Past Seminars

Abstract:

Classically, a Siegel modular form is said to be singular or distinguished if many of its Fourier coefficients are 0, in a precise sense. I will explain the construction of singular and distinguished modular forms on the exceptional groups E_6, E_7, E_8. Moreover, time permitting, I will also explain an analogue of the Saito-Kurokawa lift, which produces cusp forms on Spin(8) and the exceptional group G_2 out of holomorphic Siegel modular forms on Sp(4).

Abstract:

In this talk we will first give an overview of the known
Gibbsian line ensembles in the KPZ universality class. Then we will
construct the discrete log-gamma line ensemble, which is associated
with the log-gamma polymers. This log-gamma line ensemble enjoys a
random walk Gibbs resampling invariance that follows from the
integrable nature of the log-gamma gamma polymer model via the
geometric RSK correspondence. By exploiting such resampling
invariance, we show the tightness of this log-gamma line ensemble
under the weak noise scaling. Furthermore, a Gibbs property, as
enjoyed by the KPZ line ensemble, holds for all subsequential limits.

Abstract:

Personalized recommendations are useful to assist users in their choices in web-based market places, entertainment engines, information providers. Learning individual preferences is an important step. Users express their preferences by rating, ranking, (possibly inconsistently) comparing, liking and clicking items. Such data contain information about the individual user’s ranking of the items. Click-through data can be seen as (consistent) pair comparisons. The Mallows rank model allows to analyse rank data, but its computational complexity has limited its use to a particular form, based on Kendall’s distance. We developed new computationally tractable methods for Bayesian inference in Mallows models that work with any right-invariant distance. Our method performs inference on the latent consensus ranking of all items and on the individual latent rankings by Bayesian augmentation. Current popular recommendation algorithms are based on matrix factorisations, have high accuracy and achieve good clickthrough rates. However diversity of the recommended items is often poor and most algorithms do not produce interpretable uncertainty quantifications of the recommendations. With a simulation study and real life data examples, we demonstrate that compared to matrix factorisation approaches, our Bayesian Mallows method makes personalized recommendations mpared to matrix factorisation approaches, our Bayesian Mallows method makes personalized recommendations.

Arnoldo Frigessi is professor of statistics at the University of Oslo, leads the Oslo Center for Biostatistics and Epidemiology and is director of BigInsight. BigInsight is a centre of excellence for research-based innovation, a consortium of industry, business, public actors and academia, developing model based machine learning methodologies for big data. Originally from Italy, where he had positions in Rome and Venice, he moved to Norway in 2019 as a researcher at the Norwegian Computing Centre, before he became professor at the University of Oslo.

Frigessi has developed statistical methodology motivated by specific problems in science, technology and industry. He has designed stochastic models to study principles, dynamics and patterns of complex dependence. Inference is usually based on computationally intensive stochastic algorithms. Currently, he has research collaborations in genomics, personalised therapy in cancer, infectious disease models, eHealth research, personalised and viral marketing, s

Abstract:

When G is a reductive (non-compact) Lie group, one can consider automorphic forms for G. These are functions on the locally symmetric space X_G associated to G that satisfy some sort of nice differential equation. When X_G has the structure of a complex manifold, the _modular forms_ for the group G are those automorphic forms that correspond to holomorphic functions on X_G. They possess close ties to arithmetic and algebraic geometry. For certain exceptional Lie groups G, the locally symmetric space X_G is not a complex manifold, yet nevertheless possesses a very special class of automorphic functions that behave similarly to the holomorphic modular forms above. Building upon work of Gan, Gross, Savin, and Wallach, I will define these modular forms and explain what is known about them.

Abstract:

Though Einstein's fundamental theory of general relativity has already celebrated its one hundredth birthday, there are still many outstanding unsolved problems. The Kerr stability conjecture is one of the most important open problems, which posits that the Kerr metrics are stable solutions of the vacuum Einstein equation. Over the past decade, there have been huge advances towards this conjecture based on the study of wave equations in black hole spacetimes and structures in the Einstein equation. In this talk, I will discuss the recent progress in the stability problems with special focus on the wave gauge.

Abstract:

In this talk we will describe how one can detect regularity in Jordan curves through analysis of associated geometric square functions. We will particularly focus on the resolution to a conjecture of L. Carleson. Joint work with Xavier Tolsa and Michele Villa (https://arxiv.org/abs/1909.08581).

