Past Seminars

Abstract:

Calogero-Sutherland models are a class of completely integrable (quantum) many-body systems. In the most general case, particles interact pairwise through an elliptic potential. We discuss the second quantization of several elliptic Calogero-Sutherland models. In degenerate (trigonometric, rational) cases, there is a well-studied correspondence between Calogero-Sutherland systems and Benjamin-Ono equations, a nonlinear, integrable integro-differential equation. We discuss the correspondence in the elliptic regime, where second quantization of a particular system leads to a new two-component elliptic Benjamin-Ono equation. We construct a Lax pair for this equation via a Riemann-Hilbert problem on the torus. This is joint work with Edwin Langmann and Jonatan Lenells.

Abstract:

There are many papers concerning 3rd and higher order superintegrable systems on 2D Euclidean space, particularly by Pavel Winternitz, his students and collaborators. However, we are not aware of similar studies on nonflat spaces. We decided to study the 2-sphere (with separation in polar coordinates) and the 2-hyperboloid (with separation in horospherical coordinates). The computations were very complex for these nonflat systems and the results less rich than for flat space, which is to be expected. In each case we have a Hamiltonian operator H a 2nd order symmetry operator A and compute a 3rd order symmetry operator B.
We classified the potentials which yielded

a nonzero B. They fall into 4 classes: 1) Systems that are 2nd order superintegrable, 2) Systems that are 3rd order superintegrable but do not generate a cubic algebra, 3) Systems with algebraically dependent generators A and B, 4) Systems that are 3rd order superintegrable with a cubic algebra.

We pay special attention to the class 3 cases which also occur for Euclidian space with little change. We call these functionally dependent superintegrable systems, and show that they always permit a computation of the A eigenfunctions by simple quadrature. For the hyperboloid we show that the TTW method applied to a 2nd order
superintegrable system in horospherical coordinates yields a 3rd order superintegrable system.

Abstract:

We discuss the facial weak order, a poset structure that extends the poset of regions on a central hyperplane arrangement to the set of all faces of the arrangement which was first introduced on the braid arrangements by Krob, Latapy, Novelli, Phan and Schwer. We provide various characterizations of this poset including a global one, a local one, one using covectors and a geometric one using the associated zonotope. We then show that the facial weak order is in fact a lattice for simplicial hyperplane arrangements, generalizing a result by Björner, Edelman and Zieglar showing the poset of regions is a lattice for simplicial arrangements. We end by stating some properties on the facial weak order.

Abstract:

We study the problem of minimizing a strongly convex and smooth function when we have noisy estimates of its gradient. We propose a novel multistage accelerated algorithm that is universally optimal in the sense that it achieves the optimal rate both in the deterministic and stochastic case and operates without knowledge of noise characteristics. The algorithm consists of stages that use a stochastic version of Nesterov's accelerated algorithm with a specific restart and parameters selected to achieve the fastest reduction in the bias-variance terms in the convergence rate bounds.

Mert Gürbüzbalaban is an assistant professor at Rutgers University. Previously, he was a postdoctoral associate at the Laboratory for Information and Decision Systems (LIDS) at MIT. He is broadly interested in optimization and computational science driven by applications in large-scale information and decision systems. He received his B.Sc. degrees in Electrical Engineering and Mathematics as a valedictorian from Bo?aziçi University, Istanbul, Turkey, the “Diplôme d’ingénieur” degree from École Polytechnique, France, and the M.S. and Ph.D. degrees in (Applied) Mathematics from the Courant Institute of Mathematical Sciences, New York University.

Dr. Gürbüzbalaban received the Kurt Friedrichs Prize (given by the Courant Institute of New York University for an outstanding thesis) in 2013, Bronze Medal in the École Polytechnique Scientific Project Competition in 2006, the Nadir Orhan Bengisu Award (given by the electrical-electronics engineering department of Bo?aziçi University to the best graduating undergraduate student) in 2005 and the Bülent Kerim Altay Award from the Electrical-Electronics Engineering Department of Middle East Technical University in 2001. He received funding from a variety of sources including multiple programs at the U.S. National Science Foundation.

Abstract:

The (affine cone over the) Grassmannian is a prototypical example of a variety with "cluster structure"; that is, its coordinate ring is a cluster algebra. Scott (2006) gave a combinatorial description of this cluster algebra in terms of Postnikov's plabic graphs. It has been conjectured essentially since Scott's result that Schubert varieties also have a cluster structure with a description in terms of plabic graphs. I will discuss recent work with K. Serhiyenko and L. Williams proving this conjecture. The proof uses a result of Leclerc, who shows that many Richardson varieties in the full flag variety have cluster structure using cluster-category methods, and a construction of Karpman to build plabic graphs for each Schubert variety. Time permitting, I will also discuss our results on cluster structures on a larger class of positroid varieties, which involve the combinatorics of "generalized" plabic graphs.

Abstract:

This is joint with Maury Bramson, who introduced our work last week and then spoke more particularly about the existence of uncountably many mutually singular equilibria in the majority vote process on a tree of vertex degree 5 and higher, whenever the noise rate is sufficiently small. Each equilibrium is associated with a different fixed point of the majority vote operation.

Natural question: If the system starts at (or near) one of these fixed points and the noise rate is small, does it converge to the corresponding equilibrium, and if so, how fast? Answering this question can tell us much about the properties of these equilibria, but standard techniques do not apply. I will describe a new method, based on the "graphical construction" of these systems, which shows exponentially quick convergence to equilibrium when the noise rate is sufficiently small. These methods apply to other models on trees, such as the stochastic Ising model, for which the convergence result was previously unknown.

This lecture is self-contained; essential features from the previous week will be included (with some new pictures).

Abstract:

Whether the 3D incompressible Euler equations can develop a finite time singularity from smooth initial data is a long-standing open question in mathematical fluid dynamics. Recent computations have provided strong numerical evidence that the 3D Euler equations develop a finite time singularity from smooth initial data. I will report some recent progress in providing a rigorous justification of the singularity formation in the 3D Euler equations and related models.

Abstract:

We will give less precise but hopefully very motivational introduction to Langlands philosophy with many examples. A serious background in anything is not essential.

