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SUMMARY:Quantum Field Theory and Topological Phases via Homotopy Theory and Operator Algebras
DESCRIPTION:Workshop on Quantum Field Theory and Topological Phases via Homotopy Theory and Operator Algebras \nDates: June 30 – July 11\, 2025 \nLocation: CMSA\, 20 Garden Street\, Cambridge MA and Max Planck Institute for Mathematics\, Bonn\, Germany \nThis event is a twinned workshop at the CMSA (Harvard) and the Max Planck Institute for Mathematics (Bonn). Lectures will alternate between the two sites\, watched simultaneously on both sides\, and there will be opportunities for dialogue between the locations. The first week will contain four pedagogical lecture series; lecturers and locations are \nMichael Hopkins\, Harvard  (CMSA)Alexei Kitaev\, Caltech (CMSA)Pieter Naaijkens\, Cardiff (MPIM)Bruno Nachtergaele\, UC Davis (MPIM) \nThe second week will consist of research talks. \nParticipants are strongly encouraged to attend at the location that minimizes travel and hence the ecological impact of the conference. \nThe application deadline was March 16\, 2025. \nDirections to CMSA \nMPIM-Bonn location: https://www.mpim-bonn.mpg.de/qft25  \n  \nRegister for Zoom Webinar \n  \nQuantum Field Theory (QFT) and Quantum Statistical Mechanics are central to high energy physics and condensed matter physics; they also raise deep questions in mathematics. The application of operator algebras to these areas of physics is well-known. Recent developments indicate that to understand some aspects QFT properly a further ingredient is needed: homotopy theory and infinity-categories. One such development is the recognition that symmetry in a QFT is better described by a homotopy type rather than a group (so-called generalized symmetries). Another one is the work of Lurie and others on extended Topological Field Theory (TFT) and the Baez-Dolan cobordism hypothesis. Finally\, there is a conjecture of Kitaev that invertible phases of matter are classified by homotopy groups of an Omega-spectrum. This workshop will bring together researchers and students approaching this physics using different mathematical techniques: operator algebras\, homotopy theory\, higher category theory\, etc. The goal is to catalyze new interactions between different communities. At the workshop recent developments will be reviewed and hopefully progress can be made on two outstanding problems: the Kitaev conjecture as well as the long-standing goal of finding a proper mathematical formulation for QFT. \nOrganizers: \n\nDan Freed\, Harvard University CMSA & Math\nDennis Gaitsgory\, MPIM Bonn\nOwen Gwilliam\, UMass Amherst\nAnton Kapustin\, Caltech\nCatherine Meusburger\, University of Erlangen-Nürnberg\n\n  \nTalks are recorded and available on the CMSA Youtube Playlist. \n\nBACKGROUND READING \nParticipants are encouraged to have some basic familiarity with the definition of a C*-algebra and quantum spin system. Some knowledge of quantum channels (completely positive trace-preserving maps) and quantum circuits will be useful. Some knowledge of Clifford algebras will also be helpful. \nPossible references include: \n 1) arXiv:1311.2717 (Sections 2.1\, 2.2\, 2.4\, and 2.5 up to Theorem 2.5.3) \n 2) Lectures by Daniel Spiegel on “C*-Algebraic Foundations of Quantum Spin Systems”\, at the Summer School on C*-Algebraic Quantum Mechanics and Topological Phases of Matter\, University of Colorado Boulder\, July 29 to August 2\, 2024. (lecture notes and video recordings: https://sites.google.com/colorado.edu/caqm). \n3) https://nextcloud.tfk.ph.tum.de/etn/wp-content/uploads/2022/09/JvN_lecture_notes_S2016_abcde-1.pdf \n4) https://en.wikipedia.org/wiki/Classification_of_Clifford_algebras \n5) Karoubi\, K-theory\, section III.3 \n6.) Alexei Kitaev: A norm bound for 1D local matrices (pdf) \n  \nSchedule Times are Eastern Time  \ndownload schedule pdf \nWorkshop on Quantum Field Theory and Topological Phases via Homotopy Theory and Operator Algebras \nJune 30 – July 11\, 2025 \n  \n\n\n\n\nMonday\, June 30 \n\n\n\n\n8:00–9:00 am \n\n\nMPIM \n\n\nBruno Nachtergaele\, UC Davis \nTitle: Ground states of quantum lattice systems: Quantum Lattice Systems: observables\, dynamics\, ground states\, GNS representation\, ground state gap\, examples \n\n\n\n\n9:00–9:30 am \n\n\n  \n\n\nBreakfast break \n\n\n\n\n9:30–10:30 am \n\n\nCMSA \n\n\nMichael Hopkins\, Harvard \nTitle: Lattice models and topological quantum field theories I \nAbstract: This series will cover the relationship between gapped Hamiltonian lattice models and topological quantum field theories\, with an emphasis on a conjecture of Kitaev. \n\n\n\n\n10:30–10:45 am \n\n\n  \n\n\nbreak \n\n\n\n\n10:45–11:45 am \n\n\nMPIM \n\n\nPieter Naajkens\, Cardiff \nTitle: Introduction to superselection sector theory: Motivation and introduction of basic setting \nAbstract: (week 1 lectures) In this series of lectures\, I will give an introduction to the operator-algebraic (Doplicher-Haag-Roberts) approach to study the superselection sectors of a (2D) gapped quantum spin system. The sectors have a rich mathematical structure of a braided monoidal category. This category describes all the algebraic properties of the ‘anyons’ or ‘charges’ such quantum spin systems can have. The aim of these lectures is to build up this theory from first principles\, using simple examples of topologically ordered models to illustrate the main ideas. If time permits\, I will elaborate on how this fits into the larger programme of the classification of gapped phases of matter\, and long-range entangled states in particular. No prior knowledge of operator algebras or tensor categories is assumed. \nSLIDES (pdf) \n\n\n\n\n11:45 am –12:00 pm \n\n\n  \n\n\nbreak \n\n\n\n\n12:00–1:00 pm \n\n\nCMSA \n\n\nAlexei Kitaev\, Caltech \nTitle: Local definitions of gapped Hamiltonians and topological and invertible states I \n\n\n\n\nTuesday\, July 1 \n\n\n\n\n8:00–9:00 am \n\n\nMPIM \n\n\nBruno Nachtergaele\, UC Davis \nTitle: Ground states of quantum lattice systems: Quasilocality: almost local observables and interactions\, Lieb-Robinson bounds\, quasi-adiabatic evolution\, stability I \n\n\n\n\n9:00–9:30 am \n\n\n  \n\n\nBreakfast break \n\n\n\n\n9:30–10:30 am \n\n\nCMSA \n\n\nMichael Hopkins\, Harvard \nTitle: Lattice models and topological quantum field theories II \n\n\n\n\n10:30–10:45 am \n\n\n  \n\n\nbreak \n\n\n\n\n10:45–11:45 am \n\n\nMPIM \n\n\nPieter Naajkens\, Cardiff \nTitle: Introduction to superselection sector theory: Building the braided (fusion) category of superselection sectors I \nSLIDES (pdf) \n\n\n\n\n11:45 am –12:00 pm \n\n\n  \n\n\nbreak \n\n\n\n\n12:00–1:00 pm \n\n\nCMSA \n\n\nAlexei Kitaev\, Caltech \nTitle: Local definitions of gapped Hamiltonians and topological and invertible states II \n\n\n\n\nWednesday\, July 2 \n\n\n\n\n8:00–9:00 am \n\n\nMPIM \n\n\nBruno Nachtergaele\, UC Davis \nTitle: Ground states of quantum lattice systems: Quantum Entanglement in many-body systems: short-range entangled states\, topological entanglement\, stability II \n\n\n\n\n9:00–9:30 am \n\n\n  \n\n\nBreakfast break \n\n\n\n\n9:30–10:30 am \n\n\nCMSA \n\n\nMichael Hopkins\, Harvard \nTitle: Lattice models and topological quantum field theories III \n\n\n\n\n10:30–10:45 am \n\n\n  \n\n\nbreak \n\n\n\n\n10:45–11:45 am \n\n\nMPIM \n\n\nPieter Naajkens\, Cardiff \nTitle: Introduction to superselection sector theory: Building the braided (fusion) category of superselection sectors II \nSLIDES (pdf) \n\n\n\n\n11:45 am –12:00 pm \n\n\n  \n\n\nbreak \n\n\n\n\n12:00–1:00 pm \n\n\nCMSA \n\n\nAlexei Kitaev\, Caltech \nTitle: Local definitions of gapped Hamiltonians and topological and invertible states III \n\n\n\n\nThursday\, July 3 \n\n\n\n\n8:00–9:00 am \n\n\nMPIM \n\n\nBruno Nachtergaele\, UC Davis \nTitle: Ground states of quantum lattice systems: Quantum Phase Diagrams: order parameters\, topological invariants\, examples \n\n\n\n\n9:00–9:30 am \n\n\n  \n\n\nBreakfast break \n\n\n\n\n9:30–10:30 am \n\n\nCMSA \n\n\nMichael Hopkins\, Harvard \nTitle: Lattice models and topological quantum field theories IV \n\n\n\n\n10:30–10:45 am \n\n\n  \n\n\nbreak \n\n\n\n\n10:45–11:45 am \n\n\nMPIM \n\n\nPieter Naajkens\, Cardiff \nTitle: Introduction to superselection sector theory: Classification of phases and long-range entanglement \nSLIDES (pdf) \n\n\n\n\n11:45 am –12:00 pm \n\n\n  \n\n\nbreak \n\n\n\n\n12:00–1:00 pm \n\n\nCMSA \n\n\nAlexei Kitaev\, Caltech \nTitle: Local definitions of gapped Hamiltonians and topological and invertible states IV \n\n\n\n\nNo talks Friday July 4  \n\n\n\n\n  \n\n\n\n\nMonday July 7 \n\n\n\n\n8:00–9:00 am \n\n\nMPIM \n\n\nJackson van Dyke\, TU Munich \nTitle: Moduli spaces of projective 3d TQFTs \nAbstract: A gapped quantum system is well-approximated at low energy by a projective topological field theory. Therefore questions concerning the classification\, symmetries\, and anomalies of gapped quantum systems can be reinterpreted via the homotopy theory of the moduli space of such theories. I will describe a moduli space of 3-dimensional TQFTs\, and the sense in which its homotopy theory informs us about the low energy behavior of gapped systems in 2+1 dimensions. This moduli space depends on the fixed target category: Explicitly\, it is built from the classifying spaces of higher groups of automorphisms of ribbon categories. The emphasis will be on target categories which have convenient algebraic features\, yet are analytically robust enough to allow for boundary/relative theories defined in terms of unitary representations on topological vector spaces. \n\n\n\n\n9:00–9:30 