Abstract:

Current personalized cancer treatment is based on biomarkers which allow assigning each patient to a subtype of the disease, for which treatment has been established. Such stratifiedpatient treatments represent a first important step away from one-size-fits-all treatment.However, the accuracy of disease classification comes short in the granularity of thepersonalization: it assigns patients to one of a few classes, within which heterogeneity inresponse to therapy usually is still very large. In addition, the combinatorial explosivequantity of combinations of cancer drugs, doses and regimens, makes clinical testingimpossible. We propose a new strategy for personalised cancer therapy, based on producing acopy of the patient’s tumour in a computer, and to expose this synthetic copy to multiplepotential therapies. We show how mechanistic mathematical modelling, patient specificinference and simulation can be used to predict the effect of combination therapies in a breastcancer. The model accounts for complex interactions at the cellular and molecular level, andis able of bridging multiple spatial and temporal scales. The model is a combination ofordinary and partial differential equations, cellular automata and stochastic elements. Themodel is personalised by estimating multiple parameters from individual patient data,routinely acquired, including histopathology, imaging and molecular profiling. The resultsshow that mathematical models can be personalized to predict the effect of therapies in eachspecific patient. The approach is tested with data from five breast tumours collected in arecent neoadjuvant clinical phase II trial. The model predicted correctly the outcome after 12weeks treatment and showed by simulation how alternative treatment protocols would haveproduced different, and some times better, outcomes. This study is possibly the first onetowards personalized computer simulation of breast cancer treatment incorporating relevantbiologically-specific mechanisms and multi-type individual patient data in a mechanistic andmultiscale manner: a first step towards virtual treatment comparison.Xiaoran Lai, Oliver Geier, Thomas Fleischer, Øystein Garred, Elin Borgen, Simon Funke,Surendra Kumar, Marie Rognes, Therese Seierstad, Anne-Lise Børressen-Dale, VesselaKristensen, Olav Engebråten, Alvaro Köhn-Luque, and Arnoldo Frigessi, Tow

Abstract:

For a domain ? ? Rd, quantitative, scale-invariant absolute continuity (more precisely, the weak-A? property) of harmonic measure with respect to surface measure on ??, is equivalent to the solvability of the Dirichlet problem for Laplace’s equation, with data in some Lp space on ??, with p < ?. Drawing an analogy to the famous Wiener criterion, which characterizes the domains in which the classical Dirichlet problem, with continuous boundary data, can be solved, it is of interest to find criteria for Lp solvability, thus allowing for singular boundary data. We shall review known results in this direction, in which (within the past 18 months) a rather complete picture has now emerged.

Abstract:

Fano varieties are positively curved algebraic varieties which form one of the three building blocks in the classification. Unlike the case of negatively curved varieties, moduli spaces of Fano varieties (even smooth ones) can fail to be Hausdorff. K-stability was originally invented as an algebro-geometric notion characterizing the existence of K\"ahler-Einstein metrics on Fano varieties. Recently, people have found strong evidence toward constructing compact Hausdorff moduli spaces of Fano varieties using K-stability. In this talk, I will discuss recent progress in this approach, including an algebraic proof of the existence of Fano K-moduli spaces, and describing these moduli spaces explicitly. This talk is partly based on joint works with H. Blum and C. Xu.

Abstract:

Crystalline cohomology is a type of Weil cohomology theory that fills in the gap at $p$ in the family of $l$-adic cohomologies. It's introduced by Alexander Grothendieck and developed by Pierre Berthelot. We will briefly discuss what is crystalline cohomology and why we need it. With the help of Frobenius action, we can define a semi-linear morphism on crystalline cohomology which provides a Newton polygon. We will state the Katz's conjecture (which is proved by Mazur and Ogus) (slogan: Newton polygon lies above Hodge polygon) and show some applications (if time permits).

Abstract:

According to the Bloomberg Barclays Global Aggregate Index, there were $17 trillion (or 30%) bonds traded with negative yields within that popular fixed income benchmark, at the end of August 2019. Government bonds in Germany, Japan, and Switzerland all carry negative yields - meaning investors will lose money to hold them to maturity. How did we get here? Are those policies introduced by global central banks, after the 2008 financial crisis, effective (to spur inflation)? In this talk, I seek to use some case studies like reserve banking system, asset bubbles, and “currency war” to explore this topic.Bio: https://www.linkedin.com/in/liyuepeng/

Abstract:

The capacity to quantify crop architecture and morphology is foundational to the development of higher yielding cultivars via hybridization and genetic engineering. However, at the mass scale required by the science, manual plant phenotyping with physical instruments is arduous, time consuming, subjective, and a leading cause of undergraduate burnout in the UMN plant genetics department. While the process has been slightly improved with manual image analysis, such is almost as time consuming and remains subject to human error. Thereby, in efforts to further expedite phenotyping processes, circumvent human error, and provide more detailed analyses, we aim to completely automate plant phenotyping processes for the UMN plant genetics department and beyond. Working from over 15,000 soybean plants, we’ve advanced robust image processing platforms for measuring petiole and stem length, leaf area, leaf shape via signature curves, and branch angles via energy minimization in 2D, and begun preliminary work at 3D reconstructions from 2D data for 3D branch angles and further analyses. After data extraction and verification, we plan to implement clustering algorithms and machine learning to automatically group plant phenotypes as well as conduct principal component analysis to assemble an allometry space and identify the primary genes of influence.

Abstract:

PhD and Master's students would likely benefit the most from this talk. There will be some discussion on opportunities at Target.

The introduction of this talk will provide an overview of the AI sciences organization at Target and discuss summer internship opportunities. The remainder of the talk will overview the kinds of machine learning problems Target deals with, and dive into three such problems, in the fields of recommender systems and computer vision.

