Interdisciplinary Science Seminar

During the 2021–22 academic year, the CMSA will be hosting an Interdisciplinary Science Seminar, organized by Yingying Wu. This seminar will take place on Thursdays at 9:00am – 10:00am (Boston time). The meetings will take place virtually on Zoom. To learn how to attend, please fill out this form or contact Yingying Wu (ywu@cmsa.fas.harvard.edu).

This seminar encompasses theoretical mathematics and applications of geometric analysis to algorithms and machine learning. We hope the seminar will serve the role of facilitating collaborations between mathematicians, physicists, biologists, and medical practitioners to exchange ideas and participate in collaboration.

The schedule below will be updated as talks are confirmed.

Spring 2022

DateSpeakerTitle/Abstract
1/6/2022Boyu Zhang, Princeton UniversityTitle: The smooth closing lemma for area-preserving surface diffeomorphisms

Abstract: In this talk, I will introduce the smooth closing lemma for area-preserving diffeomorphisms on surfaces. The proof is based on a Weyl formula for PFH spectral invariants and a non-vanishing result of twisted Seiberg- Witten Floer homology. This is joint work with Dan Cristofaro-Gardiner and Rohil Prasad.
1/13/2022Francis Lazarus, CNRS / Grenoble UniversityTitle: A universal triangulation for flat tori

Abstract: A celebrated theorem of Nash completed by Kuiper implies that every smooth Riemannian surface has a C¹ isometric embedding in the Euclidean 3-space E³. An analogous result, due to Burago and Zalgaller, states that every polyhedral surface, obtained by gluing Euclidean triangles, has an isometric PL embedding in E³. In particular, this provides PL isometric embeddings for every flat torus (a quotient of E² by a rank 2 lattice). However, the proof of Burago and Zalgaller is partially constructive, relying on the Nash-Kuiper theorem. In practice, it produces PL embeddings with a huge number of vertices, moreover distinct for every flat torus. Based on a construction of Zalgaller and on recent works by Arnoux et al. we exhibit a universal triangulation with less than 10.000 vertices, admitting for any flat torus an isometric embedding that is linear on each triangle. Based on joint work with Florent Tallerie.
1/20/2022Yann Ollivier, FacebookTitle: Markov chains, optimal control, and reinforcement learning

Abstract: Markov decision processes are a model for several artificial intelligence problems, such as games (chess, Go…) or robotics. At each timestep, an agent has to choose an action, then receives a reward, and then the agent’s environment changes (deterministically or stochastically) in response to the agent’s action. The agent’s goal is to adjust its actions to maximize its total reward. In principle, the optimal behavior can be obtained by dynamic programming or optimal control techniques, although practice is another story.

Here we consider a more complex problem: learn all optimal behaviors for all possible reward functions in a given environment. Ideally, such a “controllable agent” could be given a description of a task (reward function, such as “you get +10 for reaching here but -1 for going through there”) and immediately perform the optimal behavior for that task. This requires a good understanding of the mapping from a reward function to the associated optimal behavior.

We prove that there exists a particular “map” of a Markov decision process, on which near-optimal behaviors for all reward functions can be read directly by an algebraic formula. Moreover, this “map” is learnable by standard deep learning techniques from random interactions with the environment. We will present our recent theoretical and empirical results in this direction.
1/27/2022Fabian Gundlach,
Harvard University
Title: Polynomials vanishing at lattice points in convex sets

Abstract: Let P be a convex subset of R^2. For large d, what is the smallest degree r_d of a polynomial vanishing at all lattice points in the dilate d*P? We show that r_d / d converges to some positive number, which we compute for many (but maybe not all) triangles P.
2/3/2022Aaron Fenyes, Institut des Hautes Études ScientifiquesTitle: Quasiperiodic prints from triply periodic blocks

Abstract: Slice a triply periodic wooden sculpture along an irrational plane. If you ink the cut surface and press it against a page, the pattern you print will be quasiperiodic. Patterns like these help physicists see how metals conduct electricity in strong magnetic fields. I’ll show you some block prints that imitate the printing process described above, and I’ll point out the visual features that reveal conductivity properties.

Interactive slides: https://www.ihes.fr/~fenyes/seeing/slices/
2/10/2022Maddie Weinstein, Stanford UniversityTitle: Metric Algebraic Geometry

Abstract: A real algebraic variety is the set of points in real Euclidean space that satisfy a system of polynomial equations. Metric algebraic geometry is the study of properties of real algebraic varieties that depend on a distance metric. In this talk, we introduce metric algebraic geometry through a discussion of Voronoi cells, bottlenecks, and the reach of an algebraic variety. We also show applications to the computational study of the geometry of data with nonlinear models.
2/17/2022Guilherme Ost, Institute of Mathematics of the Federal University of Rio de JaneiroTitle: Sparse Markov Models for High-dimensional Inference

Abstract: Finite order Markov models are theoretically well-studied models for dependent data.  Despite their generality, application in empirical work when the order is larger than one is quite rare.  Practitioners avoid using higher order Markov models because (1) the number of parameters grow exponentially with the order, (2) the interpretation is often difficult. Mixture of transition distribution models (MTD)  were introduced to overcome both limitations. MTD represent higher order Markov models as a convex mixture of single step Markov chains, reducing the number of parameters and increasing the interpretability. Nevertheless, in practice, estimation of MTD models with large orders are still limited because of curse of dimensionality and high algorithm complexity. Here, we prove that if only few lags are relevant we can consistently and efficiently recover the lags and estimate the transition probabilities of high order MTD models. Furthermore, we show that using the selected lags we can construct non-asymptotic confidence intervals for the transition probabilities of the model. The key innovation is a recursive procedure for the selection of the relevant lags of the model.  Our results are  based on (1) a new structural result of the MTD and (2) an improved martingale concentration inequality. Our theoretical results are illustrated through simulations.
2/24/2022
Hui Yu, National University of Singapore
Title: Singular Set in Obstacle Problems 

Abstract: In this talk we describe a new method to study the singular set in the obstacle problem. This method does not depend on monotonicity formulae and works for fully nonlinear elliptic operators. The result we get matches the best-known result for the case of Laplacian. 
03/03/2022Rong Ge, Duke UniversityTitle: Towards Understanding Training Dynamics for Mildly Overparametrized Models

Abstract: While over-parameterization is widely believed to be crucial for the success of optimization for the neural networks, most existing theories on over-parameterization do not fully explain the reason — they either work in the Neural Tangent Kernel regime where neurons don’t move much, or require an enormous number of neurons. In this talk I will describe our recent works towards understanding training dynamics that go beyond kernel regimes with only polynomially many neurons (mildly overparametrized). In particular, we first give a local convergence result for mildly overparametrized two-layer networks. We then analyze the global training dynamics for a related overparametrized tensor model. For both works, we rely on a key intuition that neurons in overparametrized models work in groups and it’s important to understand the behavior of an average neuron in the group. Based on two works: https://arxiv.org/abs/2102.02410 and https://arxiv.org/abs/2106.06573.