Abstract:

We show that certain sets of representations of Dynkin quivers are determined by their Jordan form data. The description is phrased in terms of the bounded derived category of representations of the quiver. This continues the study of the paper of Garver, Patrias and Thomas: Minuscule reverse plane partitions via quiver representations.

Abstract:

In many practical applications, we often need to provide solutions to quantities of interest to a large-scale problem but with only subsampled data and partial information of the physical model. Traditional PDE solvers cannot be used directly for this purpose. On the other hand, many powerful techniques have been developed in data science to represent and compress data for useful information. A crucial factor for the success of these methods is to exploit some low rank or sparsity structures in these high-dimensional data. In this talk, we will describe our recent effort in developing effective numerical methods to solve multiscale problems using subsampled data. The PDE analysis will help reveal certain important solution structures so that we can use techniques from data science to give accurate approximations for these quantities of interest.

Thomas Yizhao Hou is the Charles Lee Powell professor of applied and computational mathematics at Caltech. His research interests are centered around developing mathematical analysis and effective computational methods for vortex dynamics, interfacial flows, multiscale problems and data analysis. He received his Ph.D. in mathematics from UCLA in 1987, and joined the Courant Institute as a junior faculty member in 1989. He moved to Caltech in 1993 and was named the Charles Lee Powell Professor in 2004. He was the founding Editor-in-Chief of the SIAM Journal on Multiscale Modeling and Simulation from 2002 to 2007 and served on the IMA Board of Governors from 2010 to 2014. Dr. Hou is a Fellow of American Academy of Arts and Sciences, a SIAM Fellow and an AMS Fellow.

Abstract:

This talk focuses on generalized Bott-Samelson resolutions of Schubert varieties. These resolutions are iterated G/P-bundles (for different parabolic subgroups P of the algebraic group G). Special cases have appeared in the work of Zelevinsky, Wolper, Ryan and several other authors. After introducing these resolutions and some of their properties, we discuss a possible best resolution for a given Schubert variety X(w), based on a combinatorial condition on the inversion set of the Weyl group element w. If time permits, we will discuss a computer program that calculates local intersection cohomology (i.e., Kazhdan-Lusztig polynomials) from these resolutions. This is joint work with Jennifer Koonz.

Abstract:

The majority vote process was one of the first interacting particle systems to be investigated and can be described as follows. There are two possible opinions at each site and that opinion switches randomly to the majority opinion of the neighboring sites. Also, at a different rate epsilon, the opinion at each site randomly changes due to noise.

Despite its simple dynamics, the majority vote process is difficult to analyze. In particular, on Z^d with d>1 and epsilon chosen small, it is not known whether there exists more than one equilibrium. This is surprising due to the close analogy between the majority vote process and the Ising model.

Here, we discuss work with Larry Gray on the majority vote process on the infinite tree with vertex degree d, where it is shown that, for small noise, there are uncountably many mutually singular equilibria, and that convergence to equilibrium occurs exponentially quickly from nearby initial states. Our methods rely on graphical constructions; they are quite flexible and can be used to obtain analogous results for other models, such as the stochastic Ising model on a tree.

This is the first part of a two-lecture presentation, and will concentrate on background and on existence of equilibria. Larry Gray will give the second part the following week, which will concentrate on convergence.

Abstract:

A celebrated result of Waldspurger from the eighties express the central value of certain base-change L-functions for $GL_2$ as toric periods of modular forms or generalizations thereof. In the mid 2000s Gan, Gross and Prasad have formulated conjectural generalizations to higher rank classical groups relating the non-vanishing of central values of certain automorphic L-functions to the non-vanishing of certain explicit integrals of automorphic forms that are called 'automorphic periods'. These predictions have been subsequently refined by Ichino-Ikeda and N.Harris into precise identities relating the two invariants. These conjectures also have local counterparts which concern certain branching laws in the representation theory of real or p-adic groups. Most of these conjectures have now been established for unitary groups. This talk aims to give an introduction to this circle of ideas and to review recent results on the subject.

Abstract:

Come hear recent graduates and current grad students discuss their experiences navigating the job market. This panel is hosted by the Student Number Theory seminar, but all graduate students are welcome to attend.

Abstract:

We continue reviewing minuscule weights, their weight posets, and minuscule heaps, with the goal of eventually understanding Section 4 of the paper by Garver, Patrias and Thomas

Abstract:

The Jacquet-Rallis fundamental lemma is a local identity between (relative) orbital integrals which originates from the relative trace formula approach to the Gan-Gross-Prasad conjecture for unitary groups and is a crucial ingredient in the recent results of W. Zhang on this conjecture. It was established soon after its formulation by Z. Yun in positive characteristic using the same geometric ideas as in Ngô's proof of the endoscopic fundamental lemma and transferred to characteristic 0 by J. Gordon by model-theoretic techniques. In this talk, I will present an alternative proof of this fundamental lemma in characteristic zero which is purely local and based on harmonic analytic tools. We note that a third proof of this fundamental lemma has been recently proposed by W. Zhang through a similar although global argument.

Abstract:

We consider solutions of the Navier-Stokes equations in 3d with vortex filament initial data of arbitrary circulation, that is, initial vorticity given by a divergence-free vector-valued measure of arbitrary strength supported on a smooth curve. First, we prove global well-posedness for perturbations of the Oseen vortex column in scaling-critical spaces. Second, we prove local well-posedness (in a sense to be made precise) when the filament is a smooth, closed, non-self-intersecting curve. Besides their physical interest as a model for the coherent vortex filament structures observed in 3d fluids, these results are the first to give well-posedness (in a certain sense) in a neighborhood of large self-similar solutions of 3d Navier-Stokes, as well as solutions which are locally approximately self-similar. Joint work with Pierre Germain and Benjamin Harrop-Griffiths.

Abstract:

We recall the self-homeomorphisms of a closed oriented reducible 3-manifold. Using this description, we discuss various problems in low-dimensional topology and dynamics, such as the existence of Anosov tori in 3-manifolds (joint work with Shicheng Wang), the simplicial volume of mapping tori of 3-manifolds (joint work Michelle Bucher) and the virtual Betti numbers of mapping tori of 3-manifolds.