Mauricio Flores received his PhD in Applied Mathematics from the University of Minnesota in 2018, under the supervision of Jeff Calder & Gilad Lerman. He is currently a Lead AI Scientist at Target, where he builds machine learning as well as computer vision models for visually compatible recommendations, and more recently, for damage detection in Target’s distribution centers.

Abstract:

This talk concerns a family of "collapsing" Ricci-flat Kähler manifolds, namely Calabi-Yau manifolds, converging to a lower dimensional limit, which develop singularities arising in various contexts such as metric Riemannian geometry, complex geometry and degenerating nonlinear equations. A primary aspect is to formulate how well behaved or badly behaved such spaces can be in terms of the recently developed regularity theory. Under the above framework, our next focus is on a longstanding fundamental problem which is to understand singularities of collapsing Ricci-flat metrics along an algebraically degenerating family. We will give accurate characterizations of such metrics and explain possible generalizations.

Abstract:

Starting from the works of Erdos, Yau, Schlein with coauthors, the significant progress in understanding the universal behavior of many random graph and random matrix models were achieved. However for the random matrices with a spacial structure our understanding is still very limited. In this talk I am going to overview applications of another approach to the study of the local eigenvalues statistics in random matrix theory based on so-called supersymmetry techniques (SUSY) . SUSY approach is based on the representation of the determinant as an integral over the Grassmann (anticommuting) variables. Combining this representation with the representation of an inverse determinant as an integral over the Gaussian complex field, SUSY allows to obtain an integral representation for the main spectral characteristics of random matrices such as limiting density, correlation functions, the resolvent's elements, etc. This method is widely (and successfully) used in the physics literature and is potentially very powerful but the rigorous control of the integral representations, which can be obtained by this method, is quite difficult, and it requires powerful analytic and statistical mechanics tools. In this talk we will discuss some recent progress in application of SUSY to the analysis of local spectral characteristics of the prominent ensemble of random band matrices, i.e. random matrices whose entries become negligible if their distance from the main diagonal exceeds a certain parameter called the band width.

Abstract:

https://www.fitmetrix.io/webportal/schedulemobile/f9719b20-4914-e911-a97...

Abstract:

Inverse problems and uncertainty quantification (UQ) are pervasive in scientific
discovery and decision-making for complex, natural, engineered, and societal systems.
They are perhaps the most popular mathematical approaches for enabling predictive scientific simulations that integrate observational/experimental data, simulations and/or
models. Unfortunately, inverse/UQ problems for practical complex systems possess these the simultaneous challenges: the large-scale forward problem challenge, the high dimensional parameter space challenge, and the big data challenge.

To address the first challenge, we have developed parallel high-order (hybridized) discontinuous Galerkin methods to discretize complex forward PDEs. To address the second challenge, we have developed various approaches from model reduction to advanced Markov chain Monte Carlo methods to effectively explore high dimensional parameter spaces to compute posterior statistics. To address the last challenge, we have developed a randomized misfit approach that uncovers the interplay between the Johnson-Lindenstrauss and the Morozov's discrepancy principle to significantly reduce the dimension of the data without compromising the quality of the inverse solutions.

In this talk we selectively present scalable and rigorous approaches to tackle these challenges for PDE-governed Bayesian inverse problems. Various numerical results for simple to complex PDEs will be presented to verify our algorithms and theoretical findings. If time permits, we will present our recent work on scientific machine learning for inverse and learning problems.

Abstract:

Matroids are combinatorial objects that capture the essence of linear independence. We first give a gentle introduction to the recent breakthrough in matroid theory, the Hodge theory of matroids, developed by Adiprasito, Huh, and Katz. By combining two prominent recent approaches to matroids, tropical geometric and Lie/Coxeter theoretic, we give a new presentation for the Chow ring of a matroid that further tightens the interaction between combinatorics and geometry of matroids. We discuss various applications, including a simplified proof of the main portion of the Hodge theory of matroids. This is joint work with Spencer Backman and Connor Simpson.

Abstract:

Taffy is a type of candy made by repeated 'pulling' (stretching andfolding) a mass of heated sugar. The purpose of pulling is to get air
bubbles into the taffy, which gives it a nicer texture. Until the
late 19th century, taffy was pulled by hand, an arduous task. The
early 20th century saw an avalanche of new devices to mechanize the
process. These devices have fascinating connections to the
topological dynamics of surfaces, in particular with pseudo-Anosov
maps. Special algebraic integers such as the Golden ratio and the
lesser-known Silver ratio make an appearance, as well as more exotic
numbers. We examine different designs from a mathematical
perspective, and discuss their efficiency. This will be a "colloquium
style" talk that should be accessible to graduate students.