Bio: Professor Rong Ge is Associate Professor of Computer Science at Duke University. He received his Ph.D. from the Computer Science Department of Princeton University, supervised by Sanjeev Arora. He was a post-doc at Microsoft Research, New England. In 2019, he received both a Faculty Early Career Development Award from the National Science Foundation and the prestigious Sloan Research Fellowship. His research interest focus on theoretical computer science and machine learning. Modern machine learning algorithms such as deep learning try to automatically learn useful hidden representations of the data. He is interested in formalizing hidden structures in the data and designing efficient algorithms to find them. His research aims to answer these questions by studying problems that arise in analyzing text, images, and other forms of data, using techniques such as non-convex optimization and tensor decompositions.
3/10/2022
Wei Ai, University of Maryland, College Park
Title: Virtual Teams in Gig Economy — An End-to-End Data Science Approach

Abstract: The gig economy provides workers with the benefits of autonomy and flexibility, but it does so at the expense of work identity and co-worker bonds. Among the many reasons why gig workers leave their platforms, an unexplored aspect is the organization identity. In a series of studies, we develop a team formation and inter-team contest at a ride-sharing platform. We employ an end-to-end data science approach, combining methodologies from randomized field experiments, recommender systems, and counterfactual machine learning. Together, our results show that platform designers can leverage team identity and team contests to increase revenue and worker engagement in a gig economy.

Bio: Wei Ai is an Assistant Professor in the College of Information Studies (iSchool) and the Institute for Advanced Computer Studies (UMIACS) at the University of Maryland. His research interest lies in data science for social good, where the advances of machine learning and data analysis algorithms translate into measurable impacts on society. He combines machine learning, causal inference, and field experiments in his research, and has rich experience in collaborating with industrial partners. He earned his Ph.D. from the School of Information at the University of Michigan. His research has been published in top journals and conferences, including PNAS, ACM TOIS, WWW, and ICWSM.
3/17/2022Kenji Kawaguchi,
National University of Singapore
Title: On optimization and generalization in deep learning

Abstract: Deep neural networks have achieved significant empirical success in many fields, including the fields of computer vision and natural language processing. Along with its empirical success, deep learning has been theoretically shown to be attractive in terms of its expressive power. However, the theory of expressive power does not ensure that we can efficiently find an optimal solution in terms of optimization and generalization, during the optimization process. In this talk, I will discuss some mathematical properties of optimization and generalization for deep neural networks. 
3/24/2022Enno Keßler, Max Planck Institute for Mathematics (Bonn)Title: An operadic structure on supermoduli spaces

Abstract: The operadic structure on the moduli spaces of algebraic curves  encodes in a combinatorial way how nodal curves in the boundary can be obtained by glueing smooth curves along marked points. In this talk, I will present a generalization of the operadic structure to moduli spaces of SUSY curves (or super Riemann surfaces). This requires colored graphs and generalized operads in the sense of Borisov-Manin. Based joint work with Yu. I. Manin and Y. Wu. https://arxiv.org/abs/2202.10321
3/31/2022Christopher Eur, Harvard UniversityTitle: Compactification of an embedded vector space and its combinatorics

Abstract: Matroids are combinatorial abstractions of vector spaces embedded in a coordinate space.  Many fundamental questions have been open for these classical objects.  We highlight some recent progress that arise from the interaction between matroid theory and algebraic geometry.  Key objects involve compactifications of embedded vector spaces, and an exceptional Hirzebruch-Riemann-Roch isomorphism between the K-ring of vector bundles and the cohomology ring of stellahedral varieties.
4/7/2022Ravi Vakil, Stanford UniversityTitle: The space of vector bundles on spheres: algebra, geometry, topology

Abstract: Bott periodicity relates vector bundles on a topological space X to vector bundles on X “times a sphere”.   I’m not a topologist, so I will try to explain an algebraic or geometric incarnation, in terms of vector bundles on the Riemann sphere.   I will attempt to make the talk introductory, and (for the most part) accessible to those in all fields, at the expense of speaking informally and not getting far.   This relates to recent work of Hannah Larson, as well as joint work with (separately) Larson and Jim Bryan.
4/14/2022Songpeng Zu, University of California, San DiegoTitle: SIMPLEs: a single-cell RNA sequencing imputation strategy preserving gene modules and cell clusters variation

Abstract: A main challenge in analyzing single-cell RNA sequencing (scRNA-seq) data is to reduce technical variations yet retain cell heterogeneity. Due to low mRNAs content per cell and molecule losses during the experiment (called ‘dropout’), the gene expression matrix has a substantial amount of zero read counts. Existing imputation methods treat either each cell or each gene as independently and identically distributed, which oversimplifies the gene correlation and cell type structure. We propose a statistical model-based approach, called SIMPLEs (SIngle-cell RNA-seq iMPutation and celL clustErings), which iteratively identifies correlated gene modules and cell clusters and imputes dropouts customized for individual gene module and cell type. Simultaneously, it quantifies the uncertainty of imputation and cell clustering via multiple imputations. In simulations, SIMPLEs performed significantly better than prevailing scRNA-seq imputation methods according to various metrics. By applying SIMPLEs to several real datasets, we discovered gene modules that can further classify subtypes of cells. Our imputations successfully recovered the expression trends of marker genes in stem cell differentiation and can discover putative pathways regulating biological processes.
4/21/2022Peihan Miao, University of Illinois ChicagoTitle: Secure Multi-Party Computation: from Theory to Practice

Abstract: Encryption is the backbone of cybersecurity. While encryption can secure data both in transit and at rest, in the new era of ubiquitous computing, modern cryptography also aims to protect data during computation. Secure multi-party computation (MPC) is a powerful technology to tackle this problem, which enables distrustful parties to jointly perform computation over their private data without revealing their data to each other. Although it is theoretically feasible and provably secure, the adoption of MPC in real industry is still very much limited as of today, the biggest obstacle of which boils down to its efficiency.

My research goal is to bridge the gap between the theoretical feasibility and practical efficiency of MPC. Towards this goal, my research spans both theoretical and applied cryptography. In theory, I develop new techniques for achieving general MPC with the optimal complexity, bringing theory closer to practice. In practice, I design tailored MPC to achieve the best concrete efficiency for specific real-world applications. In this talk, I will discuss the challenges in both directions and how to overcome these challenges using cryptographic approaches. I will also show strong connections between theory and practice.

Biography: Peihan Miao is an assistant professor of computer science at the University of Illinois Chicago (UIC). Before coming to UIC, she received her Ph.D. from the University of California, Berkeley in 2019 and had brief stints at Google, Facebook, Microsoft Research, and Visa Research. Her research interests lie broadly in cryptography, theory, and security, with a focus on secure multi-party computation — especially in incorporating her industry experiences into academic research.
4/28/2022
Tina Torkaman,
Harvard University
Title: Intersection number and systole on hyperbolic surfaces

Abstract: Let X be a compact hyperbolic surface. We can see that there is a constant C(X) such that the intersection number of the closed geodesics is  \leq C(X) times the product of their lengths. Consider the optimum constant C(X). In this talk, we describe its asymptotic behavior in terms of systole,  length of the shortest closed geodesic on X.
5/5/2022
Yang Yuan,
Tsinghua University, IIIS
Title Qianfang: a type-safe and data-driven healthcare system starting from Traditional Chinese Medicine

Abstract: Although everyone talks about AI + healthcare, many people were unaware of the fact that there are two possible outcomes of the collaboration, due to the inherent dissimilarity between the two giant subjects. The first possibility is healthcare-leads, and AI is for building new tools to make steps in healthcare easier, better, more effective or more accurate. The other possibility is AI-leads, and therefore the protocols of healthcare can be redesigned or redefined to make sure that the whole infrastructure and pipelines are ideal for running AI algorithms.
Our system Qianfang belongs to the second category. We have designed a new kind of clinic for the doctors and patients, so that it will be able to collect high quality data for AI algorithms. Interestingly, the clinic is based on Traditional Chinese Medicine (TCM) instead of modern medicine, because we believe that TCM is more suitable for AI algorithms as the starting point.
In this talk, I will elaborate on how we convert TCM knowledge into a modern type-safe large-scale system, the mini-language that we have designed for the doctors and patients, the interpretability of AI decisions, and our feedback loop for collecting data.
Our project is still on-going, not finished yet.