Abstract:

Adrienne Sands of U of MN will continue on "Classical properties of Epstein zeta functions

Abstract:

Neural networks solve complex computer vision problems with human-like accuracy. However, it has recently been observed that neural nets are easily fooled and manipulated by "adversarial examples," in which an attacker manipulates the network by making tiny changes to its inputs. In this talk, I give a high-level overview of adversarial examples, and then discuss a newer type of attack called "data poisoning," in which a network is manipulated at train time rather than test time. Then, I explore adversarial examples from a theoretical viewpoint and try to answer a fundamental question: "Are adversarial examples inevitable?"

Tom is an Assistant Professor at University of Maryland. His research lies at the intersection of optimization and distributed computing, and targets applications in machine learning and image processing. He designs optimization methods for a wide range of platforms. This includes powerful cluster/cloud computing environments for machine learning and computer vision, in addition to resource limited integrated circuits and FPGAs for real-time signal processing. Before joining the faculty at Maryland, he completed his PhD in Mathematics at UCLA, and was a research scientist at Rice University and Stanford University. He has been the recipient of several awards, including SIAM’s DiPrima Prize, a DARPA Young Faculty Award, and a Sloan Fellowship.

Abstract:

Two Teams of MFM students from the 2019 MFM Modeling Workshp will present their work to the Seminar. Each group will take a half hour to cover their topics, including Q & A

Yield Curve Construction: Calculating the present value of future cash flows is a crucial task for any trading desk. We will explore yield curve bootstrapping and interpolation techniques used to value financial instruments in a consistent framework. The pros and cons of each method will become apparent, and we will converge on an optimal technique that is consistent with major trading desks. Finally, we will see how the financial crisis changed yield curve construction, and fundamentally altered the way we think of the valuing future cash flows.

Valuation and Replication Strategies for Variance Derivatives:Variance derivatives have played a major role in the financial markets since the 1990s, as they provide pure exposure to volatility without other added effects. A primary instrument is variance swaps. In this project the participants will explore traditional theory of valuing variance swaps, and their continuous and discrete replication with vanilla derivatives. The connections among variance, volatility swaps, and VIX derivatives will be drawn, alongside examining non-parametric variance swap replication approaches.

Abstract:

In preparation for my oral exam, I will extend the existing connections between quantum groups and the study of spherical Whittaker models on metaplectic covering groups of GL(r,F), for F a nonarchimedean local field. Brubaker, Buciumas, and Bump showed that for a certain metaplectic n-fold cover of GL(r,F) a set of Yang-Baxter equations govern the behavior of the Whittaker functions and that these equations arise from a Drinfeld twist of a quantum affine Lie superalgebra. I will extend their results to all metaplectic covers of GL(r,F), showing that the same Yang-Baxter equations underlie the scattering matrix for the Whittaker functions over an nQ-fold metaplectic cover, where nQ is an integer determined by the cover, and that these equations arise from Drinfeld twists of quantum groups. (I will also define what most of these words mean.)

Abstract:

We review minuscule weights, their weight posets, and minuscule heaps, with the goal of eventually understanding Section 4 of the paper by Garver, Patrias and Thomas.

Abstract:

In this talk we discuss Vlasov-Poisson-Boltzmann system in bounded domains. Some recent results on regularity and large time behavior of solutions will be presented.

Abstract:

In experimental systems involving diffusing and convecting vapors that react to form solid particulates, a complex sequence of nucleation and growth reactions produces pulsing charged crystals, oscillating fronts, and patterns such as beautiful 3-dimensional structures that we call “microtornadoes”, “microstalagtites”, and “microhurricanes”. We will review the rich history of these experiments, starting with a counterdiffusional experiment that figures in the pioneering work on diffusion of Dalton, Graham, Fick, and Stefan. Mathematical analysis will progress from a reaction-diffusion model for the origins of the initial reaction zone, to an analysis of oscillations and particle size distributions, to a fluid dynamical model. The insights carry over to similar structures in protein crystallization experiments and the formation of periodic structures in plants.

Abstract:

In this talk, we consider decision-making problems under data uncertainty. In particular, we study a framework, called Wasserstein distributionally robust optimization, that aims to find a decision that hedges against a set of distributions that are close to some nominal distribution in Wasserstein metric. This framework, although being an infinite dimensional optimization, has a finite-dimensional tractable reduction in various data-driven settings by virtue of duality, and is closely related to many regularization problems in statistical learning. I will discuss the generalization error bound and asymptotic properties of such framework, and compare it with other distributional uncertainty sets including divergence-based and moment-based sets. If time permits, I will talk about an application of the framework to robust classification with limited information.

Rui Gao is an Assistant Professor of Information, Risk, and Operations Management at the McCombs School of Business at the University of Texas at Austin. He received his Ph.D. in Operations Research from Georgia Institute of Technology in 2018. His current research interests lie in the intersection of decision-making under uncertainty and statistical learning, as well as their applications in data analytics. His work has been recognized with several INFORMS prize, including finalist in Nicholson student paper competition in 2016, runner-up in the Computing Society student paper prize in 2017, and winner of the Data Mining best paper award in 2017.

Abstract:

In a recent beautiful but technical article, William Y.C. Chen, Qing-Hu Hou, and Doron Zeilberger developed an algorithm for finding and proving congruence identities (modulo primes) of indefinite sums of many combinatorial sequences, namely those (like the Catalan and Motzkin sequences) that are expressible in terms of constant terms of powers of Laurent polynomials. We first give a leisurely exposition and then extend it in two directions. The Laurent polynomials may be of several variables, and instead of single sums we have multiple sums.

Abstract:

From "is networking really important" to "do hiring managers read cover letters", from "why should data exploration begin with a question" to "how to build a culture of data driven decision making", we have heard it all many, many times. But these are questions that warrant a reminder. And another. And another.

I hope to be that person and remind you of a few key strategies to employ in job search and best practices to avoid pitfalls in the business world. I will share failures and lessons from my experience in data management and analytics, as well as decode marketing function.