Abstract:

This talk will highlight the evolution of building out a data science capability in a retail environment as well as explore cutting-edge developments in constructing machine learning pipelines in the cloud, emerging advancements in time-series forecasting, applications of GPU accelerated graph processing for entity resolution, and how we’re adapting the latest research in language models to translate the language of shopping for the ultimate personalized experience

Abstract:

A central problem in number theory is that of finding rational or integer solutions to systems of polynomials in several variables. This leads one naturally to the slightly easier problems of finding solutions modulo a prime $p$. Using a discrete analogue of the Fourier transformation, this modulo $p$ problem can be reformulated in terms of exponential sums. We will discuss $p$-adic properties of such exponential sums in the case of higher genus curves as well as connections to complex differential equations.

Abstract:

Abstract: Recall that the Frobenius morphism is simply the map sending an element in a ring of prime characteristic $p>0$ -- say, a polynomial with coefficients in a finite field -- to its $p$-th power. Though simple to define, Frobenius has proven to be a useful and effective tool in algebraic geometry, representation theory, number theory, and commutative algebra. Furthermore, and remarkably, some of the most interesting applications of Frobenius are to the study of objects defined over the complex numbers, and more generally, over a field of characteristic zero! In this talk, we will discuss some of these applications, with an eye towards classical singularity theory and birational algebraic geometry, both over the complex numbers.

Abstract:

I will give a brief mathematical introduction to Anderson localization followed by a discussion of my recent work with Jian ding. In our work we establish localization near the edge for the Anderson Bernoulli model on the two dimensional lattice. Our proof follows the program of Bourgain--Kenig and uses a new unique continuation result inspired by Buhovsky--Logunov--Malinnikova--Sodin. I will also discuss recent work of by Li and Zhang on the three dimensional case.

Abstract:

Local cohomology modules are fundamental tools in commutative algebra, due to the algebraic and geometric information they carry. For instance, they can help determine the number of equations necessary to define an affine variety. Unfortunately, however, the application of local cohomology is limited by the fact that these modules are typically very large (e.g., not finitely generated), and can be difficult to determine explicitly. In this talk, we discuss new techniques developed to understand the structure of local cohomology (e.g., coming from invariant theory). We also describe recently-discovered "connectedness properties" of spectra that local cohomology encodes.

Abstract:

Abstract not available.

Abstract:

Mathematical models can support biomedical research through identification of key mechanisms, validation of experiments, and simulation of new therapeutic approaches.

We investigate the evolution of melanomas under adoptive cell transfer therapy with cytotoxic T-cells. It was shown in experiments that phenotypic plasticity, more precisely an inflammation-induced, reversible dedifferentiation, is an important escape mechanism for the tumor. Recently, the effects of possible mutation to a permanently resistant genotype were studied by introducing knockout melanoma cells into the wildtype tumor.

We use a stochastic individual-based Markov process to describe the evolution of the tumor under various therapeutic approaches. It is an extension of the model introduced in the paper of Baar et al in 2016 and further includes the effects of T-cell exhaustion and some limited spatial component which results in additional non-linearities. The model is implemented as a hybrid algorithm that combines Gillespie-type stochastic calculations and a deterministic approximation to speed up simulations while keeping the effects of random events.

Numerical simulations confirm the resistance to therapy via phenotypic switching as well as genotypic mutation. T-cell exhaustion is identified as an important mechanism that is crucial in fitting the model to the experimental data. We gain further insights into how originally unfit knockout cells can accumulate under therapy, shield the wild type cells from the T-cells, and thus cause an earlier relapse. Going beyond the experiment, the possibility of naturally occurring rare mutations, in contrast to artificially introduced knockout cells, is explored in simulations and produces the same effects. Thus, the clinical relevance of the experimental findings can be confirmed.

Abstract:

This is the third of a series of three talks on the spectral sequence of a filtered complex. This material is by now classical and is an important part of homological algebra. The main difficulty in dealing with spectral sequences is that there are a lot of indexes involved and this is a considerable obstacle to understanding what is going on. The goal of these talks is to present this material, including most proofs, in an accessible manner.

Abstract:

Deep neural networks (DNNs) have revolutionized machine learning by gradually replacing the traditional model-based algorithms with data-driven methods. While DNNs have proved very successful when large training sets are available, they typically have two shortcomings: First, when the training data are scarce, DNNs tend to suffer from overfitting. Second, the generalization ability of overparameterized DNNs still remains a mystery.

In this talk, I will discuss two recent works to “inject” the “modeling” flavor back into deep learning to improve the generalization performance and interpretability of the DNN model. This is accomplished by DNN regularization through applied differential geometry and harmonic analysis. In the first part of the talk, I will explain how to improve the regularity of the DNN representation by enforcing a low-dimensionality constraint on the data-feature concatenation manifold. In the second part, I will discuss how to impose scale-equivariance in network representation by conducting joint convolutions across the space and the scaling group. The stability of the equivariant representation to nuisance input deformation is also proved under mild assumptions on the Fourier-Bessel norm of filter expansion coefficients.