am \n\n\n  \n\n\nBreakfast break \n\n\n\n\n9:30–10:30 am \n\n\nCMSA \n\n\nConstantin Teleman\, UC Berkeley \nTitle: Quantizing homotopy types \nAbstract: Kontsevich (90’s) proposed a topological quantization of (sigma-models into) finite homotopy types to top dimensions (d\, d+1). Its enhancement to a `fully extended’ TQFT was described later (Freed\, Hopkins\, Lurie and the speaker) in the target category of iterated algebras. Independently\, Chas and Sullivan constructed a (partially defined) 2-dimensional TQFT (d=1) with target compact oriented manifolds. I will briefly review the features of the finite homotopy theory and its boundary conditions\, with particular interest in Dirichlet conditions; their analogue in Chas-Sullivan theory (older work by Blumberg\, Cohen and the speaker). Finally\, I propose a generalization combining these to a higher-dimensional Chas-Sullivan theory. \nSlides (link) \n\n\n\n\n10:30–10:45 am \n\n\n  \n\n\nbreak \n\n\n\n\n10:45–11:45 am \n\n\nMPIM \n\n\nMatthias Ludewig\, University of Greifswald \nTitle: Generalized Kitaev Pairings and Higher Berry curvature in coarse geometry \nAbstract: In Appendix C of his “Anyons” paper\, Kitaev introduced the notion of a “generalized Chern number” for a 2-dimensional system by diving the system in three ordered parts and measuring a signed rotational flux. This construction has since been used by several authors to measure topological non-triviality of a physical system. In recent work with Guo Chuan Thiang\, we observe that the recipe provided by Kitaev can be interpreted in coarse geometry as the pairing of a K-theory class with a coarse cohomology class. A corresponding index theorem then provides a proof that the set of values of this “Kitaev pairing” is always quantized\, as already argued by Kitaev. In our work\, we generalize Kitaev’s definition and the corresponding quantization result to arbitrary dimensions. By replacing a single Hamiltonian with a whole family of Hamiltonians (parametrized by a space X)\, we recover and extend the construction of “Higher Berry curvatures” by Kapustin and Spodyneiko. Given a coarse cohomology class\, we obtain a characteristic class on the parameter space X\, which is integral whenever integrated against a cycle in X that lies in the image of the homological Chern character (so\, in particular\, spheres in X). \n\n\n\n\n11:45 am –12:00 pm \n\n\n  \n\n\nbreak \n\n\n\n\n12:00–1:00 pm \n\n\nCMSA \n\n\nTheo Johnson-Freyd\, Perimeter Institute \nTitle: Some thoughts about the Kapustin–Kitaev cobordism conjecture \nAbstract: In 2013\, Kitaev explained that\, under some reasonable locality hypotheses\, gapped invertible phases of bosonic lattice models in different dimensions are naturally organized into an \Omega-spectrum. The following year\, Kapustin conjectured that this spectrum is dual to a Thom spectrum\, specifically (smooth) oriented bordism MSO\, and that for fermionic lattice models one sees instead the dual to spin bordism. In 2016\, Freed and Hopkins proved Kapustin’s conjecture for invertible phases of continuous unitary QFTs valued in an at-the-time conjectural universal target category. Freed and Hopkins put bordism categories into the statement of the problem\, by working from the beginning with continuous QFTs. Kapustin’s conjecture for lattice models remains open.David Reutter and I\, in ongoing work in progress\, have investigating Kapustin’s conjecture from the perspective of deeper category theory. We have built the universal target category for phases satisfying a finite semisimplicity hypothesis\, and we are working on relaxing finite semisimplicity. We can show that any spectrum of invertible finite-semisimple phases will indeed be dual to a Thom spectrum for some topological group G acting on the spectrum of spheres. For example\, if one looks just at those bosonic phases which can be topologically condensed from the vacuum\, G is almost the (oriented) piecewise linear group\, whose Thom spectrum is the bordism spectrum MSPL is the (oriented) *piecewise* smooth manifolds; the difference between MSPL and MSO is only visible in dimensions 7 and above. I say almost because in fact our G is what you would get if you tried to build MSPL\, but could only make finitary measurements\, which surely is explained by our restriction to condensable semisimple TQFTs. We conjecture that MSPL\, rather than MSO\, classifies invertible gapped phases of bosonic lattice models.The general relation between MSPL and topological phases is explained by a certain “surgery exact sequence” for topological phases that mirrors the surgery sequence for MSPL. By studying this sequence\, we can also answer the question of which invertible phases admit a gapped boundary condition. In particular that only (the trivial phase and) the Arf–Kervaire invariants admit finite-semisimple gapped boundary conditions. \nSLIDES (pdf) \n\n\n\n\nTuesday\, July 8 \n\n\n\n\n8:00–9:00 am \n\n\nMPIM \n\n\nDavid Reutter\, University of Hamburg \nTitle: On the categorical spectrum of topological quantum field theories \nAbstract: As originally suggested by Kitaev\, invertible topological quantum field theories of varying dimensions should assemble into a spectrum/generalized homology theory. A candidate for such a spectrum of invertible TQFTs was proposed by Freed and Hopkins\, with the defining property that (isomorphism classes of) n-dimensional invertible TQFTs are completely determined by their partition functions on closed n-manifolds. More generally\, not-necessarily-invertible TQFTs should assemble into a ‘categorical spectrum’\, an analogue of a spectrum with non-invertible cells at each level. In this talk\, I will explain that there exists a unique such categorical spectrum satisfying a list of reasonable assumptions on the collection of (compact/very finite & discrete) TQFTs; one of these assumptions being that its invertibles agree with Freed and Hopkins’ suggestion. I will explain these assumptions\, sketch how this categorical spectrum looks like in low-dimensions\, outline its construction\, and how it may be used to learn about gapped boundaries of anomaly theories in high dimensions. This is based on work in progress with Theo Johnson-Freyd. \n\n\n\n\n9:00–9:30 am \n\n\n  \n\n\nBreakfast break \n\n\n\n\n9:30–10:30 am \n\n\nCMSA \n\n\nAgnes Beaudry\, UC Boulder \nTitle: An algebraic theory of planon-only fracton orders \nAbstract: In this talk\, I will describe an algebraic theory for planon-only abelian fracton orders. These are three-dimensional gapped phases with the property that fractional excitations are abelian particles restricted to move in parallel planes. The fusion and statistics data can be identified with a finitely generated module over a Laurent polynomial ring together with a U(1)-valued quadratic form. These systems thus lend themselves to an elegant algebraic theory which we expect will lead to easily computable phase invariants and a classification. As a starting point\, we establish a necessary condition for physical realizability\, the excitation-detector principle\, which I will explain. We conjecture that this criterion is also sufficient for realizability. I will also discuss preliminary classification results.This talk is based on joint with Michael Hermele\, Wilbur Shirley and Evan Wickenden. \n\n\n\n\n10:30–10:45 am \n\n\n  \n\n\nbreak \n\n\n\n\n10:45–11:45 am \n\n\nMPIM \n\n\nJoão Faria Martins\, University of Leeds \nTitle: A categorification of Quinn’s finite total homotopy TQFT with application to TQFTs and once-extended TQFTs derived from discrete higher gauge theory \nAbstract: Quinn’s Finite Total Homotopy TQFT is a topological quantum field theory defined for any dimension n of space\, depending on the choice of a homotopy finite space B. For instance\, B can be the classifying space of a finite group or a finite 2-group.In this talk\, I will report on recent joint work with Tim Porter on once-extended versions of Quinn’s Finite Total Homotopy TQFT\, taking values in the symmetric monoidal bicategory of groupoids\, linear profunctors\, and natural transformations between linear profunctors. The construction works in all dimensions\, yielding (0\,1\,2)-\, (1\,2\,3)-\, and (2\,3\,4)-extended TQFTs\, given a homotopy finite space B. I will  show how to compute these once-extended TQFTs when B is the classifying space of a homotopy 2-type\, represented by a crossed module of groups.Reference: Faria Martins J\, Porter T: “A categorification of Quinn’s finite total homotopy TQFT with application to TQFTs and once-extended TQFTs derived from strict omega-groupoids.” arXiv:2301.02491 [math.CT] \n\n\n\n\n11:45 am –12:00 pm \n\n\n  \n\n\nbreak \n\n\n\n\n12:00–1:00 pm \n\n\nCMSA \n\n\nEmil Prodan\, Yeshiva University \nTitle: Mapping the landscape of frustration-free models \nAbstract: Frustration-free models are of great interest because they are amenable to specialized techniques and their understanding is more complete among the general quantum spin models. In this talk\, I will establish an almost bijective relation between frustration-free families of projections and a subclass of hereditary subalgebras defined by an intrinsic property. This relation sets further synergies between frustration-free models and open projections in double duals\, and subsets of pure states spaces. These connections enable a better understanding of the class of frustration-free models. For example\, the open projections in the double dual derived from frustration-free models is dense in the norm-topology in the space of generic open projections\, thus assuring us that\, for many