Sandhya Surapaneni (aka Sandy) works as a Global Marketing Analytics Leader at Stratasys, a pioneer in 3D printing technology. Her current focus is on building a foundation in analytics to integrate data-driven insights into every day business decisions that align with the company's long term strategy. Prior to joining Stratasys, Sandy worked as a Database Lead at BI Worldwide, building and supporting the data layer of several web applications. Sandy has a M.S in Computer Science from the University of Texas at Arlington and a M.S in Business Analytics from Carlson School of Management. Born and raised in India, she has been a Minneapolis resident for 13 years and enjoys its lakes and coffee shops.

Abstract:

I will discuss the asymptotic non-vanishing of syzygies for products of projective spaces, generalizing the monomial methods of Ein-Erman-Lazarsfeld. This provides the first example of how the asymptotic syzygies of a smooth projective variety whose embedding line bundle grows in a semi-ample fashion behave in nuanced and previously unseen ways.

Abstract:

Lagrangian chaos refers to the chaotic behavior of
Lagrangian trajectories in a fluid. This chaotic behavior often
characterized by the dynamics having a positive Lyapunov exponent,
namely the property that initially close trajectories will separate at
an exponential rate after long time. In this talk we will consider a
variety of stochastically forced fluid models, including the 2
dimensional stochastic Navier-Stokes equations, and show that under
certain non-degeneracy conditions on the noise, the Lagrangian flow
possesses a positive Lyapunov exponent. The proof crucially uses the
theory of random dynamical systems as well as tools from Malliavin
calculus and control theory to satisfy a certain non-degeneracy
criterion originally due to Furstenberg. We will explore several
important consequences of a positive exponent for passive scalars
advected by the fluid. Most importantly, we show that these velocity
fields are almost sure exponential mixers. Specifically, using the
positive Lyapunov exponent, we obtain almost sure exponential decay in
time of passive scalars in any negative Sobolev norm through a
detailed study of ergodicity of the two point Lagrangian motion. This
work is joint with Jacob Bedrossian and Alex Blumenthal.

Abstract:

Serre and Swinnerton-Dyer reproved many congruence relations around 1970 for the Ramanujan tau function by reinterpreting the relations in terms of l-adic representations. We will give an introduction to this topic.

Abstract:

We describe how the Jordan form data of quiver representations interact with reflection functors, continuing with the paper of Garver, Patrias and Thomas: Minuscule reverse plane partitions via quiver representations.

Abstract:

In the absence of analytical reconstruction methods, numerical solutions of nonlinear inverse problems have been mostly based on least-square formulations where solutions are sought by minimizing the $L^2$ difference between model predictions and measured data. We present here recent computational studies of some nonlinear inverse problems where quadratic Wasserstein distances are used to measure the discrepancy between model predictions and measured data. Related numerical and theoretical issues will be discussed.

Abstract:

Modern massive datasets pose an enormous computational burden to practitioners. Distributed computation has emerged as a universal approach to ease the burden: Datasets are partitioned over machines, which compute locally, and communicate short messages. Distributed data also arises due to privacy reasons, such as with medical databases. It is important to study how to do statistical inference and machine learning in a distributed setting. In this talk, we present results about one-step parameter averaging in statistical linear models under data parallelism. We do linear regression on each machine, and take a weighted average of the parameters. How much do we lose compared to doing linear regression on the full data? Here we study the performance loss in estimation error, test error, and confidence interval length in high dimensions, where the number of parameters is comparable to the training data size. We discover several key phenomena. First, averaging is not optimal, and we find the exact performance loss. Second, different problems are affected differently by the distributed framework. Estimation error and confidence interval length increases a lot, while prediction error increases much less. These results match numerical simulations and a data analysis example. To derive these results, we rely on recent results from random matrix theory, where we also develop a new calculus of deterministic equivalents as a tool of broader interest.

Edgar Dobriban is an assistant professor of statistics at the Wharton School of the University of Pennsylvania. He obtained his PhD in Statistics in 2017 from Stanford University, advised by David Donoho, and his undergraduate degree in mathematics from Princeton University in 2012. His research interests are in developing statistical methods and theory for large-scale data analysis.

Abstract:

Two Teams of MFM students from the 2019 MFM Modeling Workshop will present their work to the Seminar. Each group will take a half hour to cover their topics, including Q & A

Machine Learning in Equity Classification: This MFM modeling workshop team worked with various machine learning classification models with the goal of classifying equities via well-known quantitative factors such as Value and Momentum. The classification was supervised, utilizing a novel ETF dataset which was supplemented extensively. The team worked in Python, especially the Scikit Learn module. They will present their project to seminar attendees

Smart Beta Investing in Commodities: Ever since the first stocks and bonds were issued by the Dutch East India Company (VOC), investors have tried to understand what drives returns. Smart Beta strategies have gained popularity lately by offering the potential for better-than-market returns with better-defined risks, especially after the recognition in 2008 that multi-asset classes can experience severe losses at the same time despite their apparent intrinsic differences. Smart beta strategies can take many different forms, with a variety of objectives. They can simply aim at reducing risks (the “risk-based approach”) or enhancing return through exposure to systematic factors (the “factor-based” approach). In commodities investing, alternative index movement was born from frustration with the inherent biases of conventional indices. For example, negative commodity “roll yields” can erode returns by as much as 50%. This team explored the opportunities of constructing a commodity investment portfolio that uses different smart beta approaches to seek enhancing returns and risk reduction. Factors like curve, value, and momentum were examined in the back-test.

Abstract:

I was late. I only decided to find a job in industry in the 4th year of my graduate study even though I never had a job outside of academia. The one question that I kept asking was: how do you get a job?

It was scary because I did't know what to do. But I ended up getting a job. And today, I think I'm doing fine.

If you are about to start on the path to industry but you feel that you are full of questions and doubts, then this talk may be for you. In this talk, I will show you two things:

1. my experience of going into industry, and
2. how to thrive in it.

You will walk away with guidelines and tips that I learned from my mistakes and observations. It will help you save time and avoid frustrations in the process.