Abstract:

  I study how extrapolative expectations affect corporate real and financial activities and asset prices. Empirically, high misperception on earnings growth, a measure constructed to proxy for extrapolation, is associated with an increase in investment, debt issuance, equity issuance, and firm-level bond and stock prices in the short-term, but predicts a decline in all these activities and prices in the long-term. These patterns are more pronounced among small and financially constrained firms. Theoretically, I build a dynamic model with extrapolative expectations and financial frictions, and show that the interaction of these two frictions is crucial to explain the empirical findings. Intuitively, after a sequence of favorable shocks, agents extrapolate and become overoptimistic about future productivities. Firms invest and borrow more in the short-term. Lower perceived default probability improves financing conditions, further increasing investment and borrowing. Future realizations turn out worse than expected, making real and financial activities and asset prices subject to predictable reversals in the long-term.Bio: https://carlsonschool.umn.edu/faculty/yao-deng

Abstract:

Work of Levasseur and Stafford describes the rings of differential operators on various classical invariant rings of characteristic zero; in each of these cases, the differential operators form a simple ring. Towards an attack on the simplicity of rings of differential operators on invariant rings of reductive groups over the complex numbers, Smith and Van den Bergh asked if reduction modulo p works for differential operators in this context. In joint work with Jack Jeffries, we establish that this is not the case for various classical groups.

Abstract:

The speaker has long feared the technicalities and intricacies of equivariant stable homotopy theory. Fortunately, beginning with the work of Glasman, major simplification on the foundations of the subject has been made (cf. the work of Ayala-Mazel-Gee-Rozenblyum, Nikolaus-Scholze and the Barwick school). We offer another perspective (that the speaker has a chance of understanding) on equivariant stable homotopy theory, at least for the group C_2, via algebraic geometry. We view it as a way to remedy an infamous annoyance: the 2-étale cohomological dimension of the field of real numbers is infinite. We do this by identifying the genuine C_2-spectra with a category of motives based on Real algebraic geometry ala Scheiderer. This is joint work with Jay Shah.

Abstract:

According to the US Census Bureau, homeownership rates peaked during the first quarter of 2005 at 69.1% but fell to just 63.8% in the fourth quarter of 2015, a year when residential mortgage debt outstanding was still above ten trillion-dollar mark and mortgage origination was about $1.7T. Mortgage Back Security (MBS) originations have continued to experience a steady growth attracting investors, servicers, insurers, lenders, and GSEs (Government Sponsored Enterprises). In contrary, MBS market presented various financial risks including prepayment from the homeowners - be that voluntary or involuntary.

To manage the risks presented by the borrowers, modeling prepayment behavior is critical in the work banks do. Four fundamental components of mortgage prepayment activity will be examined in this presentation.

Abstract:

This is the first of a series of three talks on the spectral sequence of a filtered complex.
This material is by now classical and is an important part of homological algebra.
The main difficulty in dealing with spectral sequences is that there are a lot of indexes involved and this is a considerable obstacle to understanding what is going on. The goal of these talks is to present this material, including most proofs, in an accessible manner.

Abstract:

Multiobjective optimization problems are ubiquitous in science and engineering contexts, and nondominated sorting is a sorting process fundamental to multiobjective optimization. Recently proposed approaches to nondominated sorting exploit an underlying PDE that arises in the continuum limit. The need for theoretical guarantees for nondominated sorting algorithms motivates the problem of finding rates of convergence for the continuum limit. In this talk I will introduce PDE techniques from the theory of viscosity solutions and show how they can be used to solve this problem. Furthermore, I will show how semiconvexity estimates can be used to bolster convergence rates, and discuss approaches to obtaining semiconvexity estimates. This talk is intended to be entirely self-contained, so no prior knowledge of PDE will be assumed.

Abstract:

After introducing the theory of FI-modules in 2012, the collaborative unit consisting of Thomas Church, Jordan Ellenberg and Benson Farb applied their framework to asymptotically stable counting problems in a certain classes of FI-varieties over finite fields in their 2013 paper Representation stability in cohomology and asymptotics for families of varieties over finite fields. The paper serves as a proof-of-concept, unifying a number of previously-known combinatorial results. The key to their method is the Grothendieck-Lefschetz fixed-point theorem with twisted statistics, which relates the rational cohomology of an algebraic variety over the complex numbers with the trace of the Frobenius map applied to the étale cohomology with coefficients in an $\ell$-adic sheaf of that variety over a finite field. In this talk, we shall introduce the Grothendieck-Lefschetz formula and its associated machinery as well as FI-modules and representation stability, then use these ideas to give an exposition of some results of Church, Ellenberg and Farb as they relate to configuration spaces and the braid group.

Abstract:

The talk is based on recent and ongoing work with Swapneel
Mahajan. We will introduce a notion of Hopf monoid relative to a real
hyperplane arrangement. When the latter is the braid arrangement, the
notion is closely related to that of a Hopf monoid in Joyal's category
of species, and to the classical notion of connected graded Hopf
algebra. We are able to extend many concepts and results from the
classical theory of connected Hopf algebras to this level. The
extended theory connects to the representation theory of a certain
finite dimensional algebra, the Tits algebra of the arrangement. This
perspective on Hopf theory is novel even when applied to the classical
case. We will outline our approach to generalizations of the classical
Leray-Samelson, Borel-Hopf, and Cartier-Milnor-Moore theorems to this
setting. Background on hyperplane arrangements will be reviewed.