Bio: Yang Yuan is now an assistant professor at IIIS, Tsinghua. He finished his undergraduate study at Peking University in 2012. Afterwards, he received his PhD at Cornell University in 2018, advised by Professor Robert Kleinberg. During his PhD, he was a visiting student at MIT/Microsoft New England (2014-2015) and Princeton University (2016 Fall). Before joining Tsinghua, he spent one year at MIT Institute for Foundations of Data Science (MIFODS) as a postdoc researcher. He now works on AI+Healthcare, AI Interpretability and AI system.
5/12/2022Justin Solomon,
MIT
Title: Geometric Models for Sets of Probability Measures

Abstract: Many statistical and computational tasks boil down to comparing probability measures expressed as density functions, clouds of data points, or generative models.  In this setting, we often are unable to match individual data points but rather need to deduce relationships between entire weighted and unweighted point sets. In this talk, I will summarize our team’s recent efforts to apply geometric techniques to problems in this space, using tools from optimal transport and spectral geometry. Motivated by applications in dataset comparison, time series analysis, and robust learning, our work reveals how to apply geometric reasoning to data expressed as probability measures without sacrificing computational efficiency.
5/19/2022Fatemeh Mohammadi,
Ghent University
Title: The geometry of conditional independence models with hidden variables

Abstract: Conditional independence (CI) is an important tool in statistical modeling, as, for example, it gives a statistical interpretation to graphical models. In general, given a list of dependencies among random variables, it is difficult to say which constraints are implied by them. Moreover, it is important to know what constraints on the random variables are caused by hidden variables. On the other hand, such constraints are corresponding to some determinantal conditions on the tensor of joint probabilities of the observed random variables. Hence, the inference question in statistics relates to understanding the algebraic and geometric properties of determinantal varieties such as their irreducible decompositions or determining their defining equations. I will explain some recent progress that arises by uncovering the link to point configurations in matroid theory and incidence geometry. This connection, in particular, leads to effective computational approaches for (1) giving a decomposition for each CI variety; (2) identifying each component in the decomposition as a matroid variety; (3) determining whether the variety has a real point or equivalently there is a statistical model satisfying a given collection of dependencies. 
 
The talk is based on joint works with Oliver Clarke, Kevin Grace, and Harshit Motwani. 
 
The papers are available on arxiv: https://arxiv.org/pdf/2011.02450 and https://arxiv.org/pdf/2103.16550.pdf
5/26/2022Wai-Tong Louis Fan, Indiana UniversityTitle: Extinction and coexistence for reaction-diffusion systems on metric graphs

Abstract: In spatial population genetics, it is important to understand the probability of extinction in multi-species interactions such as growing bacterial colonies, cancer tumor evolution and human migration. This is because extinction probabilities are instrumental in determining the probability of coexistence and the genealogies of populations. A key challenge is the complication due to spatial effect and different sources of stochasticity. In this talk, I will discuss about methods to compute the probability of extinction and other long-time behaviors for stochastic reaction-diffusion equations on metric graphs that flexibly parametrizes the underlying space. Based on recent joint work with Adrian Gonzalez-Casanova and Yifan (Johnny) Yang.
6/2/2022Jaesik Park, Pohang University of Science and TechnologyTitle: Fast Point Transformer

Abstract: The recent success of neural networks enables a better interpretation of 3D point clouds, but processing a large-scale 3D scene remains a challenging problem. Most current approaches divide a large-scale scene into small regions and combine the local predictions together. However, this scheme inevitably involves additional stages for pre- and post-processing and may also degrade the final output due to predictions in a local perspective. This talk introduces Fast Point Transformer that consists of a new lightweight self-attention layer. Our approach encodes continuous 3D coordinates, and the voxel hashing-based architecture boosts computational efficiency. The proposed method is demonstrated with 3D semantic segmentation and 3D detection. The accuracy of our approach is competitive to the best voxel-based method, and our network achieves 129 times faster inference time than the state-of-the-art, Point Transformer, with a reasonable accuracy trade-off in 3D semantic segmentation on S3DIS dataset.

Bio: Jaesik Park is an Assistant Professor at POSTECH. He received his Bachelor’s degree from Hanyang University in 2009, and he received his Master’s degree and Ph.D. degree from KAIST in 2011 and 2015, respectively. Before joining POSTECH, He worked at Intel as a research scientist, where he co-created the Open3D library. His research interests include image synthesis, scene understanding, and 3D reconstruction. He serves as a program committee at prestigious computer vision conferences, such as Area Chair for ICCV, CVPR, and ECCV.
6/16/2022Jianfeng Lu,
Duke University
Title: Surface hopping algorithms for non-adiabatic quantum systems

Abstract: Surface hopping algorithm is widely used in chemistry for mixed quantum-classical dynamics. In this talk, we will discuss some of our recent works in mathematical understanding and algorithm development for surface hopping methods. These methods are based on stochastic approximations of semiclassical path-integral representation to the solution of multi-level Schrodinger equations; such methodology also extends to other high dimensional transport systems.
6/23/2022Hui Jiang, University of Michigan

Location: Science Center 530
Title: Some new algorithms in statistical genomics

Abstract: The statistical analysis of genomic data has incubated many innovations for computational method development. This talk will discuss some simple algorithms that may be useful in analyzing such data. Examples include algorithms for efficient resampling-based hypothesis testing, minimizing the sum of truncated convex functions, and fitting equality-constrained lasso problems. These algorithms have the potential to be used in other applications beyond statistical genomics.

Bio: Hui Jiang is an Associate Professor in the Department of Biostatistics at the University of Michigan. He received his Ph.D. in Computational and Mathematical Engineering from Stanford University. Before joining the University of Michigan, he was a postdoc in the Department of Statistics and Stanford Genome Technology Center at Stanford University. He is interested in developing statistical and computational methods for analyzing large-scale biological data generated using modern high-throughput technologies.
6/30/2022Spyros Tserkis, HarvardTitle: Entanglement and its key role in quantum information

Abstract: Entanglement is a type of correlation found in composite quantum systems, connected with various non-classical phenomena. Currently, entanglement plays a key role in quantum information applications such as quantum computing, quantum communication, and quantum sensing. In this talk the concept of entanglement will be introduced along with various methods that have been proposed to detect and quantify it. The fundamental role of entanglement in both quantum theory and quantum technology will also be discussed.