Chang currently works as a data scientist at Lowe's. He entered into the data science field as an intern for a baseball team, the Tampa Bay Rays. He got his Ph.D in Mathematics from Vanderbilt University in 2017 under Alex Powell. Besides math and data, Chang has become a soul food advocate and survives on barbecue and hot chicken. You can learn more about his experience outside of academia at his blog: https://changhsinlee.com and his YouTube channel: https://youtube.com/c/softwareforscience

Abstract:

Bernstein-Sato polynomials are fundamental in D-module theory. For
example, they are the main finiteness ingredient in the construction of
nearby cycles. We will present a positive characteristic analogue of the
Bernstein-Sato polynomials. After diving in the world of characteristic
p D-modules, we shall consider how our construction varies with the
prime p. This turns out to be related to questions of Hodge theory and
Poisson homology.

Abstract:

Multi-scale kinetic equations can be compressed: in certain regimes, the Boltzmann equation is asymptotically equivalent to the Euler equations, and the radiative transfer equation is asymptotically equivalent to the diffusion equation. A lot of detailed information is lost when a system passes to the limit. In linear algebra, it is equivalent to a system being of low rank. I will discuss such transition and how it affects the computation: mainly, in the forward regime, inserting low-rankness could greatly advances the computation, while in the inverse regime, the system being of low rank typically makes the problems significantly harder.

Abstract:

A quiver Grassmannian is a variety parametrizing subrepresentations of a given quiver representation. Reineke has shown that all projective varieties can be realized as quiver Grassmannians. In this talk, I will study a class of smooth projective varieties arising as quiver Grassmannians for (truncated) preprojective representations of an n-Kronecker quiver, i.e. a quiver with two vertices and n parallel arrows between them. The main result I will present is a recursive construction of cell decompositions for these quiver Grassmannians motivated by the theory of rank two cluster algebras. If there is time I will discuss a combinatorial labeling of the cells by which their dimensions may conjecturally be directly computed. This is a report on joint work with Thorsten Weist.

Abstract:

Spin glasses are disordered spin systems initially invented by theoretical physicists with the aim of understanding some strange magnetic properties of certain alloys. In particular, over the past decades, the study of the Sherrington-Kirkpatrick (SK) mean-field model via the replica method has received great attention. In this talk, I will discuss another approach to studying the SK model proposed by Thouless-Anderson-Palmer (TAP). I will explain how the generalized TAP correction appears naturally and give the corresponding generalized TAP representation for the free energy. Based on a joint work with D. Panchenko and E. Subag.

Abstract:

In this talk, we will be concerned with the average values of certain random functionals of the path of a stochastic process during a given time period. High-order asymptotic characterizations of such values when the time period shrinks to 0 have a wide range of applications. In statistics, they are instrumental in establishing infill asymptotic properties of high-frequency based statistical methods of stochastic processes. In finance, they have been used as model selection and calibration tools based on near expiration option prices. In some Engineering problems, they also show up as a method to solve a problem in continuous time by looking at the analogous problem in discrete time and shrinking the time step to 0. These short-time asymptotic methods are especially useful in the study of complex models with jumps and stochastic volatility due to the lack of tractable formulas and efficient statistical and numerical procedures. In this talk, I will discuss some recent advances in the area and illustrate their broad relevance in several contexts.

Bio: Dr. Figueroa-López is a Professor in the Department of Mathematics and Statistics at Washington University in St. Louis. He currently serves as part of the executive committee and the chair of the statistics committee of the Department. Formerly he was Associate Professor of Statistics at Purdue University, where he served as Associate Director of the Computational Finance Program and as a member of the University Senate. Professor Figueroa’s ongoing research includes short-time asymptotics of jump-diffusion models, diffusion limits of Limit Order Book models, optimal limit order placement problems, market making via reinforced learning, and optimal tuning of high-frequency based econometric methods. He was awarded the NSF career award in 2012 and currently has two active NSF grants on the interplay of finance, statistics, and probability. He is an Associate Editor of the SIAM Journal on Financial Mathematics (SIFIN) and a former Associate Editor of Electronic Journal of Statistics.

Abstract:

We review the analogy between knots in 3-manifolds and prime ideals in number rings, and push it further to realize the Legendre symbol as the analogue of the (mod 2) linking number. This talk should be accessible even if you did knot attend seminar last week.

Abstract:

This talk is postponed to March 27.

Abstract:

A theorem of Deligne says that compatible systems of l-adic sheaves on a smooth curve over a finite field are compatible along the boundary. I will present an extension of Deligne's theorem to schemes of finite type over the ring of integers of a local field. This has applications to the equicharacteristic case of some conjectures on l-independence. I will also discuss the relationship with compatible wild ramification. This is joint work with Qing Lu.

Abstract:

Principal Components Analysis (PCA), a.k.a. subspace learning, is one of the most widely used dimension reduction techniques that attempts to find a low-dimensional subspace approximation of a given dataset. PCA is a solved problem when the observed data is relatively clean and lies in (or close to) a low-dimensional subspace. However, in many modern applications, the data are often either incomplete (missing data) or corrupted by outliers. Robust PCA refers to this harder problem of PCA in the presence of entry-wise outliers (sparse corruptions). An important example application is video analytics when slow-changing videos are corrupted by foreground occlusions, e.g., by moving vehicles or persons. For long data sequences, e.g., long surveillance videos, if one tries to use a single subspace to represent the entire sequence, the required subspace dimension may be too large. For such data, a better model is to assume that the data subspace can change with time, albeit gradually. This problem of tracking data lying in a slowly changing subspace, while being robust to additive sparse outliers is referred to as Robust Subspace Tracking (RST). While robust PCA has received a lot of attention in the last decade, its dynamic version, RST, was largely open until recently. In a recent body of work, we have introduced the first provably correct and practically usable online solution framework for RST that we call Recursive Projected Compressive Sensing (ReProCS). Our most recent work from ICML 2018 shows that a simple ReProCS-based algorithm provides a provably fast and nearly (delay and memory) optimal RST solution under mild assumptions: weakened standard robust PCA assumptions and subspace change that is slow enough compared to the smallest magnitude outlier entry. Our theoretical claims are also backed by extensive experimental evidence for two video applications.