Abstract:

In joint work with Andrea Nahmod and Haitian Yue, we prove local well-posedness for the derivative nonlinear Schrodinger's equation in Fourier-Lebesgue space which has the same scaling as H^s for any s>0. This closes the gap left open by the work of Grunrock-Herr where s>1/4. Here there is no trilinear estimate in any standard function space, instead we will construct the solution in a nonlinear submanifold (of a function space) by exploiting its structure. This is somehow inspired by the theory of para-controlled distributions that Gubinelli et al. developed for stochastic PDEs, but our arguments are purely deterministic.

Abstract:

U.S. election forecasting involves polling likely voters, making assumptions about voter turnout, and accounting for various features such as state demographics and voting history. While political elections in the United States are decided at the state level, errors in forecasting are correlated between states. With the goal of shedding light on the forecasting process and exploring how states influence each other, we develop a framework for forecasting elections in the U.S. from the perspective of dynamical systems. Through a simple approach that borrows ideas from epidemiology, we show how to combine a compartmental model with public polling data from HuffPost and RealClearPolitics to forecast gubernatorial, senatorial, and presidential elections at the state level. Our results for the 2012 and 2016 U.S. races are largely in agreement with those of popular pollsters, and we use our new model to explore how subjective choices about uncertainty impact results. We conclude by comparing our forecasts for the senatorial and gubernatorial races in the U.S. midterm elections of 6 November 2018 with those of popular pollsters. This is joint work with Daniel Linder (Augusta Univ.), Mason Porter (UCLA), and Grzegorz Rempala (Ohio State Univ.)

Abstract:

Recent advances in technology have led to a monumental increase in large-scale data across many platforms. One mathematical model that has gained a lot of recent attention is the use of sparsity. Sparsity captures the idea that high dimensional signals often contain a very small amount of intrinsic information. Using this notion, one may design efficient low-dimensional representations of large-scale data as well as robust reconstruction methods for those representations. Binary, or one-bit, representations of data for example, arise naturally in many applications, and are appealing in both hardware implementations and algorithm design. In this talk, we provide a brief background to sparsity and 1-bit measurements, and present new results on the problem of data classification with low computation and resource costs. We illustrate the utility of the proposed approach on recently acquired data about Lyme disease.

Abstract:

The machinery of the Weil conjectures often allows us to relate the singular cohomology of the complex points of a scheme to the cardinality of its set of points over a finite field. When we apply these methods to a moduli scheme, we obtain an enumeration of the objects the moduli parameterizes. It's rare that we can actually fully compute the cohomology of these moduli spaces, but homological stability results often give a first order approximation to the homology.

In this talk, we'll explain how to obtain second order homological computations for a class of Hurwitz moduli spaces of branched covers; these yield second order terms in enumerating the moduli over finite fields. We may interpret these as second order terms in a function field analogue of the function which counts number fields, ordered by discriminant. Our second order terms match those of Taniguchi-Thorne/Bhargava-Shankar-Tsimerman in the cubic case, and give new predictions in other Galois settings.

This is joint (and ongoing) work with Berglund, Michel, and Tran.

Abstract:

Convolution has played a prominent role in various applications in science and engineering for many years. It is also the most important operation in convolutional neural networks (CNNs). There has been a recent growth of interests of research in generalizing CNNs on 3D objects, often represented as compact manifolds. However, existing approaches cannot preserve all the desirable properties of Euclidean convolutions, namely compactly supported filters, directionality, transferability across different manifolds. In this talk, I will discuss our recent work on a new way of defining convolution on manifolds via parallel transport. This geometric way of defining parallel transport convolution (PTC) provides a natural combination of modeling and learning on manifolds. PTC allows for the construction of compactly supported filters and is also robust to manifold deformations. I will demonstrate its applications to shape analysis using deep neural networks based on parallel transportation convolutional networks (PTC-net).

Dr. Rongjie Lai received his Ph.D. degree in applied mathematics from the University of California, Los Angeles, He is currently an assistant professor at the Rensselaer polytechnic Institute. Dr. Lai’s research interests are mainly in modern scientific computing including developing mathematical and computational tools for analyzing and processing signals, images as well as unorganized data using methods of variational partial differential equations, computational differential geometry and learning. In 2018, Dr. Lai was granted an NSF CAREER award for his research in geometry and learning for manifold-structured data in 3D and higher dimension.

Abstract:

We summarize the classification of representations of GL_2 over a p-adic field, emphasizing the relationship between these representations and automorphic forms and L-functions. Time permitting, we will also discuss Whittaker models of these representations and the Casselman-Shalika formula. This talk is meant to be a sketch of these results and how they fit into the bigger picture, and should be accessible even if you are not very familiar with representation theory.

Abstract:

We will show the sharp estimate on the behavior of the smallest singular value of random matrices under very general assumptions. One of the key steps in the proof is a result about the efficient discretization of the unit sphere in an n-dimensional euclidean space. This result allows us to work with very general random matrices. The proof of the result will be outlined. Partially based on the joint work with Tikhomirov and Vershynin.