purposes\, we can choose to work with frustration-free models without losing generality. Furthermore\, the Cuntz semigroup\, originally designed to classify the positive elements of C*-algebra\, has been proven to also classify the open projections. Given the mentioned connections\, we now have a new device to investigate the ground states of quantum spin models. \nSLIDES (pdf) \n\n\n\n\nWednesday\, July 9 \n\n\n\n\n8:00–9:00 am \n\n\nMPIM \n\n\nAlexander Schenkel\, University of Nottingham \nTitle: C*-categorical prefactorization algebras for superselection sectors and topological order \nAbstract: I will present a geometric framework to encode the algebraic structures on the category of superselection sectors of an algebraic quantum field theory on the n-dimensional lattice Z^n. I will show that\, under certain assumptions which are implied by Haag duality\, the monoidal C*-categories of localized superselection sectors carry the structure of a locally constant prefactorization algebra over the category of cone-shaped subsets of Z^n. Employing techniques from higher algebra\, one extracts from this datum an underlying locally constant prefactorization algebra defined on open disks in the cylinder R^1 x S^{n-1}. While the sphere S^{n-1} arises geometrically as the angular coordinates of cones\, the origin of the line R^1 is analytic and rooted in Haag duality. The usual braided (for n=2) or symmetric (for n>2) monoidal C*-categories of superselection sectors are recovered by removing a point of the sphere and using the equivalence between E_n-algebras and locally constant prefactorization algebras defined on open disks in R^n. The non-trivial homotopy groups of spheres induce additional algebraic structures on these E_n-monoidal C*-categories\, which in the simplest case of Z^2 is given by a braided monoidal self-equivalence arising geometrically as a kind of ‘holonomy’ around the circle S^1.This talk is based on joint work with Marco Benini\, Victor Carmona and Pieter Naaijkens. \n\n\n\n\n9:00–9:30 am \n\n\n  \n\n\nBreakfast break \n\n\n\n\n9:30–10:30 am \n\n\nCMSA \n\n\nLukasz Fidkowski\, University of Washington \nTitle: Non-invertible bosonic chiral symmetry on the lattice \nAbstract: We construct a Hamiltonian lattice realization of the non-invertible chiral symmetry that mimics an axial rotation at a rational angle in a U(1) gauge theory with bosonic charged matter.  We provide a heuristic argument that this setup allows a symmetric Hamiltonian which flows\, at low energies\, to a known field theory with this symmetry. \n\n\n\n\n10:30–10:45 am \n\n\n  \n\n\nbreak \n\n\n\n\n10:45–11:45 am \n\n\nMPIM \n\n\nNils Carqueville\, University of Vienna \nTitle: Gauging categorical symmetries \nAbstract: Orbifold data are categorical symmetries that can be gauged in oriented defect topological quantum field theories. We review the general construction and apply it to 2-group symmetries of 3-dimensional TQFTs; upon further specialisation this leads to equivariantisation of G-crossed braided fusion categories. We also describe a proposal\, via higher dagger categories\, to gauging categorical symmetries in the context of other tangential structures. This is based on separate projects with Benjamin Haake and Tim Lüders. \n\n\n\n\n11:45 am –12:00 pm \n\n\n  \n\n\nbreak \n\n\n\n\n12:00–1:00 pm \n\n\nCMSA \n\n\nNikita Sopenko\, IAS \nTitle: Reflection positivity and invertible phases of 2d quantum many-body systems \nAbstract: Reflection positivity is a property that is usually taken as an assumption in the classification of topological phases of matter via continuous quantum field theories. For general quantum many-body systems\, this property does not hold. This raises the question of whether it somehow emerges in the effective theory from the microscopic description\, thereby justifying the field-theoretic approach.In this talk\, I will discuss reflection positivity in the context of invertible phases of two-dimensional lattice systems. I will explain why every such phase admits a reflection-positive representative\, and why inverse phases are represented by complex conjugate states. I will also introduce an index that distinguishes these phases and is conjecturally related to the chiral central charge. \n\n\n\n\nThursday\, July 10 \n\n\n\n\n8:00–9:00 am \n\n\nMPIM \n\n\nIlka Brunner\, Ludwig-Maximilians University of Munich \nTitle: Defects as functors between phases of Abelian gauged linear sigma models \nAbstract: Defects act naturally on boundary conditions\, providing functors between D-brane categories. In the context of gauged linear sigma models\, one can use defects to transport branes from one phase to another. In this talk\, I will show how to construct such defects explicitly. \n\n\n\n\n9:00–9:30 am \n\n\n  \n\n\nBreakfast break \n\n\n\n\n9:30–10:30 am \n\n\nCMSA \n\n\nDavid Penneys\, Ohio State \nTitle: Holography for bulk-boundary local topological order \nAbstract: In previous joint work [arXiv:2307.12552] with C. Jones\, Naaijkins and Wallick\, we introduced local topological order (LTO) axioms for quantum spin systems which allowed us to define a physical boundary manifested by a net of boundary algebras in one dimension lower. This gives a formal setting for topological holography\, where the braided tensor category of DHR bimodules of the physical boundary algebra captures the bulk topological order.In joint work with C. Jones and Naaijkens\, we extend the LTO axioms to quantum spin systems equipped with a topological boundary\, again producing a physical boundary algebra for the bulk-boundary system\, whose category of (topological) boundary DHR bimodules recovers the topological boundary order. We perform this analysis in explicit detail for Levin-Wen and Walker-Wang bulk-boundary systems.Along the way\, we introduce a 2D braided categorical net of algebras built from a unitary braided fusion category (UBFC)\, which arise as boundary algebras of Walker-Wang models. We consider the canonical state on this braided categorical net corresponding to the standard topological boundary for the Walker-Wang model. Interestingly\, in this state\, the cone von Neumann algebras are type I with finite dimensional centers\, in contrast with the type II and III cone von Neumann algebras from the Levin-Wen models studied in [arXiv:2307.12552]. Their superselection sectors recover the underlying unitary category of our UBFC\, and we conjecture the superselection category also captures the fusion and braiding. \n\n\n\n\n10:30–10:45 am \n\n\n  \n\n\nbreak \n\n\n\n\n10:45–11:45 am \n\n\nMPIM \n\n\nChristoph Schweigert\, University of Hamburg \nTitle: Tensor network states: a topological field theory perspective. \nAbstract: Projected entangled pair states (PEPS) and matrix product operators (MPO) are standard tools in quantum information theory and quantum many-body physics. We explain how to understand them in terms of Turaev-Viro models on manifolds with boundary. We then sketch how a recently developed categorical Morita theory for spherical module categories can be used to find generalizations of the standard PEPS tensors. \n\n\n\n\n11:45 am –12:00 pm \n\n\n  \n\n\nbreak \n\n\n\n\n12:00–1:00 pm \n\n\nCMSA \n\n\nGreg Moore\, Rutgers \nTitle: p-form puzzles \nAbstract: It is commonly stated that level k BF theory for a p-form (and a form of complementary dimension) is equivalent to a homotopy sigma model with target space K(A\,p) where A is a cyclic group of order k.  Some aspects of this standard statement are puzzling me. I’ll explain what they are. (Perhaps someone in the audience can resolve my puzzles.) Then I’ll revisit the (again standard) electromagnetic duality of p-form electrodynamics. The conclusion will be that a slightly modified version of Ray-Singer torsion is the partition function of an invertible topological field theory. \n\n\n\n\nFriday\, July 11Note: On Friday\, there will be separate schedules for Bonn and CMSA. \nTo view the Bonn schedule\, please visit the program page at: https://www.mpim-bonn.mpg.de/qft25 \n\n\n\n\n8:00–9:00 am \n\n\nCMSA \n\n\nMarkus Pflaum\, UC Boulder \nTitle: A tour d’horizon through homotopical aspects of C*-algebraic quantum spin systems \nAbstract: In the talk I report on joint work with Beaudry\, Hermele\, Moreno\, Qi and Spiegel\, where a homotopy theoretic framework for studying state spaces of quantum lattice spin systems has been introduced using the language of C*-algebraic quantum mechanics. First some old and new results about the state space of the quasi-local algebra of a quantum lattice spin system when endowed with either the natural metric topology or the weak* topology will be presented. Switching to the algebraic topological side\, the homotopy groups of the unitary group of a UHF algebra will then be determined and it will be indicated that the pure state space of any UHF algebra in the weak* topology is weakly contractible. In addition\, I will show at the example of non-commutative tori that also in the case of a not commutative C*-algebra\, the homotopy type of the state space endowed with the weak* topology can be non-trivial and is neither deformation nor Morita invariant. Finally\, I indicate how such tools together with methods from higher homotopy theory such as E_infinity spaces may lead to a framework for constructing Kitaev’s loop-spectrum of bosonic invertible gapped phases of matter. \n\n\n\n\n9:00–9:30 am \n\n\n  \n\n\nBreakfast break \n\n\n\n\n9:30–11:00 am \n\n\nCMSA \n\n\nSpeed Talks \nBen Gripaios\, University of CambridgeTitle: Locality and smoothness of QFTs \nCarolyn Zhang\, Harvard UniversityTitle: SymTFT approach for (non-)invertible symmetries of mixed states \nRoman Geiko\, UCLATitle: Omega-spectrum of stabilizer invertible phases \n\n\n\n\n11:00–11:15 am \n\n\n  \n\n\nbreak \n\n\n\n\n11:15–12:45 pm \n\n\nCMSA \n\n\nSpeed Talks continued \nEric Roon\, Michigan State UniversityTitle: Finitely Correlated States Driven by Topological Dynamics \nDmitri Pavlov\, Texas Tech UniversityTitle: The classification of two-dimensional extended conformal field theories \nBowen Shi\, University of Illinois Urbana-ChampaignTitle: Mathematical Puzzles from the Entanglement Bootstrap: On Immersions and regular homotopySLIDES (pdf) \n\n\n\n\n  \n 