In new work, we have looked at what can be called the ``Phaseless PCA’’ problem. This involves recovering a low-rank matrix from phaseless (magnitude-only) linear projections of each of its columns. It finds important applications in dynamic phaseless imaging applications, such as dynamic sub-diffraction imaging or Fourier ptychography, involving recovering a set of slowly-changing images that together form an approximately low-rank matrix (with each vectorized image being one column). We introduce a simple alternating minimization solution that can provable recover the low-rank matrix with sa

Abstract:

On the Monday of February 5th 2018, VIX Index (measure of expected future volatility) spiked by 116% to 37.3, and massive turbulence was observed across global financial markets. During the talk, we will review this event and discuss on the following topics:

• How did we get there? --- Low volatility environment and the great snake of risk 
• What happened? --- 2017’s hottest trades went wrong, and several funds and firms (!) were effectively wiped out
• What did we learn? – Positioning, behavior, and never underestimate the risk of financial markets (convexity to volatility and VaR calcs)

Abstract:

Internship/job searching is a multifaceted process that can summon many questions along the way! During this workshop, we will aim to address your most asked questions about getting started with your search, including:
- Where to find internships/jobs to apply for
- What to look for and how to read an internship/job posting
- What you can be doing now and over the summer to help prepare for your search.
There will be time to discuss more questions during the session, so come with your questions in hand!

Whitney Moore has been a Career Counselor in the CSE Career Center at the University of Minnesota since the fall of 2011. Her passion lies in living a life of positivity and inspiring others to do the same, particularly while navigating career and leadership development. In addition to her work at the U of M, Whitney is a past President of the Minnesota College and University Career Services Association (MCUCSA) and holds degrees from Gustavus Adolphus College and Minnesota State University, Mankato. Away from campus, Whitney is motivated by running with her dog, competing in triathlon, eating baked goods, and exploring MN with her husband. As an utter positivist, you can also find her musings on living with positivity on Instagram at @ThePositivistExplorer!

Abstract:

We present a new method to derive quantitative estimates proving the propagation of chaos for large stochastic or deterministic systems of interacting particles. Our approach requires to prove large deviations estimates for non-continuous potentials modified by the limiting law. But it leads to explicit bounds on the relative entropy between the joint law of the particles and the tensorized law at the limit; and it can be applied to very singular kernels that are only in negative Sobolev spaces and include the Biot-Savart law for 2D Navier-Stokes and 2D Euler. Joint work with P.-E. Jabin.

Abstract:

A topological definition of an attractor leaves out metric information relevant to modeling real-world systems, particularly how far the attractor persists against perturbations and error. This talk will review some existing approaches to measuring the strength of an attractor in metric terms and will introduce the quantity “intensity” to generalize basin steepness to systems of autonomous ODEs in arbitrary dimension. One can compute an attractor’s intensity by probing a domain of attraction with bounded, non-autonomous control and tracking the sets reachable from the attractor. A connection between reachable sets and isolating blocks implies that an attractor’s intensity not only reflects its capacity to retain solutions under time-varying perturbations, but also gives a lower bound on the distance the attractor continues in the space of vector fields.
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Abstract:

Since its introduction in 2000, the locally linear embedding (LLE) has been widely applied as an unsupervised learning tool. However, only few hand-waiving arguments are available to explain what is going on before 2018. For the sake of scientific soundness, we provide a systematic analysis of LLE, particularly under the manifold setup. In this talk, several theoretical results will be discussed. (1) We derive the corresponding kernel function, which in general is asymmetric and and not form a Markov process. (2) The regularization is critical. Different regularizations lead to dramatically different results. If chosen correctly, asymptotically we obtain the Laplace-Beltrami operator even under nonuniform sampling. (3) It has an intimate relationship with the local covariance analysis and tangent bundle structure. (4) When the boundary is not empty, we run into an interesting mixed-type differential equation. (5) An ingredient of the kernel provides a novel way to detect boundary, and hence a new approach to derive the Laplace-Beltrami operator with the Dirichlet boundary condition. If time permits, its relationship with several statistical topics like the locally linear regression and error in variable will be discussed. This is a joint work with Nan Wu.

Abstract:

In the era of “big data”, analysts usually explore various statistical models or machine learning methods for observed data in order to facilitate scientific discoveries or gain predictive power. Whatever data and fitting procedures are employed, a crucial step is to select the most appropriate model or method from a set of candidates. Model selection is a key ingredient in data analysis for reliable and reproducible statistical inference or prediction, and thus central to scientific studies in fields such as ecology, economics, engineering, finance, political science, biology, and epidemiology. There has been a long history of model selection techniques that arise from researches in statistics, information theory, and signal processing. A considerable number of methods have been proposed, following different philosophies and exhibiting varying performances. The purpose of this talk is to bring an overview of them, in terms of their motivation, large sample performance, and applicability. I will provide practically relevant discussions on theoretical properties of state-of- the-art model selection approaches, and share some thoughts on controversial views on the practice of model selection.

Bio for Jie Ding: https://cla.umn.edu/statistics/news-events/story/new-faculty-member-char...

Abstract:

Virtual resolutions as defined by Berkesch, Erman, and Smith,
provide a more geometrically meaningful generalization of free
resolutions in the case of subvarieties of a toric variety. In this
setting I prove an analog of Hilbert's syzygy theorem for virtual
resolutions of monomial ideals in toric varieties subject to some mild
conditions.

Abstract:

We introduce the paper of Garver, Patrias and Thomas: Minuscule reverse plane partitions via quiver representations, starting with a review of aspects of quiver representations.