Bio: Spyros Tserkis is a postdoctoral researcher at Harvard University, working on quantum information theory. Before joining Harvard in Fall 2021, he was a postdoctoral researcher at MIT and the Australian National University. He received his PhD from the University of Queensland.
7/7/2022Fouad El Baidouri, Broad InstituteTBA
7/14/2022Ioannis Petrides, Harvard UniversityTitle: Topological and geometrical aspects of spinors in insulating crystals

Abstract:  Introducing internal degrees of freedom in the description of crystalline insulators has led to a myriad of theoretical and experimental advances. Of particular interest are the effects of periodic perturbations, either in time or space, as they considerably enrich the variety of electronic responses. Here, we present a semiclassical approach to transport and accumulation of general spinor degrees of freedom in adiabatically driven, weakly inhomogeneous crystals of dimensions one, two and three under external electromagnetic fields. Our approach shows that spatio-temporal modulations of the system induce a spinor current and density that is related to geometrical and topological objects — the spinor-Chern fluxes and numbers — defined over the higher-dimensional phase-space of the system, i.e., its combined momentum-position-time coordinates.
The results are available here: https://arxiv.org/abs/2203.14902

Bio: Ioannis Petrides is a postdoctoral fellow at the School of Engineering and Applied Sciences at Harvard University. He received his Ph.D. from the Institute for Theoretical Physics at ETH Zurich. His research focuses on the topological and geometrical aspects of condensed matter systems.
7/21/2022Vaidehi Subhash Natu, StanfordTitle: Infants’ sensory-motor cortices undergo microstructural tissue growth coupled with myelination

Abstract: The establishment of neural circuitry during early infancy is critical for developing visual, auditory, and motor functions. However, how cortical tissue develops postnatally is largely unknown. By combining T1 relaxation time from quantitative MRI and mean diffusivity (MD) from diffusion MRI, we tracked cortical tissue development in infants across three timepoints (newborn, 3 months, and 6 months). Lower T1 and MD indicate higher microstructural tissue density and more developed cortex. Our data reveal three main findings: First, primary sensory-motor areas (V1: visual, A1: auditory, S1: somatosensory, M1: motor) have lower Tand MD at birth than higher-level cortical areas. However, all primary areas show significant reductions in Tand MD in the first six months of life, illustrating profound tissue growth after birth. Second, significant reductions in Tand MD from newborns to 6-month-olds occur in all visual areas of the ventral and dorsal visual streams. Strikingly, this development was heterogenous across the visual hierarchies: Earlier areas are more developed with denser tissue at birth than higher-order areas, but higher-order areas had faster rates of development. Finally, analysis of transcriptomic gene data that compares gene expression in postnatal vs. prenatal tissue samples showed strong postnatal expression of genes associated with myelination, synaptic signaling, and dendritic processes. Our results indicate that these cellular processes may contribute to profound postnatal tissue growth in sensory cortices observed in our in-vivo measurements. We propose a novel principle of postnatal maturation of sensory systems: development of cortical tissue proceeds in a hierarchical manner, enabling the lower-level areas to develop first to provide scaffolding for higher-order areas, which begin to develop more rapidly following birth to perform complex computations for vision and audition.
 
This work is published here: https://www.nature.com/articles/s42003-021-02706-w

Fall 2021

DateSpeakerTitle/Abstract
09/23/2021Michael Simkin,
Harvard CMSA
Title: The number of n-queens configurations

Abstract: The n-queens problem is to determine Q(n), the number of ways to place n mutually non-threatening queens on an n x n board. The problem has a storied history and was studied by such eminent mathematicians as Gauss and Polya. The problem has also found applications in fields such as algorithm design and circuit development.

Despite much study, until recently very little was known regarding the asymptotics of Q(n). We apply modern methods from probabilistic combinatorics to reduce understanding Q(n) to the study of a particular infinite-dimensional convex optimization problem. The chief implication is that (in an appropriate sense) for a~1.94, Q(n) is approximately (ne^(-a))^n. Furthermore, our methods allow us to study the typical “shape” of n-queens configurations.
09/30/2021Itamar Shamir, CMSATitle: Gravity, geometry and entanglement

Abstract: I will review some of the problems and central ideas regarding the quantum nature of gravity. The talk is intended for non-specialists and as much as possible assumes no prier knowledge. 
10/07/2021Nicholas J. Magazine, Louisiana State University Title: SiRNA Targeting TCRb: A Proposed Therapy for the Treatment of Autoimmunity

Abstract: As of 2018, the United States National Institutes of Health estimate that over half a billion people worldwide are affected by autoimmune disorders. Though these conditions are prevalent, treatment options remain relatively poor, relying primarily on various forms of immunosuppression which carry potentially severe side effects and often lose effectiveness over time. Given this, new forms of therapy are needed. To this end, we have developed methods for the creation of small-interfering RNA (siRNA) for hypervariable regions of the T-cell receptor β-chain gene (TCRb) as a highly targeted, novel means of therapy for the treatment of autoimmune disorders.

This talk will review the general mechanism by which autoimmune diseases occur and discuss the pros and cons of conventional pharmaceutical therapies as they pertain to autoimmune disease treatment. I will then examine the rational and design methodology for the proposed siRNA therapy and how it contrasts with contemporary methods for the treatment of these conditions. Additionally, the talk will compare the efficacy of multiple design strategies for such molecules by comparison over several metrics and discuss how this will be guiding future research.
10/14/2021Ian Gemp, DeepMindTitle: D3C: Reducing the Price of Anarchy in Multi-Agent Learning

Abstract: In multi-agent systems the complex interaction of fixed incentives can lead agents to outcomes that are poor (inefficient) not only for the group but also for each individual agent. Price of anarchy is a technical game theoretic definition introduced to quantify the inefficiency arising in these scenarios– it compares the welfare that can be achieved through perfect coordination against that achieved by self-interested agents at a Nash equilibrium. We derive a differentiable upper bound on a price of anarchy that agents can cheaply estimate during learning. Equipped with this estimator agents can adjust their incentives in a way that improves the efficiency incurred at a Nash equilibrium. Agents adjust their incentives by learning to mix their reward (equiv. negative loss) with that of other agents by following the gradient of our derived upper bound. We refer to this approach as D3C. In the case where agent incentives are differentiable D3C resembles the celebrated Win-Stay Lose-Shift strategy from behavioral game theory thereby establishing a connection between the global goal of maximum welfare and an established agent-centric learning rule. In the non-differentiable setting as is common in multiagent reinforcement learning we show the upper bound can be reduced via evolutionary strategies until a compromise is reached in a distributed fashion. We demonstrate that D3C improves outcomes for each agent and the group as a whole on several social dilemmas including a traffic network exhibiting Braess’s paradox a prisoner’s dilemma and several reinforcement learning domains.
10/21/2021Colin Guillarmou, CNRS/Univ. Paris SaclayTitle: Mathematical resolution of the Liouville conformal field theory.

Abstract: The Liouville conformal field theory is a well-known beautiful quantum field theory in physics describing random surfaces. Only recently a mathematical approach based on a well-defined path integral to this theory has been proposed using probability by David, Kupiainen, Rhodes, Vargas. 