URL:https://cmsa.fas.harvard.edu/event/mpqft25/
LOCATION:Hybrid
CATEGORIES:Event,Workshop
ATTACH;FMTTYPE=image/png:https://cmsa.fas.harvard.edu/media/QFT_2025.png
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/New_York:20250731T110000
DTEND;TZID=America/New_York:20250731T120000
DTSTAMP:20260417T185739
CREATED:20250730T163542Z
LAST-MODIFIED:20250730T182012Z
UID:10003759-1753959600-1753963200@cmsa.fas.harvard.edu
SUMMARY:Joint BHI/CMSA Foundation Seminar: The semiclassical energy outflux emerging from a collapsing shell
DESCRIPTION:Joint BHI/CMSA Foundation Seminar \nLocation: BHI seminar room \nSpeaker: Noa Zilberman (Princeton University) \nTitle: The semiclassical energy outflux emerging from a collapsing shell \nAbstract: When a compact object collapses to form a black hole\, quantum field theory predicts the emission of an energy outflux to future null infinity\, which later relaxes to Hawking radiation. Within the semiclassical framework\, we derive a simple\, closed form\, analytical expression for the energy outflux emitted from a spherical thin null shell collapsing to form a black hole. In particular\, this energy outflux vanishes (quadratically in r-2M) as the shell approaches the horizon. This result refutes claims that the Hawking energy outflux originates from the collapsing body\, showing instead that it develops in a broad strong-field region. Additionally\, this vanishing implies that semiclassical backreaction cannot prevent or significantly affect the classical process of gravitational collapse and horizon formation (as sometimes claimed). \n 
URL:https://cmsa.fas.harvard.edu/event/joint-bhi-cmsa-foundation-seminar-the-semiclassical-energy-outflux-emerging-from-a-collapsing-shell/
LOCATION:Black Hole Initiative\, 20 Garden Street\, Cambridge MA\, CMSA\, 20 Garden Street\, Cambridge\, MA\, 02138\, United States
CATEGORIES:Foundation Seminar
ATTACH;FMTTYPE=image/png:https://cmsa.fas.harvard.edu/media/CMSA-BHI-Joint-Foundations-Seminar-07.31.25-scaled.png
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/New_York:20250902T161500
DTEND;TZID=America/New_York:20250902T183000
DTSTAMP:20260417T185739
CREATED:20250829T204330Z
LAST-MODIFIED:20250902T170240Z
UID:10003773-1756829700-1756837800@cmsa.fas.harvard.edu
SUMMARY:Fukaya category and gauge theory
DESCRIPTION:Geometry and Quantum Theory Seminar \nSpeaker: Saman Habibi Esfahani\, Harvard CMSA \nTitle: Fukaya category and gauge theory \nAbstract: After setting up some background\, I will discuss the Fukaya $A_\ infty$-category and several instances where it appears in gauge theory\, such as in the study of flat connections on Riemann surfaces\, holomorphic sections of some hyperkähler bundles\, and instantons and holomorphic curves in K3 surfaces. If time permits\, I will also outline potential applications of these ideas to the study of 3-manifolds and manifolds with special holonomy. \n  \n 
URL:https://cmsa.fas.harvard.edu/event/quantumgeo_9225/
LOCATION:Science Center 507\, 1 Oxford Street\, Cambridge\, 02138
CATEGORIES:Geometry and Quantum Theory Seminar
ATTACH;FMTTYPE=image/png:https://cmsa.fas.harvard.edu/media/CMSA-Geometry-Quantum-Theory-9.2.25.edit_-scaled.png
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/New_York:20250903T160000
DTEND;TZID=America/New_York:20250903T173000
DTSTAMP:20260417T185739
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:20250908T090000
DTEND;TZID=America/New_York:20250910T170000
DTSTAMP:20260417T185739
CREATED:20250502T174228Z
LAST-MODIFIED:20250909T151806Z
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. \nRegistration required \nIn-person registration \nZoom meeting registration \n  \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:20250909T161500
DTEND;TZID=America/New_York:20250909T183000
DTSTAMP:20260417T185739
CREATED:20250829T204407Z
LAST-MODIFIED:20250908T135742Z
UID:10003774-1757434500-1757442600@cmsa.fas.harvard.edu
SUMMARY:Higher categories of cobordisms
DESCRIPTION:Geometry and Quantum Theory Seminar \nSpeaker: Lorenzo Riva \nTitle: Higher categories of cobordisms \nAbstract: I will give a brief introduction to topological field theories from a higher categorical perspective. After saying a few things about higher categories\, I will define a family of n-categories of bordisms and talk about their universal properties. I will try to squeeze in the canonical example — representations of the 2-dimensional oriented bordism 2-category are separable symmetric Frobenius algebras — and\, time permitting\, talk about my current work. \n 
URL:https://cmsa.fas.harvard.edu/event/quantumgeo_9925/
LOCATION:Science Center 507\, 1 Oxford Street\, Cambridge\, 02138
CATEGORIES:Geometry and Quantum Theory Seminar
ATTACH;FMTTYPE=image/png:https://cmsa.fas.harvard.edu/media/CMSA-Geometry-Quantum-Theory-9.9.25-scaled.png
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/New_York:20250911T090000
DTEND;TZID=America/New_York:20250912T170000
DTSTAMP:20260417T185739
CREATED:20250502T175902Z
LAST-MODIFIED:20251026T044243Z
UID:10003743-1757581200-1757696400@cmsa.fas.harvard.edu
SUMMARY:Big Data Conference 2025
DESCRIPTION:Big Data Conference 2025 \nDates: Sep. 11–12\, 2025 \nLocation: Harvard University CMSA\, 20 Garden Street\, Cambridge & via Zoom \nThe Big Data Conference features speakers from the Harvard community as well as scholars from across the globe\, with talks focusing on computer science\, statistics\, math and physics\, and economics. \nInvited Speakers \n\nMarkus J. Buehler\, MIT\nYiling Chen\, Harvard\nJordan Ellenberg\, UW Madison\nYue M. Lu\, Harvard\nPankaj Mehta\, BU\nNick Patterson\, Harvard\nGautam Reddy\, Princeton\nTrevor David Rhone\, Rensselaer Polytechnic Institute\nTess Smidt\, MIT\n\nOrganizers: \nMichael M. Desai\, Harvard OEB |  Michael R. Douglas\, Harvard CMSA | Yannai A. Gonczarowski\, Harvard Economics | Efthimios Kaxiras\, Harvard Physics | Melanie Weber\, Harvard SEAS \n  \nBig Data Youtube Playlist \n  \nSchedule \nThursday\, Sep. 11\, 2025 \n  \n\n\n\n9:00 am\nRefreshments\n\n\n9:30 am\nIntroductions\n\n\n9:45–10:45 am\nGautam Reddy\, Princeton \nTitle: Global epistasis in genotype-phenotype maps\n\n\n10:45–11:00 am\nBreak\n\n\n11:00 am –12:00 pm\nNick Patterson\, Harvard \nTitle: The Origin of the Indo-Europeans \nAbstract: Indo-European is the largest family of human languages\, with very wide geographical distribution and more than 3 billion native speakers. How did this family arise and spread? This question has been discussed for nearly 250 years but with the advent of the availability of DNA from ancient fossils is now largely understood\, at least in broad outlines. We will describe what we now know about the origins.\n\n\n12:00–1:30 pm\nLunch break\n\n\n1:30–2:30 pm\nMarkus Buehler\, MIT \nTitle: Superintelligence for scientific discovery \nAbstract: AI is moving beyond prediction to become a partner in invention. While today’s models excel at interpolating within known data\, true discovery requires stepping outside existing truths. This talk introduces superintelligent discovery engines built on multi-agent swarms: diverse AI agents that interact\, compete\, and cooperate to generate structured novelty. Guided by Gödel’s insight that no closed system is complete\, these swarms create gradients of difference – much like temperature gradients in thermodynamics – that sustain flow\, invention\, and surprise. Case studies in protein design and music composition show how swarms escape data biases\, invent novel structures\, and weave long-range coherence\, producing creativity that rivals human processes. By moving from “big data” to “big insight”\, these systems point toward a new era of AI that composes knowledge across science\, engineering\, and the arts.\n\n\n2:30–2:45 pm\nBreak\n\n\n2:45–3:45 pm\nJordan Ellenberg\, UW Madison \nTitle: What does machine learning have to offer mathematics?\n\n\n3:45–4:00 pm\nBreak\n\n\n4:00–5:00 pm\nPankaj Mehta\, Boston University \nTitle: Thinking about high-dimensional biological data in the age of AI \nAbstract: The molecular biology revolution has transformed our view of living systems. Scientific explanations of biological phenomena are now synonymous with the identification of the genes and proteins. The preeminence of the molecular paradigm has only become more pronounced as new technologies allow us to make measurements at scale. Combining this wealth of data with new artificial intelligence (AI) techniques is widely viewed as the future of biology. Here\, I will discuss the promise and perils of this approach. I will focus on our unpublished work with collaborators on two fronts: (i) transformer-based models for understanding genotype-to-phenotype maps\, and (ii) LLM-based ‘foundational models’ for cellular identity\, such as TranscriptFormer\, which is trained on single-cell RNA sequencing (scRNAseq) data. While LLMs excel at capturing complex evolutionary and demographic structure in DNA sequence data\, they are much less adept at elucidating the biology of cellular identity. We show that simple parameter-free models based on linear-algebra outperform TranscriptFormer on downstream tasks related to cellular identity\, even though TranscriptFormer has nearly a billion parameters. If time permits\, I will conclude by showing how we can combine ideas from linear algebra\, bifurcation theory\, and statistical physics to classify cell fate transitions using scRNAseq data.