Abstract:

We consider the problem of reconstructing a hidden assignment of random variables (x_1,…, x_n) sitting on the nodes of a graph, given noisy observations of pairs (x_u, x_v) for every edge (u,v) of the graph. Such problems have been extensively studied when the underlying graph is ‘mean-field’ (e.g., the complete graph, Erdos-Renyi, random regular,…), in which case the existence of a ``possible-but-hard’’ phase where reconstruction is possible but computationally difficult is ubiquitous.

In contrast, the picture is dramatically different when the graph is amenable. I will represent a generic result about the optimality of local algorithms for computing marginals under a somewhat strong model of side information. Next, I will focus on Z_2 synchronization (aka planted Ising model) on the Euclidean lattice with weaker side information, and discuss a renormalization-based procedure for reconstructing the assignment.

Joint work with Andrea Montanari.

Abstract:

I will discuss a research program aimed at building a Minecraft assistant, in order to facilitate the study of agents that can complete tasks specified by dialogue, and eventually, to learn from dialogue interactions. I will describe the tools and platform we have built allowing players to interact with the agents and to record those interactions, and the data we have collected. In addition, I will describe an initial agent from which we (and hopefully others) can iterate.

Abstract:

As researchers gather ever-larger datasets there is an increasing demand for citizen science and reliance on machine learning. We will introduce Zooniverse, the world's largest citizen science platform, and show how citizen scientists are helping researchers extract meaningful information from their data. But the demand for citizen scientists and the volumes of data they are being asked to process is beginning to tax even the abilities of our 1.8 million volunteers. We will show how machine learning is being deployed to ease some of this burden and show how machine learning and citizen science can empower each other to process data more efficiently than either alone.

Abstract:

The talk will give a historical introduction to number theory leading up to Dirichlet’s class number formula which was one of the biggest achievements of analytic methods in number theory. No background is necessary to understand the talk.

Abstract:

Veit Elser proposed a random graph model for percolation in which physical dimension appears as a parameter. Studying this model combinatorially leads naturally to the consideration of numerical graph invariants which we call \textit{Elser numbers} \text{els}_k(G), where G is a connected graph and k a nonnegative integer. Elser had proven that \text{els}_1(G)=0 for all G. By interpreting the Elser numbers as Euler characteristics of appropriate simplicial complexes called \emph{nucleus complexes}, we prove that for all graphs G, they are nonpositive when k=0 and nonnegative for k\geq2. The last result confirms a conjecture of Elser. At the end, we will present an open problem naturally arising from our proof of Elser's conjecture.

Abstract:

Data accessibility is important--publicly available data-sets support vital social science research, social programs and data-informed governance. In recent years, an increasing amount of data has been curated and made generally available through sites like data.gov, IPUMS, and other resources, fueling the progress of research in Big Data. However, data with the most potential value for public good can also be the most privacy sensitive--such as data on abuse, STDS, extreme poverty, or mental health. These datasets exist, but may be redacted or entirely withheld from public view due to legal restrictions and the very real danger that anonymized individuals may be reidentified.

Privacy-preserving synthetic data provides a pathway to publicly release datasets that contain very sensitive information. The basic process consists of three parts: A generative model is built which captures the distribution of the original sensitive data, perturbation steps are applied to the model to improve its privacy properties (either formal or heuristic-based), and then the model is used to synthesize a new data set of synthetic individuals. The synthetic dataset preserves the significant properties of the original data, but because it contains no real people, it can be safely released to the public. When the distributional difference between the real and synthetic data mimics the difference between two subsamples of the original data, i.e. when privacy error mimics sampling error, we can think of the synthetic data as survey results from a parallel dimension: The same pattern of information as the original data, with no real people.

In this talk, I'll cover approaches to creating synthetic data, the difference between formal and heuristic-based privacy, and, importantly, quality metrics used to verify that the synthetic data is a good substitute for the original data (a challenging problem itself in a high dimensional feature space). High quality synthetic data is a rapidly progressing research area, with both promising success stories and an exciting frontier of open problems.

Abstract:

Since first defined by Deligne and Lusztig, a Deligne-Lusztig variety has become unavoidable when studying the representation theory of finite groups of Lie type. This is a certain subvariety of the flag variety of the corresponding reductive group, and its cohomology groups are naturally endowed with the action such finite groups, which in turn gives a decomposition of irreducible representations called Lusztig series. In this talk, I will briefly discuss the background of Deligne-Lusztig theory and provide a formula to calculate the homology class of Deligne-Lusztig varieties in the Chow group of the flag variety. If time permits, I will also discuss their analogues.