Many works since the ’80s in theoretical physics (starting with Belavin-Polyakov-Zamolodchikov) tell us that conformal field theories in dimension 2 are in general « Integrable », the correlations functions are solutions of PDEs and can in principle be computed explicitely by using algebraic tools (vertex operator algebras, representations of Virasoro algebras, the theory of conformal blocks). However, for Liouville Theory this was not done at the mathematical level by algebraic methods.

I’ll explain how to combine probabilistic, analytic and geometric tools to give explicit (although complicated) expressions for all the correlation functions on all Riemann surfaces in terms of certain holomorphic functions of the moduli parameters called conformal blocks, and of the structure constant (3-point function on the sphere). This gives a concrete mathematical proof of the so-called conformal bootstrap and of Segal’s gluing axioms for this CFT. The idea is to break the path integral on a closed surface into path integrals on pairs of pants and reduce all correlation functions to the 3-point correlation function on the Riemann sphere $S^2$. This amounts in particular to prove a spectral resolution of a certain operator acting on $L^2(H^{-s}(S^1))$ where $H^{-s}(S^1)$ is the Sobolev space of order -s<0 equipped with a Gaussian measure, which is viewed as the space of fields, and to construct a certain representation of the Virasoro algebra into unbounded operators acting on this Hilbert space.  

This is joint work with A. Kupiainen, R. Rhodes and V. Vargas.
10/28/2021Jie Yang, Delft University of TechnologyTitle: ARCH: Know What Your Machine Doesn’t Know

Abstract: Despite their impressive performance, machine learning systems remain prohibitively unreliable in safety-, trust-, and ethically sensitive domains. Recent discussions in different sub-fields of AI have reached the consensus of knowledge need in machine learning; few discussions have touched upon the diagnosis of what knowledge is needed. In this talk, I will present our ongoing work on ARCH, a knowledge-driven, human-centered, and reasoning-based tool, for diagnosing the unknowns of a machine learning system. ARCH leverages human intelligence to create domain knowledge required for a given task and to describe the internal behavior of a machine learning system; it infers the missing or incorrect knowledge of the system with the built-in probabilistic, abductive reasoning engine. ARCH is a generic tool that can be applied to machine learning in different contexts. In the talk, I will present several applications in which ARCH is currently being developed and tested, including health, finance, and smart buildings.
11/04/2021Qifeng Chen, The Hong Kong University of Science and TechnologyTitleExploring Invertibility in Image Processing and Restoration

Abstract: Today’s smartphones have enabled numerous stunning visual effects from denoising to beautification, and we can share high-quality JPEG images easily on the internet, but it is still valuable for photographers and researchers to keep the original raw camera data for further post-processing (e.g., retouching) and analysis. However, the huge size of raw data hinders its popularity in practice, so can we almost perfectly restore the raw data from a compressed RGB image and thus avoid storing any raw data? This question leads us to design an invertible image signal processing pipeline. Then we further explore invertibility in other image processing and restoration tasks, including image compression, reversible image conversion (e.g., image-to-video conversion), and embedding novel views in a single JPEG image. We demonstrate that customized invertible neural networks are highly effective in these inherently non-invertible tasks.
11/11/2021Lvzhou Chen, University of Texas at AustinTitle: The Kervaire conjecture and the minimal complexity of surfaces 

Abstract: We use topological methods to solve special cases of a fundamental problem in group theory, the Kervaire conjecture. 
The conjecture asserts that, for any nontrivial group G and any element w in the free product G*Z, the quotient (G*Z)/<<w>> is still nontrivial. We interpret this as a problem of estimating the minimal complexity (in terms of Euler characteristic) of surfaces in HNN extensions. This gives a conceptually simple proof of Klyachko’s theorem that confirms the Kervaire conjecture for any G torsion-free. I will also explain new results obtained using this approach. 
11/18/2021Matteo Parisi, CMSA/IASTitle: Amplituhedra, Scattering Amplitudes and Triangulations

Abstract: In this talk I will discuss about Amplituhedra – generalizations of polytopes inside the Grassmannian – recently introduced by physicists as new geometric constructions encoding interactions of elementary particles in certain Quantum Field Theories. In particular, I will explain how the problem of finding triangulations of Amplituhedra is connected to computing scattering amplitudes of N=4 super Yang-Mills theory. Triangulations of polygons are encoded in the associahedron studied by Stasheff in the sixties; in the case of polytopes, triangulations are captured by secondary polytopes constructed by Gelfand et al. in the nineties. Whereas a “secondary” geometry describing triangulations of Amplituhedra is still not known, and we pave the way for such studies. We will discuss how the combinatorics of triangulations interplays with T-duality from String Theory, in connection with a dual object we define – the Momentum Amplituhedron. A generalization of T-duality led us to discover a striking duality between triangulations of Amplituhedra of “m=2” type and the ones of a seemingly unrelated object – the Hypersimplex. The latter is a polytope which has been central in many contexts, such as matroid theory, torus orbits in the Grassmannian, and tropical geometry. Based on joint works with Lauren Williams, Melissa Sherman-Bennett, Tomasz Lukowski [arXiv:2104.08254, arXiv:2002.06164].
12/02/2021Ryosuke Takahashi, National Cheng Kung UniversityTitle: Polyhomogeneous expansions and Z/2-harmonic spinors branching along graphs

Abstract: In this talk, we will first reformulate the linearization of the moduli space of Z/2-harmonic spinorsv branching along a knot. This formula tells us that the kernel and cokernel of the linearization are isomorphic to the kernel and cokernel of the Dirac equation with a polyhomogeneous boundary condition. In the second part of this talk, I will describe the polyhomogenous expansions for the Z/2-harmonic spinors branching along graphs and formulate the Dirac equation with a suitable boundary condition that can describe the perturbation of graphs with some restrictions. This is joint work with Andriy Haydys and Rafe Mazzeo.
12/09/2021

11:30–12:30 pm
Michael Douglas, Simons Center/CMSATitle: Numerical Higher Dimensional Geometry

Abstract: In 1977, Yau proved that a Kahler manifold with zero first Chern class admits a Ricci flat metric, which is uniquely determined by certain “moduli” data. These metrics have been very important in mathematics and in theoretical physics, but despite much subsequent work we have no analytical expressions for them. But significant progress has been made on computing numerical approximations. We give an introduction (not assuming knowledge of complex geometry) to these problems and describe these methods.
12/16/2021An Huang,
Brandeis University
Title: Quadratic reciprocity from a family of adelic conformal field theories

Abstract: We consider a deformation of the 2d free scalar field action by raising the Laplacian to a positive real power. It turns out that the resulting non-local generalized free action is invariant under two commuting actions of the global conformal symmetry algebra, although it’s no longer invariant under the local conformal symmetry algebra. Furthermore, there is an adelic version of this family of global conformal field theories, parametrized by the choice of a number field, together with a Hecke character. Tate’s thesis plays an important role here in calculating Green’s functions of these theories, and in ensuring the adelic compatibility of these theories. In particular, the local L-factors contribute to prefactors of these Green’s functions. We shall try to see quadratic reciprocity from this context, as a consequence of an adelic version of holomorphic factorization of these theories. This is work in progress with B. Stoica and X. Zhong.