\n\n\n\n  \nFriday\, Sep. 12\, 2025  \n\n\n\n9:00-9:45 am\nRefreshments\n\n\n9:45–10:45 am\nYiling Chen\, Harvard \nTitle: Data Reliability Scoring \nAbstract: Imagine you are trying to make a data-driven decision\, but the data at hand may be noisy\, biased\, or even strategically manipulated. Can you assess whether such a dataset is reliable—without access to ground truth?\nWe initiate the study of reliability scoring for datasets reported by potentially strategic data sources. While the true data remain unobservable\, we assume access to auxiliary observations generated by an unknown statistical process that depends on the truth. We introduce the Gram Determinant Score\, a reliability measure that evaluates how well the reported data align with the unobserved truth\, using only the reported data and the auxiliary observations. The score comes with provable guarantees: it preserves several natural reliability orderings. Experimentally\, it effectively captures data quality in settings with synthetic noise and contrastive learning embeddings.\nThis talk is based on joint work with Shi Feng\, Fang-Yi Yu\, and Paul Kattuman.\n\n\n10:45–11:00 am\nBreak\n\n\n11:00 am –12:00 pm\nYue M. Lu\, Harvard \nTitle: Nonlinear Random Matrices in High-Dimensional Estimation and Learning \nAbstract: In recent years\, new classes of structured random matrices have emerged in statistical estimation and machine learning. Understanding their spectral properties has become increasingly important\, as these matrices are closely linked to key quantities such as the training and generalization performance of large neural networks and the fundamental limits of high-dimensional signal recovery. Unlike classical random matrix ensembles\, these new matrices often involve nonlinear transformations\, introducing additional structural dependencies that pose challenges for traditional analysis techniques. \nIn this talk\, I will present a set of equivalence principles that establish asymptotic connections between various nonlinear random matrix ensembles and simpler linear models that are more tractable for analysis. I will then demonstrate how these principles can be applied to characterize the performance of kernel methods and random feature models across different scaling regimes and to provide insights into the in-context learning capabilities of attention-based Transformer networks.\n\n\n12:00–1:30 pm\nLunch break\n\n\n1:30–2:30 pm\nTrevor David Rhone\, Rensselaer Polytechnic Institute \nTitle: Accelerating the discovery of van der Waals quantum materials using AI \nAbstract: van der Waals (vdW) materials are exciting platforms for studying emergent quantum phenomena\, ranging from long-range magnetic order to topological order. A conservative estimate for the number of candidate vdW materials exceeds ~106 for monolayers and ~1012 for heterostructures. How can we accelerate the exploration of this entire space of materials? Can we design quantum materials with desirable properties\, thereby advancing innovation in science and technology? A recent study showed that artificial intelligence (AI) can be harnessed to discover new vdW Heisenberg ferromagnets based on Cr2Ge2Te6 [1]\, [2] and magnetic vdW topological insulators based on MnBi2Te4 [3]. In this talk\, we will harness AI to efficiently explore the large chemical space of vdW materials and to guide the discovery of vdW materials with desirable spin and charge properties. We will focus on crystal structures based on monolayer Cr2I6 of the form A2X6\, which are studied using density functional theory (DFT) calculations and AI. Magnetic properties\, such as the magnetic moment are determined. The formation energy is also calculated and used as a proxy for the chemical stability. We also investigate monolayers based on MnBi2Te4 of the form AB2X4 to identify novel topological materials. Further to this\, we study heterostructures based on MnBi2Te4/Sb2Te3 stacks. We show that AI\, combined with DFT\, can provide a computationally efficient means to predict the thermodynamic and magnetic properties of vdW materials [4]\,[5]. This study paves the way for the rapid discovery of chemically stable vdW quantum materials with applications in spintronics\, magnetic memory and novel quantum computing architectures.\n[1]        T. D. Rhone et al.\, “Data-driven studies of magnetic two-dimensional materials\,” Sci. Rep.\, vol. 10\, no. 1\, p. 15795\, 2020.\n[2]        Y. Xie\, G. Tritsaris\, O. Granas\, and T. Rhone\, “Data-Driven Studies of the Magnetic Anisotropy of Two-Dimensional Magnetic Materials\,” J. Phys. Chem. Lett.\, vol. 12\, no. 50\, pp. 12048–12054.\n[3]        R. Bhattarai\, P. Minch\, and T. D. Rhone\, “Investigating magnetic van der Waals materials using data-driven approaches\,” J. Mater. Chem. C\, vol. 11\, p. 5601\, 2023.\n[4]        T. D. Rhone et al.\, “Artificial Intelligence Guided Studies of van der Waals Magnets\,” Adv. Theory Simulations\, vol. 6\, no. 6\, p. 2300019\, 2023.\n[5]        P. Minch\, R. Bhattarai\, K. Choudhary\, and T. D. Rhone\, “Predicting magnetic properties of van der Waals magnets using graph neural networks\,” Phys. Rev. Mater.\, vol. 8\, no. 11\, p. 114002\, Nov. 2024.\nThis work used the Extreme Science and Engineering Discovery Environment (XSEDE)\, which is supported by National Science Foundation Grant No. ACI-1548562. This research used resources of the Argonne Leadership Computing Facility\, which is a DOE Office of Science User Facility supported under Contract No. DE-AC02-06CH11357. This material is based on work supported by the National Science Foundation CAREER award under Grant No. 2044842.\n\n\n2:30–2:45 pm\nBreak\n\n\n2:45–3:45 pm\nTess Smidt\, MIT \nTitle: Applications of Euclidean neural networks to understand and design atomistic systems \nAbstract: Atomic systems (molecules\, crystals\, proteins\, etc.) are naturally represented by a set of coordinates in 3D space labeled by atom type. This poses a challenge for machine learning due to the sensitivity of coordinates to 3D rotations\, translations\, and inversions (the symmetries of 3D Euclidean space). Euclidean symmetry-equivariant Neural Networks (E(3)NNs) are specifically designed to address this issue. They faithfully capture the symmetries of physical systems\, handle 3D geometry\, and operate on the scalar\, vector\, and tensor fields that characterize these systems. \nE(3)NNs have achieved state-of-the-art results across atomistic benchmarks\, including small-molecule property prediction\, protein-ligand binding\, force prediciton for crystals\, molecules\, and heterogeneous catalysis. By merging neural network design with group representation theory\, they provide a principled way to embed physical symmetries directly into learning. In this talk\, I will survey recent applications of E(3)NNs to materials design and highlight ongoing debates in the AI for atomistic sciences community: how to balance the incorporation of physical knowledge with the drive for engineering efficiency.\n\n\n\n 
URL:https://cmsa.fas.harvard.edu/event/bigdata_2025/
LOCATION:CMSA Room G10\, CMSA\, 20 Garden Street\, Cambridge\, MA\, 02138\, United States
CATEGORIES:Big Data Conference,Conference,Event
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END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/New_York:20250915T090000
DTEND;TZID=America/New_York:20250918T170000
DTSTAMP:20260417T185739
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
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END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/New_York:20250915T150000
DTEND;TZID=America/New_York:20250915T160000
DTSTAMP:20260417T185739
CREATED:20250910T193835Z
LAST-MODIFIED:20250910T194841Z
UID:10003788-1757948400-1757952000@cmsa.fas.harvard.edu
SUMMARY:Orientifolds for F-theory on K3 Surfaces
DESCRIPTION:Quantum Field Theory and Physical Mathematics Seminar \nSpeaker: Chuck Doran (Alberta/CMSA) \nTitle: Orientifolds for F-theory on K3 Surfaces \nAbstract: Compactification of F-theory on an elliptically fibered K3 surface provides a framework to encode type IIB string theory on elliptic curves\, with the Kaehler modulus of the elliptic curve encoded in the complex structure of the elliptic fibers. In work with Malmendier\, Mendez-Diez\, and Rosenberg we extend that perspective by examining F-theory orientifolds on elliptically fibered K3 surfaces and connecting them to D-brane classifications using real K-theory (KR-theory).  The real structures—antiholomorphic involutions—on our K3 surfaces connect the geometry with the physics\, providing a natural setting for understanding the interplay between elliptic fibration structures and D-brane classifications in F-theory. We construct Real normal forms with their associated antiholomorphic involutions and use this to make explicit the 2-torsion Brauer twist that relates our normal forms to the Jacobian (Weierstrass normal form) elliptic fibration\, including the realization of a representative for the twisting class as an Azumaya algebra. This all connects back to the physics by considering three families of real K3 surfaces whose string limits give the three different type IIB theories on P1 with four type I_0^∗ Kodaira fibers.