Abstract:

Network dynamical systems play an important role in many fields of science; whenever there are agents whose time evolution is linked through some interaction structure, we may view the system as a network and model it accordingly. However, despite their prevalence, network dynamical systems are in general not well understood. One can identify two reasons for this. First of all, many coordinate changes and other transformations from well-known dynamical systems techniques do not respect the underlying network structure. Second of all, despite this somewhat `ethereal' character, systems with a network structure often display behavior that is highly anomalous for general dynamical systems. Examples of this include very unusual bifurcation scenarios and high spectral degeneracies. As a possible explanation of this, it can be shown that a large class of network ODEs admit hidden symmetry, which may be discovered through the so-called fundamental network construction. In most cases, this underlying symmetry does not come from a group though, but rather from a more general algebraic structure such as a monoid or category. I will show how the fundamental network allows one to adapt techniques from dynamical systems theory to a network setting, and how some of the more unusual properties of networks may be explained. In doing so, I will mostly focus on spectral properties of linear network maps.
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Abstract:

We obtain the first strong coresets for the k-median and subspace approximation problems with sum of distances objective function, on n points in d dimensions, with a number of weighted points that is independent of both n and d; namely our coresets have size poly(k/eps). A strong coreset (1+eps)-approximates the cost function for all possible sets of centers simultaneously. We also give input sparsity time algorithms for computing these coresets, which are fixed parameter tractable in k/eps. We obtain the result by introducing a new dimensionality reduction technique for coresets that significantly generalizes earlier results for squared Euclidean distances to sums of p-th powers of Euclidean distances for constant p >= 1.

Joint work with Christian Sohler

Abstract:

Despite low premiums and high subsidies, farmers view crop insurance programs as a gamble. One explanation, in a revenue protection program, is that farmers exhibit loss aversion when premiums are just above coverage. Introducing a model for conditional loss aversion (CLA), in the context of cumulative prospect theory, it can be shown that the introduction of a forgivable premium can remove the producers loss aversion. This would result in producers being willing to spend more on an insurance program and thus, allow for a reduction in the implied subsidy.

Bio: https://www.linkedin.com/in/kylejore/

Abstract:

Experts have long realized the parallels between elliptic and parabolic theory of partial differential equations. It is well-known that elliptic theory may be considered a static, or steady-state, version of parabolic theory. And in particular, if a parabolic estimate holds, then by eliminating the time parameter, one immediately arrives at the underlying elliptic statement. Producing a parabolic statement from an elliptic statement is not as straightforward. In this talk, we demonstrate a method for producing parabolic theorems from their elliptic analogs. Specifically, we show that an $L^2$ Carleman estimate for the heat operator may be obtained by taking a high-dimensional limit of $L^2$ Carleman estimates for the Laplacian. Other applications of this technique will be discussed.

Abstract:

We the limiting distribution of the largest off-diagonal entry of the sample correlation matrices of high-dimensional Gaussian populations with equi-correlation structure. Assume the entries of the population distribution have a common correlation coefficient r >0 and both the population dimension p and the sample size n tend to infinity with log p=o(n^{1/3}).
As 0< r<1, we prove that the largest off-diagonal entry of the sample correlation matrix converges to a Gaussian distribution, and the same is true for the sample covariance matrix as 0< r<1/2. This differs substantially from a well-known result for the independent case where r=0, in which the above limiting distribution is an extreme-value distribution. We then study the phase transition between these two limiting distributions and identify the regime of r where the transition occurs. It turns out that the thresholds of such a regime depend on n and converge to zero. If r is less than the threshold, larger than the threshold or is equal to the threshold, the corresponding limiting distribution is the extreme-value distribution, the Gaussian distribution and a convolution of the two distributions, respectively. The proofs rely on a subtle use of the Chen-Stein Poisson approximation method, conditioning, a coupling to create independence and a special property of sample correlation matrices. The results are then applied to evaluating the power of a high-dimensional testing problem of identity correlation matrix.

Abstract:

Continuing to follow the paper of Comes and Ostrik, On blocks of Deligne's category Rep(S_t), we prove the result in Section 3 that the category is semisimple except for countably. many values of t. The proof is an interesting use of the characterization of semisimple algebras by non-degeneracy of the trace form.

Abstract:

The equivariant moving frame method is adapted to algorithmically construct complete, minimal sets of generating invariants for finite or, more generally, discrete group actions, both linear and nonlinear. The resulting invariants are piecewise analytic and admit a rewrite rule that allows one to immediately express any other invariant (polynomial, rational, smooth, analytic, etc.) as a function of the generating invariants. The talk will be elementary and no a priori knowledge of invariant theory or moving frames will be assumed.

Abstract:

We analytically obtain the dispersion relations for transverse-electric (TE) and transverse-magnetic (TM) surface plasmon-polaritons in a nonlinear two-dimensional (2D) conducting material with inversion symmetry lying between two Kerr-type dielectric media. To this end, we use Maxwell's equations within the quasielectrostatic, weakly dissipative regime. We show that the wavelength and propagation distance of surface plasmons decrease due to the nonlinearity of the surrounding dielectric. In contrast, the effect of the nonlinearity of the 2D material depends on the signs of the real and imaginary parts of the third-order conductivity. Notably, the dispersion relations obtained by naively replacing the permittivity of the dielectric medium by its nonlinear counterpart in the respective dispersion relations of the linear regime are not accurate. We apply our analysis to the case of doped graphene and make predictions for the surface plasmon wavelength and propagation distance.

Abstract:

Abstract: In this paper, we implement an efficient simulation-based method for estimating the Greeks of American options. We perform a least square regression to determine the optimal stopping rule that is applied to calculate the Greeks, which are derived via a path-wise derivative approach. We prove that this method provides asymptotically unbiased simulation estimators for the Greeks. In addition, we propose a boundary integral technique as a faster way to approximate gamma. This technique can also be used to calculate delta and theta. Our paper is the first to provide complete simulation-based approximations for all of the Greeks (delta, gamma, theta, rho, and vega) of American options. To make the computational process more efficient, we incorporate a Brownian Bridge into the numerical simulations. We then extend the application to American basket options.

Bio: P.A. Nguyen is a PhD candidate in the University of Minnesota's Industrial Systems Engineering (ISyE) Doctoral Program. She is working with Dr. Dan Mitchell whose focus is in the area of financial engineering, specifically applying stochastic control to problems in finance and quantitative risk management. P.A. is also an alumna of the Master of Financial Mathematics (MFM) at the University of Minnesota (2014) and is currently a teaching assistant for the MFM. She worked in enterprise risk management, primarily in credit risk and interest rate risk areas, for a few years before joining UMN’s ISyE PhD program.

Abstract:

We prove the modularity of some two-dimensional residually reducible p-adic Galois representations over Q when p is at least 5. In the first talk, I will present a generalization of Emerton's local-global compatibility result. In the second talk, I will use this compatibility result to make a patching argument for completed homology in this setting.