Past Seminars

The CMSA Interdisciplinary Science Seminar, organized by Yingying Wu, will take place on Thursdays from 9:00 – 10:00 am ET. This seminar concentrates on geometric analysis, algorithms, and mathematical biology with an emphasis on genetics. The seminar is dedicated to applications of mathematics and computer science to life science and medicine. We hope the seminar will serve the role of facilitating collaborations between mathematicians, physicists, and computer scientists with domain experts in biology and medicine.

DateSpeakerTitle/Abstract
3/18/2021Omri Ben-Eliezer (CMSA)Title: Adversarially robust streaming algorithms

Abstract: Streaming algorithms are an important class of algorithms designed for analyzing and summarizing large-scale datasets. In this context, the goal is usually to obtain algorithms whose space complexity (or memory consumption) is as small as possible, making them convenient to use on a single machine.
Traditionally, streaming algorithms have been analyzed in the static setting, where the stream of incoming data is fixed in advance and does not depend on the algorithm’s outputs. This, however, is unrealistic in many situations. In this talk, I will present and discuss adversarially robust streaming algorithms, whose output is correct with high probability even when the stream updates are adaptively chosen as a function of previous outputs. This regime has received surprisingly little attention until recently, and many intriguing problems are still open. I will mention some of the recent results, discussing algorithms that are well-suited for the adversarially robust regime (random sampling), algorithms that are not robust (linear sketching), and efficient techniques to turn algorithms that work in the standard static setting into robust streaming algorithms.
The results demonstrate strong connections between the streaming context and various other areas in computer science, combinatorics and statistics.
Based on joint works with Noga Alon, Yuval Dagan, Rajesh Jayaram, Shay Moran, Moni Naor, David Woodruff, and Eylon Yogev.
3/25/2021Cliff Taubes (Department of Mathematics, Harvard University)Title: Introduction to 4-dimensional differential topology.

Abstract: Differential topology is the study of smooth manifolds. I hope to tell you where the frontier lies between knowledge and ignorance with regards to smooth 4-dimensional manifolds (which is by far the hardest dimension to understand).
4/1/2021CanceledFrontiers in Applied Mathematics and Computation
4/8/2021
12:00pm ET
Enno KeßlerTitle: Supergeometry and Super Riemann Surfaces of Genus Zero

Abstract:  Supergeometry is a mathematical theory of geometric spaces with anti-commuting coordinates and functions which is motivated by the concept of supersymmetry from theoretical physics. I will explain the functorial approach to supermanifolds by Molotkov and Sachse. Super Riemann surfaces are an interesting supergeometric generalization of Riemann surfaces. I will present a differential geometric approach to their classification in the case of genus zero and with Neveu-Schwarz punctures.
4/15/2021Cheng Yu (Department of Mathematics, University of Florida)Title: Weak solutions to the isentropic system of gas dynamics

Abstract: In this talk, I will discuss the global weak solutions to the isentropic system of gas dynamics: existence and non-uniqueness. In the first part, we generalized the renormalized techniques introduced by DiPerna-Lions to build up the global weak solutions to the compressible Navier-Stokes equations with degenerate viscosities. This existence result holds for any $\gamma>1$ in any dimensional spaces for the large initial data. In the second part, we proved that for any initial data belonging to a dense subset of the energy space, there exists infinitely many global weak solutions to the isentropic Euler equations for any $1<\gamma\leq 1+2/n$. Our result is based on a generalization of convex integration techniques by De Lellis-Szekelyhidi and weak vanishing viscosity limit of the Navier-Stokes equations. The first part is based on the joint works with D. Bresch and A. Vasseur, and the second one is based on our recent joint work with R. M Chen and A. Vasseur.
4/22/2021Matt Novack (New York University)Title: Convex Integration and Fluid Turbulence

Abstract: The Navier-Stokes and Euler equations are the fundamental models for describing viscous and inviscid fluids, respectively. Based on ideas which date back to Kolmogorov and Onsager, solutions to these equations are expected to dissipate energy even in the vanishing viscosity limit, which in turn suggests that such solutions are somewhat rough and thus only weak solutions. At these low regularity levels, however, one may construct wild weak solutions using convex integration methods. These methods originated in the works of Nash and Gromov and were adapted to the context of fluid equations by De Lellis and Szekelyhidi Jr. In this talk, we will survey the history of both phenomenological theories of turbulence and convex integration. Finally, we discuss recent joint work with Tristan Buckmaster, Nader Masmoudi, and Vlad Vicol in which we construct wild solutions to the Euler equations which deviate from Kolmogorov’s predictions.
4/29/2021Yijing Wu (Department of Mathematics, University of Maryland, College Park)Title: An isoperimetric problem with a competing nonlocal singular term

Abstract: We are interested in the minimization problem of a functional in which the perimeter is competing with a nonlocal singular term comparable to a fractional perimeter, with volume constraint. We prove that minimizers exist and are radially symmetric for small mass, while minimizers cannot be radially symmetric for large mass. For large mass, we prove that the minimizing sequences either split into smaller sets that drift to infinity or develop fingers of a prescribed width. We connect these two alternatives to a related minimization problem for the optimal constant in a classical interpolation inequality.
5/6/2021Aaron Fenyes (Institut des Hautes Études Scientifiques)Title: Visualizing neutral theory

Abstract: In this expository talk, I’ll use 1d voter models to illustrate basic features of neutral theory—a vision of how genetic and ecological diversity can emerge even without selective pressure. We’ll see how questions about the persistence and spatial organization of lineages can be rephrased, in these models, as questions about random walks.
5/13/2021Jialin Zhang (Institute of Computing Technology, Chinese Academy of Science)Title: A Tight Deterministic Algorithm for the Submodular Multiple Knapsack Problem

Abstract: Submodular function maximization has been a central topic in the theoretical computer science community over the last decade. Plenty of well-performing approximation algorithms have been designed for the maximization of (monotone or non-monotone) submodular functions over a variety of constraints. In this talk, we consider the submodular multiple knapsack problem (SMKP), which is the submodular version of the well-studied multiple knapsack problem (MKP). Roughly speaking, the problem asks to maximize a monotone submodular function over multiple bins (knapsacks). Recently, Fairstein et al. (ESA20) presented a tight (1−1/e−ϵ)-approximation randomized algorithm for SMKP. Their algorithm is based on the continuous greedy technique which inherently involves randomness. However, the deterministic algorithm of this problem has not been understood very well previously. In this paper, we present a tight (1−1/e−ϵ) deterministic algorithm for SMKP. Our algorithm is based on reducing SMKP to an exponential-size submodular maximizaion problem over a special partition matroid which enjoys a tight deterministic algorithm. We develop several techniques to mimic the algorithm, leading to a tight deterministic approximation for SMKP.
5/20/2021Shang Su (Department of Cancer Biology, The University of Toledo)Title: In silico design and evaluation of PROTAC-based protein degrader–Introductory case studies