URL:https://cmsa.fas.harvard.edu/event/qft_91525/
LOCATION:CMSA Room G10\, CMSA\, 20 Garden Street\, Cambridge\, MA\, 02138\, United States
CATEGORIES:Quantum Field Theory and Physical Mathematics
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END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/New_York:20250915T163000
DTEND;TZID=America/New_York:20250915T173000
DTSTAMP:20260417T185739
CREATED:20250904T152315Z
LAST-MODIFIED:20250904T152759Z
UID:10003776-1757953800-1757957400@cmsa.fas.harvard.edu
SUMMARY:Topological Manifolds – The First 100 Years
DESCRIPTION:Colloquium \nSpeaker: Michael Freedman (Harvard CMSA and Logical Intelligence) \nTitle: Topological Manifolds – The First 100 Years \nAbstract: I’ll review manifold topology in the topological category from its start with work of Rado (1925) and Kneser (1926) to the present. Work of Moise\, Mazur\, Kirby\, Siebenmann\, Sullivan\, Kruskal\, and the speaker will be discussed. In my view there is one pressing open question (the A-B slice problem). I will end with some thoughts on putting an AI to work on it. \n  \n 
URL:https://cmsa.fas.harvard.edu/event/colloquium-91525/
LOCATION:CMSA Room G10\, CMSA\, 20 Garden Street\, Cambridge\, MA\, 02138\, United States
CATEGORIES:Colloquium
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END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/New_York:20250916T170000
DTEND;TZID=America/New_York:20250916T180000
DTSTAMP:20260417T185739
CREATED:20250807T142820Z
LAST-MODIFIED:20250922T134159Z
UID:10003760-1758042000-1758045600@cmsa.fas.harvard.edu
SUMMARY:Geometry of Machine Learning Special Lecture: Yann LeCun
DESCRIPTION:Geometry of Machine Learning Special Lecture: Yann LeCun \nTitle: Self-Supervised Learning\, JEPA\, World Models\, and the future of AI \nDate: Tuesday\, Sep. 16\, 2025 \nTime: 5:00 pm ET \nLocation: Harvard Science Center\, Hall C & via Zoom Webinar
URL:https://cmsa.fas.harvard.edu/event/lecun91625/
LOCATION:Harvard Science Center\, 1 Oxford Street\, Cambridge\, MA\, 02138
CATEGORIES:Special Lectures
ATTACH;FMTTYPE=image/jpeg:https://cmsa.fas.harvard.edu/media/YannLeCun_GML-scaled.jpg
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/New_York:20250917T170000
DTEND;TZID=America/New_York:20250917T180000
DTSTAMP:20260417T185739
CREATED:20250311T134916Z
LAST-MODIFIED:20251010T115024Z
UID:10003656-1758128400-1758132000@cmsa.fas.harvard.edu
SUMMARY:Millennium Prize Problems Lecture - Michael Freedman: The Poincaré Conjecture and Mathematical Discovery  
DESCRIPTION:Millennium Prize Problems Lecture\nDate: September 17\, 2025 \nLocation: Harvard Science Center Hall D & via Zoom Webinar \nTime: 5:00–6:00 pm \nSpeaker: Michael Freedman\, Harvard CMSA and Logical Intelligence  \nTitle: The Poincaré Conjecture and Mathematical Discovery   \nAbstract: The AI age requires us to re-examine what mathematics is about. The Seven Millenium Problems provide an ideal lens for doing so. Five of the seven are core mathematical questions\, two are meta-mathematical – asking about the scope of mathematics. The Poincare conjecture represents one of the core subjects\, manifold topology. I’ll explain what it is about\, its broader context\, and why people cared so much about finding a solution\, which ultimately arrived through the work of R. Hamilton and G. Perelman. Although stated in manifold topology\, the proof requires vast developments in the theory of parabolic partial differential equations\, some of which I will sketch. Like most powerful techniques\, the methods survive their original objectives and are now deployed widely in both three- and four-dimensional manifold topology.  \n  \nRead more about the Poincaré Conjecture at the Clay Math website. \nOrganizers: Martin Bridson\, Clay Mathematics Institute | Dan Freed\, Harvard University and CMSA | Mike Hopkins\, Harvard University \n\n                   \n\nMillennium Prize Problems Lecture Series
URL:https://cmsa.fas.harvard.edu/event/clay_91725/
LOCATION:Harvard Science Center Hall D\, 1 Oxford Street\, Cambridge\, MA\, 02138\, United States
CATEGORIES:Millennium Prize Problems Lecture,Special Lectures
ATTACH;FMTTYPE=image/jpeg:https://cmsa.fas.harvard.edu/media/Freedman_web_ad.jpg
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/New_York:20250918T160000
DTEND;TZID=America/New_York:20250918T170000
DTSTAMP:20260417T185739
CREATED:20250904T162209Z
LAST-MODIFIED:20250910T174655Z
UID:10003777-1758211200-1758214800@cmsa.fas.harvard.edu
SUMMARY:Moduli spaces of 4d N=2 quantum field theories
DESCRIPTION:Differential Geometry and Physics Seminar  \nSpeaker: Robert Moscrop\, CMSA \nTitle: Moduli spaces of 4d N=2 quantum field theories \nAbstract: Supersymmetry endows quantum field theories with several rich algebraic and geometric structures associated to their moduli space of vacua\, providing powerful tools to study such theories non-perturbatively. For example\, in four-dimensional theories with eight supercharges\, the low energy dynamics of the theory is captured by an algebraic completely integrable system whose base is the Coulomb branch– a particular distinguished submanifold of the moduli space. This structure is so tightly constrained\, that there is an ongoing program to classify such theories purely by understanding their Coulomb branch geometry. In this talk\, I will give a gentle introduction to the geometry of the moduli spaces of 4d N=2 theories and\, time permitting\, discuss some recent results showcasing how the geometry of the Coulomb branch can be used to constrain certain physical quantities of the theory. \n  \n  \n 
URL:https://cmsa.fas.harvard.edu/event/dgphys_91825/
LOCATION:CMSA Room G10\, CMSA\, 20 Garden Street\, Cambridge\, MA\, 02138\, United States
CATEGORIES:Differential Geometry and Physics Seminar
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END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/New_York:20250919T120000
DTEND;TZID=America/New_York:20250919T130000
DTSTAMP:20260417T185739
CREATED:20241211T195345Z
LAST-MODIFIED:20250918T184123Z
UID:10003648-1758283200-1758286800@cmsa.fas.harvard.edu
SUMMARY:Top-Down Perspectives on Symmetry Theories
DESCRIPTION:Member Seminar \nSpeaker: Max Hubner \nTitle: Top-Down Perspectives on Symmetry Theories \nAbstract: I will review the construction and utility of symmetry theories for string constructed quantum field theories. Symmetry theories are extra-dimensional auxiliary theories separating aspects of a quantum field theory’s symmetries from many of its more messy features. For QFTs with extra-dimensional string constructions the symmetry theory derives directly from the extra-dimensional geometry. This perspective allows for the study of symmetries of famously string engineered systems\, such as SCFTs in 5D and 6D\, which we will discuss on an example by example basis.
URL:https://cmsa.fas.harvard.edu/event/member-seminar-91925/
LOCATION:Common Room\, CMSA\, 20 Garden Street\, Cambridge\, MA\, 02138\, United States
CATEGORIES:Member Seminar
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END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/New_York:20250922T150000
DTEND;TZID=America/New_York:20250922T160000
DTSTAMP:20260417T185739
CREATED:20250826T190916Z
LAST-MODIFIED:20250917T134457Z
UID:10003761-1758553200-1758556800@cmsa.fas.harvard.edu
SUMMARY:Non-Supersymmetric Orbifolds\, Quivers and Chen-Ruan Orbifold Cohomology
DESCRIPTION:Quantum Field Theory and Physical Mathematics Seminar \nSpeaker: Max Hübner (Uppsala & CMSA) \nTitle: Non-Supersymmetric Orbifolds\, Quivers and Chen-Ruan Orbifold Cohomology \nAbstract: We consider D3-brane probes of non-supersymmetric orbifolds and IIA on the same class of non-supersymmetric orbifolds. Both setups are characterized\, in part\, by quivers (which in the latter case relate for example to D0-brane probes) from which symmetries constraining the scale-dependence and tachyonic instabilities of the two systems\, respectively\, can be derived. We demonstrate that these considerations can be matched via a geometric analysis of the asymptotic boundary of the relevant orbifolds\, in all cases\, via considerations centered on Chen-Ruan orbifold cohomology.
URL:https://cmsa.fas.harvard.edu/event/qft_92225/
LOCATION:CMSA Room G10\, CMSA\, 20 Garden Street\, Cambridge\, MA\, 02138\, United States
CATEGORIES:Quantum Field Theory and Physical Mathematics
ATTACH;FMTTYPE=image/png:https://cmsa.fas.harvard.edu/media/CMSA-QFT-and-Physical-Mathematics-9.22.25.png
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/New_York:20250922T163000
DTEND;TZID=America/New_York:20250922T173000
DTSTAMP:20260417T185739
CREATED:20250826T191126Z
LAST-MODIFIED:20250914T170550Z
UID:10003732-1758558600-1758562200@cmsa.fas.harvard.edu
SUMMARY:Turbulent Mixing and Antagonistic Microorganisms
DESCRIPTION:Colloquium \nSpeaker: David Nelson\, Harvard \nTitle: Turbulent Mixing and Antagonistic Microorganisms \nAbstract: Unlike coffee and cream that homogenize when stirred\, growing micro-organisms (e.g.\, bacteria and baker’s yeast) can actively kill each other and avoid mixing.  How do such antagonistic interactions impact the growth and survival of competing strains\, while being spatially advected by turbulent flows?  By using analytic arguments and numerical simulations of a continuum model\, we describe the dynamics of two antagonistic strains that are dispersed by both compressible and incompressible turbulent flows in two spatial dimensions.  A key parameter is the ratio of the fluid transport time to that of biological reproduction\, which determines the winning organism that ultimately takes over the whole population from an initial heterogeneous state\, a process known as fixation.  By quantifying the probability and mean time for fixation\, we discuss how turbulence raises the threshold for biological nucleation and antagonism suppresses flow-induced mixing by depleting the population at interfaces. We highlight the unusual biological consequences of the interplay of turbulent fluid flows with antagonistic population dynamics\, with potential implications for marine microbial ecology and origins of biological chirality.
URL:https://cmsa.fas.harvard.edu/event/colloquium_92225/
LOCATION:CMSA Room G10\, CMSA\, 20 Garden Street\, Cambridge\, MA\, 02138\, United States
CATEGORIES:Colloquium
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END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/New_York:20250925T160000
DTEND;TZID=America/New_York:20250925T170000
DTSTAMP:20260417T185739
CREATED:20250826T192430Z
LAST-MODIFIED:20250919T142937Z
UID:10003762-1758816000-1758819600@cmsa.fas.harvard.edu
SUMMARY:Degeneration of Calabi-Yau 3-folds and 3-forms
DESCRIPTION:Differential Geometry and Physics Seminar  \nSpeaker: Teng Fei\, Rutgers \nTitle: Degeneration of Calabi-Yau 3-folds and 3-forms \nAbstract: We study the geometries associated to various 3-forms on a symplectic 6-manifold of different orbital types. As an application\, we demonstrate how this can be used to find Lagrangian foliations and other geometric structures of interest arising from certain degeneration of Calabi-Yau 3-folds. \n 
URL:https://cmsa.fas.harvard.edu/event/dgphys_92525/
LOCATION:CMSA Room G10\, CMSA\, 20 Garden Street\, Cambridge\, MA\, 02138\, United States
CATEGORIES:Differential Geometry and Physics Seminar
ATTACH;FMTTYPE=image/png:https://cmsa.fas.harvard.edu/media/DG-Physics-Seminar-9.25.2025.png
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/New_York:20250926T120000
DTEND;TZID=America/New_York:20250926T130000
DTSTAMP:20260417T185739
CREATED:20250826T193028Z
LAST-MODIFIED:20250918T172135Z
UID:10003763-1758888000-1758891600@cmsa.fas.harvard.edu
SUMMARY:Sections of fibrations onto curves in characteristic p>0
DESCRIPTION:Member Seminar \nSpeaker: Iacopo Brivio \nTitle: Sections of fibrations onto curves in characteristic p>0 \nAbstract: This talk is based on joint work in progress with Ben Church. Using symplectic geometry\, Pieloch showed that every smooth fibration $f\colon X\to \mathbb{P}^1$ of complex projective varieties always admits a section. I will explain how this theorem can be recovered using techniques from Hodge theory and the Minimal Model Program. An advantage of this approach is that it allows for a positive characteristic generalization\, by replacing the Hodge theoretic input by a crystalline one. I will also give an example showing that Pieloch’s result can fail in characteristic p>0.