Abstract:

We prove the modularity of some two-dimensional residually reducible p-adic Galois representations over Q when p is at least 5. In the first talk, I will present a generalization of Emerton's local-global compatibility result. In the second talk, I will use this compatibility result to make a patching argument for completed homology in this setting.

Abstract:

I'll introduce well-known counting conjectures related to number fields of bounded discriminants, and then I'll talk about some semi-related topological ideas that may allow us to strengthen these conjectures in certain situations.

Abstract:

In combinatorics there is a classic problem about a "hat check," where patrons submit their hats, return slips are lost, and people get them back willy nilly. People get sad, angry, happy, you name it! In fact, as the number of people checking hats goes to infinity, the probability that NO ONE gets their hat back goes to 1/e! Well, here at the U of M math department, we are all about generalizations. Therefore, we present to you: The 1st annual AMS Sock Derangement! (a generalization of the AMS hat check)

Abstract:

There has been high scientific interest to understand the behavior of the surface quasi-geostrophic (SQG) equation because it is a possible model to explain the formation of fronts of hot and cold air and because it also exhibits analogies with the 3D incompressible Euler equations. It is not known at this moment if this equation can produce singularities or if solutions exist globally. In this talk, I will discuss some recent works on the existence of global solutions for the SQG and modified SQG equations.

Abstract:

In the era of “big data”, analysts usually explore various statistical models or machine learning methods for observed data in order to facilitate scientific discoveries or gain predictive power. Whatever data and fitting procedures are employed, a crucial step is to select the most appropriate model or method from a set of candidates. Model selection is a key ingredient in data analysis for reliable and reproducible statistical inference or prediction, and thus central to scientific studies in fields such as ecology, economics, engineering, finance, political science, biology, and epidemiology. There has been a long history of model selection techniques that arise from researches in statistics, information theory, and signal processing. A considerable number of methods have been proposed, following different philosophies and exhibiting varying performances. The purpose of this talk is to bring an overview of them, in terms of their motivation, large sample performance, and applicability. I will provide practically relevant discussions on theoretical properties of state-of- the-art model selection approaches, and share some thoughts on controversial views on the practice of model selection.

Bio for Jie Ding
https://cla.umn.edu/statistics/news-events/story/new-faculty-member-char...

Abstract:

Bioprosthetic heart valves (BHVs) are the most popular artificial replacements for diseased valves that mimic the structure of native valves. However, the life span of BHVs remains limited to 10-15 years, and the mechanisms that underlie BHVs failure remain poorly understood. Therefore, developing a unifying mathematical framework which captures material damage phenomena in the fluid-structure interaction environment would be extremely valuable for studying BHVs failure. Specifically, in this framework the computational domain is composed of three subregions: the fluid (blood) , the fracture structure (damaged BHVs) modeled by the recently developed nonlocal (peridynamics) theory, and the undamaged thin structure (undamaged BHVs). These three subregions are numerically coupled to each other with proper interface boundary conditions.

In this talk, I will introduce two sub-problems and the corresponding numerical methods we have developed for this multiscale/multiphysics framework. In the first problem the coupling strategy for fluid and thin structure is investigated. This problem presents unique challenge due to the large deformation of BHV leaflets, which causes dramatic changes in the fluid subdomain geometry and difficulties on the traditional conforming coupling methods. To overcome the challenge, the immersogeometric method was developed where the fluid and thin structure are discretized separately and coupled through penalty forces. To ensure the capability of the developed method in modeling BHVs, we have verified and validated this method. The second problem focuses on the nonlocal Neumann-type interface boundary condition which plays a critical role in the fluid—peridynamics coupling framework. In the nonlocal models the Neumann-type boundary conditions should be defined in a nonlocal way, namely, on a region with non-zero volume outside the surface, while in fluid—structure interfaces the hydrodynamic loadings from the fluid side are typically provided on a sharp co-dimensional one surface. To overcome this challenge, we have proposed a new nonlocal Neumann-type boundary condition which provides an approximation of physical boundary conditions on a sharp surface, with an optimal asymptotic convergence rate to the local counter part. Based on this new boundary condition, we have developed a fluid—peridynamics coupling framework without overlapping regions.

Abstract:

By definition the choreography (dancing curve) is the trajectory
on which n classical bodies move chasing each other without collisions
with equal time delay. The first choreography (the remarkable Figure Eight)
at zero angular momentum was discovered unexpectedly by C Moore
(Santa Fe Institute) in 1993 for 3 equal masses in R^3 Newtonian gravity, numerically
and rigorously confirmed by Chenciner-Montgomery (2000). At the moment
about 6,000 choreographies are known, all numerically, in Newtonian gravity.
Possibly all known dancing curves are non-algebraic.

Does there exist (non)-Newtonian gravity for which the dancing curve is known
analytically? - Yes, a single example is known - it is the algebraic
lemniscate by Jacob Bernoulli (1694) - and it will be a concrete subject of the talk. Astonishingly, Newtonian Figure Eight coincides with algebraic lemniscate with
\chi^2 deviation 10^{-7}. Both choreographies admit any odd numbers of bodies
on them. 3-body choreography on algebraic lemniscate defines maximally
superintegrable trajectory with seven constants of motion and, possible,
polynomial algebra of integrals.

Abstract:

Many insurance companies offer a wide array of investment guarantees, and some of these are complicated and have no clear analytical solution. In order to manage these contracts and along with increased scrutiny from regulatory bodies, companies are having to value these contracts more frequently and in a greater number of runs. We will demonstrate methodologies and computational approaches to be able to perform analysis on these contract that can be actionable and timely.

Abstract:

In recent years, optimal transport has many applications in evolutionary dynamics, statistics, and machine learning. In this talk, we introduce dynamical optimal transport on finite graphs. We proposed to study the probability simplex as a Riemannian manifold with a Wasserstein metric. We call it a probability manifold. Various developments, especially the Fokker-Planck equation, will be introduced. The entropy production on graphs related to Shannon entropy will be established. Its connection with Fisher information and Yano’s formula will be studied. Many examples, including Mean field games, geometry of graphs, statistical learning problems, will be presented.