Abstract: Proteolysis-targeting chimeras (PROTACs) are heterobifunctional small molecules consisting of two chemical moieties connected by a linker.  The simultaneous binding of a PROTAC to both a target protein and an E3 ligase facilitates ubiquitination and degradation of the target protein. Since its proof-of-concept research in 2001, PROTAC has been vigorously developed by both research community and pharma industry, to act against therapeutically significant proteins, such as BRD4, BTK, and STAT3. However, despite the enthusiasm, designing PROTACs is challenging. Till now, no case of de novo rational design of PROTACs has been reported and the successful PROTACs usually came from the functional screen from a limitedly scaled library.  As formation of a ternary complex between the protein target, the PROTAC, and the recruited E3 ligase is considered paramount for successful degradation, several computational algorithms (PRosettaC as the example), have been developed to model this ternary complex, which have got partial agreement with the experimental data and in principle inform future rational PROTAC design. Here I will introduce some of these computational methods and share how they model the ternary complexes. 
5/27/2021Ying Hsang Liu & Moritz Spiller (University of Southern Denmark & Otto von Guericke University Magdeburg)Title: Predicting Visual Search Task Success from Eye Gaze Data for User-Adaptive Information Visualization Systems

Abstract: Information visualizations are an efficient means to support the users in understanding large amounts of complex, interconnected data; user comprehension. Previous research suggests that user-adaptive information visualizations positively impact the users’ performance in visualization tasks. This study aims to develop a computational model to predict the users’ success in visual search tasks from eye gaze data and thereby drive such user-adaptive systems. State-of-the-art deep learning models for time series classification have been trained on sequential eye gaze data obtained from 40 study participants’ interaction with a circular and an organizational graph. The results suggest that such models yield higher accuracy than a baseline classifier and previously used models for this purpose. In particular, a Multivariate Long Short Term Memory Fully Convolutional Network (MLSTM-FCN) shows encouraging performance for its use in on-line user-adaptive systems. Given this finding, such a computational model can infer the users’ need for support during interaction with a graph and trigger appropriate interventions in user-adaptive information visualization systems
6/3/2021Joaquim I. Goes (Lamont Doherty Earth Observatory at Columbia University)Title: Navigating Seas of Change – the Role and Significance of Cross-Disciplinary Research

Abstract: As atmospheric CO2 levels continue to rise and global and coastal ocean become warmer and more eutrophic as a result of human activities, we need better ways to detect and understand how marine ecosystems are responding to these changes. Until recently, most biological oceanographers relied on shipboard measurements that were limited in their coverage and inadequate to investigate changes at large spatial and temporal scales. With the advent of satellites, autonomous platforms and numerical methods, biological oceanographers are turning to empirical and semi-analytical algorithms to scale limited shipboard measurements from local scales to regional, basin and global scales. While progress has been interdisciplinary research involving collaborations between biological, physical and methodical scientists could help us make rapid advances and mitigate impacts on the livelihoods of coastal communities which are at greatest risk. This presentation will cover a case study from the Arabian Sea in the Indian Ocean and describe the promise and potential of inter-disciplinary research in advancing climate change and ecosystem research for societal benefit.
6/10/2021Tianqi Wu (CMSA)Title: The deformation space of geodesic triangulations and Tutte’s embedding

Abstract: In 1984, Bloch, Connelly, and Henderson proved that the space of geodesic triangulations of a convex polygon is contractible. It was found that Tutte’s embedding theorem could give a very simple proof to Bloch-Connelly-Henderson’s theorem, and provides an elegant algorithm for image morphing on convex polygons. We recently generalize Tutte’s embedding theorem, and prove that the deformation space of geodesic triangulations of a closed Riemannian surface of negative curvature is contractible. This confirms a conjecture by Connelly, Henderson, Ho, Starbird in 1983, and also indicates a method for image morphing on closed surfaces.
6/17/2021Fang Xie (BIDMC and HMS, Harvard University)TitleMolecular mechanisms of Taxane resistance in prostate cancer

Abstract: Taxanes act by stabilizing microtubules (MTs) and prolong survival in men with prostate cancer (PCa), but biomarkers predictive of responses and clinically actionable mechanism(s) of resistance have yet to be identified. We recently reported that a decrease or absence of MT bundling, despite high levels of intratumoral taxanes, is a basis and a potential pharmacodynamic biomarker of taxane resistance. To determine the molecular basis for this impaired MT bundling, we treated docetaxel sensitive PCa models in vivo for multiple cycles until resistance, and found upregulation of FOXJ1 (a master transcription factor regulating tubulin associated proteins), as well as one of its downstream effector protein, TPPP3, in the resistant tumors. Moreover, mining of patient databases showed that amplification of the FOXJ1 gene is also associated with taxane exposure. Together these data implicate the FOXJ1-TPPP3 regulatory network in taxane resistance. In parallel with these in vivo studies, we have carried in vitro drug screens for agents that enhance responses to docetaxel in 3D/organoid culture. A prominent agent that emerged is a histone methyltransferase inhibitor. Our overall goals are to identify clinically meaningful mechanisms of taxane resistance, to develop therapeutic combinations that enhance efficacy and/or target these resistance mechanisms, and to identify biomarkers indicative of specific mechanisms. Our hypotheses, which are based on our published and preliminary data, are that one major mechanism for taxane resistance is upregulation of the FOXJ1-TPPP3 pathway, and that combination therapies can be developed that enhance taxane efficacy and delay or prevent the emergence of resistance. The specific aims are 1) Determine the effect of FOXJ1-TPPP3 regulatory network on microtubule dynamics, stability and target-engagement by taxanes in vitro and in vivo and 2) Identify effective combination therapies to enhance docetaxel responses and overcome taxane resistance. 
6/24/2021Mike Novack
(University of Texas at Austin)
Title3D Smectic Liquid Crystals

Abstract: Liquid crystals are an intermediate state of matter which flow like liquids but retain molecular ordering similar to that of crystals. Their physical properties make them ideal for a wide range of technological applications. The molecules in smectic liquid crystals form well-defined layers which slide across one another. In this talk we will discuss a model for smectics from the physics literature based on the minimization of a suitable energy and present recent results obtained jointly with Xiaodong Yan. No prior knowledge of liquid crystals or the relevant mathematics will be assumed.
7/01/2021Zheng Shi
(Rutgers University)
Title: Mechanics of biomolecular assemblies

Abstract: The mechanical properties of biomolecular assemblies play pivotal roles in many biological and pathological processes. In this talk, I’ll focus on two different self-assembled structures in cells: 1) the plasma membrane, which defines the boundary of a cell; and 2) protein condensates, which arise from liquid-liquid phase separation (LLPS) inside cells.
In the first part, I’ll discuss recent findings on how cell membranes respond to local mechanical perturbations. In most non-motile cells, local perturbations to membrane tension remain highly localized, leading to subcellular Ca2+ influx and vesicle fusion events. Membrane-cortex attachments are responsible for impeding the propagation of membrane tension. Exception to this rule can be found in the axon of neurons, where a rapid propagation of membrane tension coordinates the growth and branching of the axon.