URL:https://cmsa.fas.harvard.edu/event/member-seminar-92625/
LOCATION:Common Room\, CMSA\, 20 Garden Street\, Cambridge\, MA\, 02138\, United States
CATEGORIES:Member Seminar
ATTACH;FMTTYPE=image/png:https://cmsa.fas.harvard.edu/media/CMSA-Member-Seminar-9.26.25-1.png
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/New_York:20250929T150000
DTEND;TZID=America/New_York:20250929T160000
DTSTAMP:20260417T185739
CREATED:20250924T181258Z
LAST-MODIFIED:20250924T183325Z
UID:10003795-1759158000-1759161600@cmsa.fas.harvard.edu
SUMMARY:Graph integrals on Kahler manifolds
DESCRIPTION:Quantum Field Theory and Physical Mathematics Seminar \nSpeaker: Minghao Wang\, Boston University \nTitle: Graph integrals on Kahler manifolds \nAbstract: I will talk about my recent work with Junrong Yan. We proved the convergence of Graph integrals on analytic Kahler manifolds in the sense of Cauchy principal values\, which are originally from holomorphic quantum field theories. In particular\, this allows us to construct geometric invariants of Calabi-Yau metrics. I will also talk about some potential applications of our results. References: arXiv:2507.09170\, arXiv:2401.08113
URL:https://cmsa.fas.harvard.edu/event/qft_92925/
LOCATION:CMSA Room G10\, CMSA\, 20 Garden Street\, Cambridge\, MA\, 02138\, United States
CATEGORIES:Quantum Field Theory and Physical Mathematics
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END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/New_York:20250930T161500
DTEND;TZID=America/New_York:20250930T183000
DTSTAMP:20260417T185739
CREATED:20250829T204925Z
LAST-MODIFIED:20250929T175811Z
UID:10003775-1759248900-1759257000@cmsa.fas.harvard.edu
SUMMARY:Geometry and Quantum Theory Seminar
DESCRIPTION:Geometry and Quantum Theory Seminar \nSpeaker 1: Max Hubner\, CMSA \nTitle: On Topological Structures in String Theory \nAbstract: Geometric engineering constructions in string theory often realize QFTs relative to an extra-dimensional geometry. This perspective parallels the symmetry TFT construction where a QFT is presented relative to its extra-dimensional symmetry quiche. Unsurprisingly\, as we will discuss\, these constructions are related. Topological features of the extra-dimensional geometry map onto the symmetry TFT. We discuss examples and generalization beyond purely geometric constructions in string theory. \nSpeaker 2: Bowen Yang\, CMSA \nTitle: Bounded L theory \nAbstract: Bounded L-groups arise in the intersection of algebraic L-theory and large-scale geometry\, providing a framework for quadratic forms and automorphisms subject to uniform control conditions. These groups play a role in topology and surgery theory\, especially in contexts where one needs to measure obstructions not just algebraically but also geometrically\, with bounds on propagation or support. In this talk I will give a gentle introduction to the basic definitions\, explain how bounded L-groups differ from classical L-groups\, and outline an application to quantum many body invariants.
URL:https://cmsa.fas.harvard.edu/event/quantumgeo_93025/
LOCATION:Science Center 507\, 1 Oxford Street\, Cambridge\, 02138
CATEGORIES:Geometry and Quantum Theory Seminar
ATTACH;FMTTYPE=image/png:https://cmsa.fas.harvard.edu/media/CMSA-Geometry-Quantum-Theory-9.30.25.png
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/New_York:20251001T140000
DTEND;TZID=America/New_York:20251001T150000
DTSTAMP:20260417T185739
CREATED:20250128T214901Z
LAST-MODIFIED:20251002T140605Z
UID:10003710-1759327200-1759330800@cmsa.fas.harvard.edu
SUMMARY:Tropicalized quantum field theory
DESCRIPTION:New Technologies in Mathematics Seminar \nSpeaker: Michael Borinsky\, Perimeter Institute  \nTitle: Tropicalized quantum field theory \nAbstract: Quantum field theory (QFT) is one of the most accurate methods for making phenomenological predictions in physics\, but it has a significant drawback: obtaining concrete predictions from it is computationally very demanding. The standard perturbative approach expands an interacting QFT around a free QFT\, using Feynman diagrams. However\, the number of these diagrams grows superexponentially\, making the approach quickly infeasible. \nI will talk about arXiv:2508.14263\, which introduces an intermediate layer between free and interacting field theories: a tropicalized QFT. Often\, this tropicalized QFT can be solved exactly. The exact solution manifests as a non-linear recursion equation fulfilled by the expansion coefficients of the quantum effective action. Geometrically\, this recursion computes volumes of moduli spaces of metric graphs and is thereby analogous to Mirzakhani’s volume recursions on the moduli space of curves. Building on this exact solution\, an algorithm can be constructed that samples points from the moduli space of graphs approximately proportional to their perturbative contribution. Via a standard Monte Carlo approach we can evaluate the original QFT using this algorithm. Remarkably\, this algorithm requires only polynomial time and memory\, suggesting that perturbative quantum field theory computations actually lie in the polynomial-time complexity class\, while all known algorithms for evaluating individual Feynman integrals are at least exponential in time and memory. The (potential) capabilities of this approach are remarkable: For instance\, we can compute perturbative expansions of massive scalar D=3 phi^3 and D=4 phi^4 quantum field theories up to loop orders between 20 and 50 using a basic proof-of-concept implementation. These perturbative orders are completely inaccessible using a naive approach.
URL:https://cmsa.fas.harvard.edu/event/newtech_10125/
LOCATION:Virtual
CATEGORIES:New Technologies in Mathematics Seminar
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END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/New_York:20251002T160000
DTEND;TZID=America/New_York:20251002T170000
DTSTAMP:20260417T185739
CREATED:20250904T162108Z
LAST-MODIFIED:20250926T180606Z
UID:10003778-1759420800-1759424400@cmsa.fas.harvard.edu
SUMMARY:Special Kähler geometry and collapsing
DESCRIPTION:Differential Geometry and Physics Seminar  \nSpeaker: Valentino Tosatti\, NYU Courant Institute \nTitle: Special Kähler geometry and collapsing \nAbstract: Special Kähler geometry was first discovered in the context of N=2 supersymmetric 4D gauge theories\, and it also plays a prominent role in mirror symmetry. A key observation of Donagi-Witten and Freed is that the base of every algebraic integrable system admits a special Kähler metric\, while the total space admits a hyperkähler metric. In this talk I will consider compact hyperkähler manifolds with a an algebraic integrable system (i.e. a holomorphic Lagrangian torus fibration)\, and consider a family of hyperkähler metrics such that the volume of the torus fibers shrinks to zero. I will explain how the hyperkähler metrics must collapse to a special Kähler metric on the base (away from the discriminant locus)\, and what we can say about the metric completion of the limit. \n 
URL:https://cmsa.fas.harvard.edu/event/dgphys_10225/
LOCATION:CMSA Room G10\, CMSA\, 20 Garden Street\, Cambridge\, MA\, 02138\, United States
CATEGORIES:Differential Geometry and Physics Seminar
ATTACH;FMTTYPE=image/png:https://cmsa.fas.harvard.edu/media/DG-Physics-Seminar-10.2.2025.png
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/New_York:20251003T120000
DTEND;TZID=America/New_York:20251003T130000
DTSTAMP:20260417T185739
CREATED:20250827T140756Z
LAST-MODIFIED:20250918T171806Z
UID:10003764-1759492800-1759496400@cmsa.fas.harvard.edu
SUMMARY:Local Donaldson-Scaduto conjecture
DESCRIPTION:Member Seminar \nSpeaker: Saman Habibi Esfahani \nTitle: Local Donaldson-Scaduto conjecture \nAbstract: This talk is based on joint works with Gora Bera and Yang Li. Motivated by collapsing Calabi-Yau 3-folds and G2-manifolds with Lefschetz K3 fibrations in the adiabatic setting\, Donaldson and Scaduto conjectured the existence and uniqueness of a special Lagrangian pair-of-pants in the Calabi-Yau 3-fold $ X \times \mathbb{C}$\, where $X$ is either a hyperkähler K3 surface (global version) or an A2-type ALE hyperkähler 4-manifold (local version). After a brief introduction to the subject\, we discuss the significance of this conjecture in the study of Calabi-Yau 3-folds and G2-manifolds\, and then prove the local version of the conjecture. \n 
URL:https://cmsa.fas.harvard.edu/event/member-seminar-10325/
LOCATION:Common Room\, CMSA\, 20 Garden Street\, Cambridge\, MA\, 02138\, United States
CATEGORIES:Member Seminar
ATTACH;FMTTYPE=image/png:https://cmsa.fas.harvard.edu/media/CMSA-Member-Seminar-10.3.25.png
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/New_York:20251006T090000
DTEND;TZID=America/New_York:20251010T170000
DTSTAMP:20260417T185739
CREATED:20250502T180256Z
LAST-MODIFIED:20251009T201426Z
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  \nRegistration required \nIn-person registration (This event is at capacity) \nZoom Webinar Registration \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:20251006T150000
DTEND;TZID=America/New_York:20251006T160000
DTSTAMP:20260417T185739
CREATED:20250924T182709Z
LAST-MODIFIED:20251006T144221Z
UID:10003796-1759762800-1759766400@cmsa.fas.harvard.edu