Abstract:

Indexed Universal Life (IUL) Insurance was developed to harness thepower of equity market returns with downside protection. However IUL iscurrently illustrated using a static credited rate which masks market returnvolatility inherent in its structure. As a result, what policyholders see as expectedperformance maybe far from reality in many cases. In our research, we modeledthe pricing algorithms of major IUL products and applied scenario testing usingMonte Carlo simulation of indices used in IUL products. The statistical variance ofindices leads to vastly different results than what is currently demonstrated inmany cases, and this variance may cause the failure of the policy. Our researchindicates a better method for demonstrating policy performance would be basedon an outcome analysis rather than the static method currently in use.Bios: Songyu Yan: https://www.linkedin.com/in/songyu-yan-826bb2126/          Ian Luo: https://www.linkedin.com/in/yifei-luo-9051b1170/

Abstract:

The Langlands Program, connecting algebra, analysis and geometry in diverse ways, is foundational to modern number theory. I will introduce this program and indicate some recent progress. As we shall see, a great deal still remains to be done.

Abstract:

We analyse compactifications of 11 dimensional or type II supergravity down to 4 dimensional Minkowski space for generic flux and generic internal Killing spinors. We note the failure of conventional differential geometry to capture the generic features of the theory and show that the correct formalism comes in the form of a closed form Leibniz algebroid - or as we call it in the physics community, generalised geometry. Our structure is similar to the generalised geometry of Hitchin, but now the structure group is the non-compact exceptional group E_{7(7)}x R^{+}. It turns out that having N=1 supersymmetry in the effective theory on Minkowski space is equivalent to an integrable SU(7) structure on the generalised tangent bundle. We provide the tensors that define the SU(7) structure and give the integrability conditions. Finally we provide an expression for the Kahler potential on the space of structures, the superpotential of the lower dimensional theory, and we explore the moduli of these structures giving explicit answers in certain cases.

Abstract:

Using form methods, one can solve linear parabolic PDE in divergence form with $L^2$ data in appropriate energy spaces, even when the coefficients are merely bounded measurable in time and space, and no maximum principle is available. This goes back, at least, to the work of Lions and his school in the 1950s. When dealing with $L^p$ data, it is not so clear which $L^p$ like solution space one should use as a replacement of Lions’ energy space. Depending on the choice, one can solve, for instance, time dependent single equations (Aronson 1968), time independent systems for a range of values of $p$ (Auscher 2005), or stochastic problems with some spatial regularity (starting with Krylov 1994).
In this talk, I explain that, by choosing tent spaces as solution spaces (inspired by their role in elliptic boundary value problems on rough domains), one gets fairly general results, including both deterministic and stochastic problems, all values of $p$, systems as well as single equations, and rough coefficients in both time and space.

This summarises joint works with Pascal Auscher, Sylvie Monniaux, Jan van Neerven, and Mark Veraar.

Abstract:

Collective dynamics is driven by alignment that tend to self-organize the crowd and by different external forces that keep the crowd together. Different emerging equilibria are self-organized into clusters, flocks, tissues, parties, etc.

I will overview recent results on the hydrodynamics of large-time, large-crowd collective behavior, driven by different “rules of engagement”. In particular, I address the question how short-range interactions lead, over time, to the emergence of long-range patterns, comparing geometric vs. topological interactions.

Abstract:

Chemotaxis is a movement of micro-organisms or cells towards the areas of high concentration of a certain chemical, which attracts the cells and may be either produced or consumed by them. In its simplest form, the chemotaxis model is described by a system of nonlinear PDEs: a convection-diffusion equation for the cell density coupled with a reaction- diffusion equation for the chemoattractant concentration. It is well-known that solutions of such systems may develop spiky structures or even blow up in finite time provided the total number of cells exceeds a certain threshold. This makes development of numerical methods for chemotaxis systems extremely delicate and challenging task.

In this talk, I will present a family of high-order numerical methods for the Keller-Segel chemotaxis system and several related models. Applications of the proposed methods to to multi-scale and coupled chemotaxis–fluid system and will also be discussed.

Abstract:

Recently Corteel and Kim introduced lecture hall tableaux in their study of multivariate little q-Jacobi polynomials. In this talk, we enumerate bounded lecture hall tableaux. We show that their enumeration is closely related to standard and semistandard Young tableaux. We also show that the number of bounded lecture hall tableaux is the coefficient of the Schur expansion of s?(m+y1,...,m+yn). To prove this result, we use two main tools: non-intersecting lattice paths and bijections. In particular we use ideas developed by Krattenthaler to prove bijectively the hook content formula. This is joint work with Sylvie Corteel.

Abstract:

A numerical semigroup is a subset of N that is closed under addition, contains 0 and has finite complement in N. There are several fundamental invariants of a numerical semigroup S among which are the Frobenius number and genus of S, denoted F(S) and g(S), respectively. The quotient of a numerical semigroup S by a positive integer d is the set S/d={x|dx?S} which is also a numerical semigroup. In this talk, I will present some recent results showing the relation between the genus of S/d and the genus of S. If time permits, we will also talk about identities relating the Frobenius numbers and the genus of quotients of numerical semigroups that are generated by certain types of arithmetic progressions. This is joint work with S. Butler, C. Defant, Y. Gao, P.E. Harris, C. Hettle, Q. Liang, H. Nam, and A. Volk.

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.