In the second part, I’ll discuss the development of quantitative techniques to measure the surface tension and viscosity of liquid protein condensates. Our results highlight a common misconception about LLPS in biology: ‘oil droplets in water’ is often used to give an intuition about protein condensates in cells. However, oil droplets and protein condensates represent two extremes in the realm of liquid properties. The unique properties of protein condensates have important implications in achieving molecular and functional understanding of LLPS.
7/08/2021Arun Debray
(University of Texas at Austin)
Title: Modeling invertible topological phases of matter using homotopy theory

Abstract: Condensed-matter theorists have discovered examples of physical systems with unusual behavior, such as pointlike excitations that behave neither as bosons nor as fermions, leading to the idea of topological phases of matter. Classifying the possible topological phases has been the focus of a lot of research in the last decade in condensed-matter theory and nearby areas of mathematics. In this talk, I’ll focus primarily on the special case of invertible phases, also called symmetry-protected topological (SPT) phases, whose classification uses techniques from homotopy theory. I will discuss two different approaches to this, due to Kitaev and Freed-Hopkins, followed by details of the homotopy-theoretic classifications. The latter includes work of Freed-Hopkins and of myself.
7/15/2021Daniel Kaplan (University of Birmingham)Title: Representations of quivers and the Deligne-Simpson problem

Abstract: A quiver is a directed graph and a representation of a quiver is an assignment of a vector space to each vertex and a linear transformation to each arrow. Many problems in linear algebra can be rephrased in terms of representation theory of quivers. I will highlight one such instance: Crawley-Boevey’s solution to the (additive) Deligne-Simpson problem. Following work of Nakajima, geometers have gained milage by realizing spaces as the space of certain representations of quivers (with relations). For instance, this gives a candidate resolution of singularities using the theory of variation of GIT. Time permitting, I will explain these developments.
7/22/2021Yinbang Lin (Tongji University)Title: Moduli spaces of stable pairs on algebraic surfaces

Abstract: As a variant of Grothendieck’s Quot schemes, we introduce the moduli space of limit stable pairs. We show an example over a smooth projective algebraic surface where there is a virtual fundamental class. We are able to describe this class explicitly. We will also show an application towards moduli of sheaves. 
7/29/2021Jeffrey Kuan
(Texas A&M University)
Title: Joint moments of multi–species $q$–Boson.

Abstract: The Airy_2 process is a universal distribution which describes fluctuations in models in the Kardar–Parisi–Zhang (KPZ) universality class, such as the asymmetric simple exclusion process (ASEP) and the Gaussian Unitary Ensemble (GUE). Despite its ubiquity, there are no proven results for analogous fluctuations of multi–species models. Here, we will discuss one model in the KPZ universality class, the $q$–Boson. We will show that the joint multi–point fluctuations of the single–species $q$–Boson match the single–point fluctuations of the multi–species $q$–Boson. Therefore the single–point fluctuations of multi–species models in the KPZ class ought to be the Airy_2 process. The proof utilizes the underlying algebraic structure of the multi–species $q$–Boson, namely the quantum group symmetry and Coxeter group actions.
8/5/2021Weishan Huang (Louisiana State University and Cornell University)Title: Designer DNA-based nanoconstructs in viral detection and blocking

Abstract: SARS-CoV-2, etiological pathogen of COVID-19, has resulted in a pandemic. There remains an urgent need of innovative technology of developing rapid diagnosis of active infections and affordable antiviral precise medicine for therapeutics. Most viruses have repetitive surface antigen units laid out on the virions following specific patterns, forming the viral capsid or envelop. To develop precise instant diagnosis of active SARS-CoV-2 infections and novel antiviral candidates against SARS-CoV-2 infection and transmission, we exploited the structural characteristics of viral surface proteins that can be matched at nanoscale precision by engineered DNA nanostructure platforms. Our preliminary data demonstrated that these pattern-matching DNA nanostructures can enable specific and sensitive sensing of SARS-CoV-2 viruses and have sufficient antiviral activities against SARS-CoV-2 pseudoviral and live viral infections. Our method can be transferrable to develop rapid diagnosis and precise inhibition of other enveloped viruses such as influenza and HIV. We are seeking expert advice from the mathematical and computational community to help with optimization of the DNA-based nanostructures.
8/12/2021Qingtao Chen (New York University Abu Dhabi)Title: Recent Progress on Volume Conjectures of links as well as 3-manifolds

Abstract: The original Volume Conjecture of Kashaev-Murakami-Murakami predicts a precise relation between the asymptotics of the colored Jones polynomials of a knot in S^3 and the hyperbolic volume of its complement. I will discuss two different directions that lead to generalizations of this conjecture. The first direction concerns different quantum invariants of knots, arising from the colored SU(n) (with the colored Jones polynomial corresponding to the case n=2). I will first display subtle relations between congruence relations, cyclotomic expansions and the original Volume Conjecture for the colored Jones polynomials of knots. I will then generalize this point of view to the colored SU(n) invariant of knots. Certain congruence relations for the colored SU(n) invariants, discovered in joint work with K. Liu, P. Peng and S. Zhu, lead us to formulate cyclotomic expansions and a Volume Conjecture for these colored SU(n) invariants. If time permits, I will briefly discuss similar ideas for the Superpolynomials that arise in HOMFLY-PT homology. 

Another direction for generalization involves the Witten-Reshetikhin-Turaev and the (modified) Turaev-Viro quantum invariants of 3-manifolds. In a joint work with T. Yang, I formulated a Volume Conjecture for the asymptotics of these 3-manifolds invariants evaluated at certain roots of unity, and numerically checked it for many examples. Interestingly, this conjecture uses roots of unity that are different from the one usually considered in literature. These 3-manifolds invariants are only polynomially large at the usual root of unity as the level of the representation approaches infinity, which is predicted by Witten’s Asymptotic Expansion Conjecture. True understanding of this new phenomenon requires new physical and geometric interpretations that go beyond the usual quantum Chern-Simons theory. Currently these new Volume Conjectures have been proved for many examples by various groups. However, like the original Volume Conjecture, a complete proof for general cases is still an open problem in this area. In a recent joint work with J. Murakami, I proved the asymptotic behavior of the quantum 6j-symbol evaluated at the unusual root of unity, which could explain the Volume Conjectures for the asymptotics of the Turaev-Viro invariants in general. 
8/19/2021Sean Carney (UCLA)Title: Fluctuating hydrodynamics model for ionic liquids

Abstract: An ionic liquid is a liquid salt with dissociated cations and anions such as molten NaCl. Of particular interest are ionic liquids composed of complex hydrocarbons that are high-viscosity liquids at room temperature. These room temperature ionic liquids (RTILs) can exhibit intriguing physical properties useful in applications such as super-capacitors, batteries, photoelectrochemical cells, and microelectromechanical lubricants. RTIL dynamics are governed by the thermodynamic competition between electrostatic forces, interfacial tension, and short range cation-anion repulsion due to the ions’ complex molecular structures. This competition is quantified by a Gibbs free energy functional from which we derive a PDE model of RTILs. The inviscid hydrodynamics and electrostatics can be derived from the calculus of variations, while the evolution equation for ion concentration and the viscous hydrodynamic force density are derived from the Onsager reciprocal relations of nonequilibrium thermodynamics. The PDEs are further augmented with white noise flux terms modeling thermal fluctuations intrinsic to fluid mixtures at mesoscopic length scales, and a low Mach number formulation is utilized to analytically remove sound waves from the equations of motion based on the assumption that they are relatively unimportant to the system dynamics.
After calibrating the strength of the short range repulsive term based on a stability analysis of the concentration equation, we numerically demonstrate the formation of mesoscopic structuring at equilibrium in two and three dimensions. In simulations with electrode boundaries the measured double layer capacitance decreases with voltage, in agreement with theoretical predictions and experimental measurements for RTILs. Finally, we present a shear electroosmosis example to demonstrate that the methodology can be used to model electrokinetic flows.

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