SUMMARY:Non-perturbative aspects of self-dual gauge theory
DESCRIPTION:Quantum Field Theory and Physical Mathematics Seminar \nSpeaker: Kevin Costello (Perimeter Institute)\n\nTitle: Non-perturbative aspects of self-dual gauge theory\n\nAbstract: Self-dual gauge theory is conformal in perturbation theory\, but has a non-trivial beta-function when instanton effects are included. I will give two computations of this beta-function\, one based on the Grothendieck-Riemann-Roch formula and one using holography in the topological string.   This leads to two new ways to compute the standard QCD beta-function at one loop\, without using Feynman diagrams.  If time permits\, I will also discuss how instantons effect scattering amplitudes.\n\n 
URL:https://cmsa.fas.harvard.edu/event/qft_100625/
LOCATION:Virtual
CATEGORIES:Quantum Field Theory and Physical Mathematics
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END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/New_York:20251006T163000
DTEND;TZID=America/New_York:20251006T173000
DTSTAMP:20260417T185739
CREATED:20250914T165359Z
LAST-MODIFIED:20250914T165941Z
UID:10003794-1759768200-1759771800@cmsa.fas.harvard.edu
SUMMARY:Geometry of dimer models
DESCRIPTION:Colloquium \nSpeaker: Alexei Borodin\, MIT \nTitle: Geometry of dimer models \nAbstract: Random dimer coverings of large planar graphs are known to exhibit unusual and visually apparent asymptotic phenomena that include formation of frozen regions and various phases in the unfrozen ones. For a specific family of subgraphs of the (periodically weighted) square lattice known as the Aztec diamonds\, the asymptotic behavior of dimers admits a precise description in terms of geometry of underlying Riemann surfaces. The goal of the talk is to explain how the surface structure manifests itself through the statistics of dimers. Based on joint works with T. Berggren and M. Duits. \n 
URL:https://cmsa.fas.harvard.edu/event/colloquium_10625/
LOCATION:CMSA Room G10\, CMSA\, 20 Garden Street\, Cambridge\, MA\, 02138\, United States
CATEGORIES:Colloquium
ATTACH;FMTTYPE=image/png:https://cmsa.fas.harvard.edu/media/CMSA-Colloquium-10.6.2025.png
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BEGIN:VEVENT
DTSTART;TZID=America/New_York:20251007T161500
DTEND;TZID=America/New_York:20251007T183000
DTSTAMP:20260417T185739
CREATED:20251001T183038Z
LAST-MODIFIED:20251007T132737Z
UID:10003802-1759853700-1759861800@cmsa.fas.harvard.edu
SUMMARY:A Classifying Space for Phases of Matrix Product States
DESCRIPTION:Geometry and Quantum Theory Seminar \nSpeakers: Daniel Spiegel\, Harvard Math \nTitle: A Classifying Space for Phases of Matrix Product States \nAbstract: Alexei Kitaev has conjectured that there should be a loop spectrum consisting of spaces of gapped invertible quantum spin systems\, indexed by spatial dimension d of the lattice. Motivated by Kitaev’s conjecture\, I will detail a concrete construction of a topological space B consisting of translation invariant injective matrix product states (MPS) of all physical and bond dimensions\, which plays the role Kitaev’s space in dimension d = 1. Having such a space is a useful tool in the discussion of parametrized phases of MPS; in fact it allows us to define a parametrized phase as a homotopy class of maps into B. The space B is constructed as the quotient of a contractible space E of MPS tensors modulo gauge transformations. The projection map from E to B is a quasifibration\, from which we can compute the homotopy groups of the classifying space B by a long exact sequence. In particular\, B has the weak homotopy type K(Z\, 2) x K(Z\, 3)\, shedding light on Kitaev’s conjecture in the context of MPS. \nDaniel Spiegel will speak for 60 minutes. \nSunghyuk Park  (CMSA) will also speak for 15 minutes
URL:https://cmsa.fas.harvard.edu/event/quantumgeo_10725/
LOCATION:Science Center 507\, 1 Oxford Street\, Cambridge\, 02138
CATEGORIES:Geometry and Quantum Theory Seminar
ATTACH;FMTTYPE=image/png:https://cmsa.fas.harvard.edu/media/CMSA-Geometry-Quantum-Theory-10.7.25-scaled.png
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BEGIN:VEVENT
DTSTART;TZID=America/New_York:20251008T140000
DTEND;TZID=America/New_York:20251008T150000
DTSTAMP:20260417T185739
CREATED:20250930T181425Z
LAST-MODIFIED:20251009T195959Z
UID:10003801-1759932000-1759935600@cmsa.fas.harvard.edu
SUMMARY:Understanding Optimization in Deep Learning with Central Flows
DESCRIPTION:New Technologies in Mathematics Seminar \nSpeaker: Alex Damian\, Harvard \nTitle: Understanding Optimization in Deep Learning with Central Flows \nAbstract: Traditional theories of optimization cannot describe the dynamics of optimization in deep learning\, even in the simple setting of deterministic training. The challenge is that optimizers typically operate in a complex\, oscillatory regime called the “edge of stability.” In this paper\, we develop theory that can describe the dynamics of optimization in this regime. Our key insight is that while the *exact* trajectory of an oscillatory optimizer may be challenging to analyze\, the *time-averaged* (i.e. smoothed) trajectory is often much more tractable. To analyze an optimizer\, we derive a differential equation called a “central flow” that characterizes this time-averaged trajectory. We empirically show that these central flows can predict long-term optimization trajectories for generic neural networks with a high degree of numerical accuracy. By interpreting these central flows\, we are able to understand how gradient descent makes progress even as the loss sometimes goes up; how adaptive optimizers “adapt” to the local loss landscape; and how adaptive optimizers implicitly navigate towards regions where they can take larger steps. Our results suggest that central flows can be a valuable theoretical tool for reasoning about optimization in deep learning. \n 
URL:https://cmsa.fas.harvard.edu/event/newtech_10825/
LOCATION:Hybrid – G10
CATEGORIES:New Technologies in Mathematics Seminar
ATTACH;FMTTYPE=image/png:https://cmsa.fas.harvard.edu/media/CMSA-NTM-Seminar-10.8.2025-scaled.png
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BEGIN:VEVENT
DTSTART;TZID=America/New_York:20251009T140000
DTEND;TZID=America/New_York:20251009T150000
DTSTAMP:20260417T185739
CREATED:20250911T184457Z
LAST-MODIFIED:20251002T182058Z
UID:10003789-1760018400-1760022000@cmsa.fas.harvard.edu
SUMMARY:Profinite tensor powers
DESCRIPTION:Algebra Seminar \nSpeaker: David Treumann (Boston College) \nTitle: Profinite tensor powers \nAbstract: I’ll discuss the problem of defining a tensor product of profinitely many copies of a vector space V\, and propose a definition $\bigotimes_X^{mcc} V$ in the special situation that (1) V is finite-dimensional over F_2\, and (2) the profinite X indexing the tensor factors is acted on with finitely many orbits by a pro-2-group. The “mcc” on the tensor sign stands for “magnetized and conditionally convergent.” A variant construction makes sense when V is a bimodule over a semisimple F_2-algebra\, and the index set X has the profinite version of a cyclic order. The definition organizes some computations in Heegard Floer homology: it can be pitched as a computation of the HF of some pro-3-manifolds\, though we do not know how to define such a thing. This is joint work with CM Michael Wong. \n 
URL:https://cmsa.fas.harvard.edu/event/algebra-seminar_10925/
LOCATION:CMSA Room G10\, CMSA\, 20 Garden Street\, Cambridge\, MA\, 02138\, United States
CATEGORIES:Algebra Seminar
ATTACH;FMTTYPE=image/png:https://cmsa.fas.harvard.edu/media/CMSA-Algebra-Seminar-10.9.25-scaled.png
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/New_York:20251009T160000
DTEND;TZID=America/New_York:20251009T170000
DTSTAMP:20260417T185739
CREATED:20250904T162516Z
LAST-MODIFIED:20251010T130239Z
UID:10003779-1760025600-1760029200@cmsa.fas.harvard.edu
SUMMARY:Symmetries and Moduli Spaces: Baby Steps beyond Calabi-Yau
DESCRIPTION:Differential Geometry and Physics Seminar  \nSpeaker: Xingyang Yu\, Virginia Tech \nTitle: Symmetries and Moduli Spaces: Baby Steps beyond Calabi-Yau \nAbstract: I will explore the interplay between symmetries and moduli spaces in string compactifications\, starting from the familiar Calabi–Yau case and then taking some baby steps toward more general settings. A classical benchmark is the line bundle over Calabi–Yau complex structure moduli space\, whose physical counterpart corresponds to the Berry phase of the spectral flow operator in worldsheet SCFT. I will review this story and then discuss how it begins to change in c=1 theories with worldsheet anomalies\, and in G_2 and Spin(7) compactifications where U(1)_R symmetry is absent. The goal is not a finished framework\, but to highlight how anomalies and non-invertible symmetries may enter the picture and to raise open questions about what kinds of structures might live over moduli spaces beyond Calabi–Yau.
URL:https://cmsa.fas.harvard.edu/event/dgphys_10925/
LOCATION:CMSA Room G10\, CMSA\, 20 Garden Street\, Cambridge\, MA\, 02138\, United States
CATEGORIES:Differential Geometry and Physics Seminar
ATTACH;FMTTYPE=image/png:https://cmsa.fas.harvard.edu/media/DG-Physics-Seminar-9.9.2025-scaled.png
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END:VCALENDAR