.fas.harvard.edu
SUMMARY:MATH-PHYSICS PROGRAM
DESCRIPTION:In the past thirty years there have been deep interactions between mathematics and theoretical physics which have tremendously enhanced both subjects. The focal points of these interactions include string theory\, general relativity\, and quantum many-body theory. \nString theory has been at the center of the ongoing effort to uncover the fundamental principles of nature and in particular to unify Einstein’s geometric theory of gravity with quantum theory. The development of this field has sparked a historically unprecedented synergy between mathematics and physics. Progress at the forefront of theoretical physics has relied crucially on very recent developments in pure mathematics. At the same time insights from physics have led to both new branches of pure mathematics as well as dramatic progress in old branches. \nSeveral examples from the recent past exemplifying this synergy include the prediction from string theory of mirror symmetry\, a highly unexpected mathematical equivalence between distinct pairs of Calabi-Yau manifolds. This fueled exciting developments in algebraic\, enumerative and symplectic geometry. At the same time the realization of string theory as a phenomenologically viable physical theory depends crucially on detailed mathematical properties of these manifolds. In Einstein’s theory of general relativity the proofs of the positive energy theorem and the stability of flat spacetime were accompanied by fundamental new results in functional analysis\, differential geometry and minimal surface theory. In the coming decades we expect many more important discoveries to arise from the interface of mathematics and physics. The Cheng Fund will foster these efforts. \n\n\nHere is a partial list of the mathematicians who have indicated that they will attend part or all of this special program \n\n\n\n\nName\nTentative Visiting Dates\n\n\n\n\nPo-Ning Chen\n2/1/15-4/30/15\n\n\nHong-Jian He\n3/5/15-5/5/15\n\n\nMonica Guica\n12/1/14-3/15/15\n\n\nAmer Iqbal\n1/8/15-4/8/15\n\n\nSuvrat Raju\n2/25/15-5/25/15\n\n\nMithat Ünsal\n9/1/15-12/31/15
URL:https://cmsa.fas.harvard.edu/event/math-physics-program/
LOCATION:CMSA 20 Garden Street Cambridge\, Massachusetts 02138 United States
CATEGORIES:Programs
END:VEVENT
END:VCALENDAR