We show how algebraic relations can be encoded in suggestive topological diagrams and use this to prove various algebraic equations in a purely pictorial way. We will first go over a few canonical examples: monoids, self-dual objects, Frobenius algebras, and monads. Then we will briefly discuss the underlying theory that makes this miracle rigorous.

In this talk, I will use my recent project (a fast algorithm for calculating the energy of a many-body quantum system in the random phase approximation) as an outline to present two cool techniques in applied math and show their actual applications in the project. First, we'll see that the trapezoid rule you teach in Calculus, when applied to periodic functions, is far more impressive than you thought. We'll also get a taste of the surprisingly nice properties that come from combining matrix decompositions with randomized algorithms. Finally, as an added bonus, we'll see how Cauchy's integral formula can be used (in this project) to sum N^2 things in O(N) time.

This talk will discuss some of the basic ideas pervasive in computational topology, especially persistent homology. Then, we will look at some currently active areas of application for these ideas, including large data sets, sensor networks, protein docking, plant root systems, and natural images. I assume no topology background, so this talk will be a good introduction to anyone interested in seeing what is going on in the field.

Abstract: In this talk I will give a quick overview of Einstein's theory of general relativity. I will then move on to discuss the mystery of dark matter: why astrophysicists think it's out there in the universe and what phenomena any successful theory of dark matter will have to explain. One such phenomenon is the Tully-Fisher relation, an intriguing correlation between the visible mass of galaxies and the rotational velocities of their stars. I will wrap up by describing a theory of wave dark matter and how it could possibly explain the Tully-Fisher relation.

In the first part of this talk I will review the basic properties of the Yamabe problem on compact manifolds and recall some well-known results of LeBrun concerning the computation of the Yamabe invariant in dimension four. In the second part I will suggest some possible generalizations for finite volume 4-manifolds. Hopefully some grad student will want to resume this work were I left it...

The weak law of large numbers states that for a sequence of independent identically distributed random variables of finite mean, their average converges to this mean in probability as the number of terms tends to infinity. Well, how fast? That is, how many draws must we make before we see this behavior? For many distributions, the convergence is exponentially fast in the number of terms. Such behavior is a hallmark of concentration of measure: under suitable conditions, well behaved functions of many random variables do not deviate much from a particular value. In this talk, we'll show that such properties are not mysterious, but can be derived from a simple recipe using a few choice inequalities. Examples in both the discrete & continuous settings will be given, making connections with convex geometry.

Coarsening is the phenomenon where many objects (water drops, molecular islands, particles in a freezing liquid) becoming a smaller number of objects in an orderly way. This talk will examine modeling one such system, tiny liquid drops, through three models: a PDE for the fluid, a coarsening dynamical system for the drops, and an LSW-type ensemble model for the group of drops. We will study self-similarity in the dynamics and extensions of the model to examine very long times when drops grow large enough that gravity distorts their shape.

Approximating the permanent of a matrix with nonnegative entries is a well studied problem. The most successful approach to date uses Markov chains, and Jerrum, Sinclair, and Vigoda developed such a method that runs in polynomial time O(n^7 (log n)^4). We present a very different approach using self-reducible acceptance/rejection, and show that for a class of dense problems, our method has an O(n^4 log n) expected running time. Also, we extend our approach to approximate the number of perfect matchings in non-bipartite graphs and general matchings in general graphs.

I will begin by recounting how I got involved in an industrial collaboration with a company that makes glass tempering furnaces and how younger grad students can seek such opportunities. I will then describe the mathematical model I developed for the company while highlighting challenges that arose due to differences in culture and priorities between academia and industry.

Abstract: Modular forms, a kind of SL2(Z)-invariant holomorphic functions defined on the upper half plane, are one of the most important objects studied in modern number theory. This talk will start from the basic definitions of modular forms and give some examples and important theorems associated with Eisenstein series. Finally we will use the power of modular forms to solve some classical problems on partitions of integers in additive number theory, including the Ramanujan congruence and sums of squares.

The Positive Mass Theorem in general relativity states that a spacelike hypersurface of a spacetime satisfying the dominant energy condition must have nonnegative total mass. In the special case in which the hypersurface is totally geodesic, local energy density coincides with scalar curvature, and the above theorem becomes a purely geometric statement about complete, asymptotically flat Riemannian manifolds. I will try to present the necessary background for one to understand the statement of the theorem. I will also discuss attempts to better understand the relationship between scalar curvature and total mass. Note that this talk is especially geared towards early graduate students and people specializing in other fields, and thus I will assume no previous knowledge of smooth manifolds, Riemannian geometry or general relativity.

Tropical Geometry is a relatively recent field, that uses combinatorics to study algebraic geometry. In this talk, I'll introduce the tropical semiring, and the idea of tropical monomials and polynomials. We'll look at some examples of tropical hypersurfaces, and if there is time I will introduce the idea of a tropical variety, and it's relationship to the traditional algebraic variety of a curve.

TBA

This talk is about the problem of packing subspaces of real/complex space so that they are as far apart as possible, sometimes called Grassmann packing. For a particular distance metric, the problem is equivalent to finding a matrix with minimum group coherence. I will give a fundamental lower bound on group coherence, and then describe some matrix constructions based on Kronecker products which achieve the lower bound on group coherence, and which therefore give optimal subspace packings. Then I will talk about results which show that random subspace packings are close to optimal asymptotically. I will also say something about why good subspace packings are currently of interest in signal processing applications. This work is joint with Robert Calderbank and Yao Xie (now at Georgia Tech). The math involved is mainly linear algebra and random matrix theory, but I will take care to make the talk accessible to all.

Knots, while combinatorial in flavor, play a key role in the topology of manifolds in three and four dimensions. I'll discuss this role and describe some classical problems about knots that were surprisingly solved only recently through high-powered techniques. Gauge theory, symplectic geometry, and the Poincare conjecture may make cameo appearances.

In this talk we look at a specific problem in probability related to the stochastic versions of the Burgers' and Navier-Stokes equations, and the path taken to construct sufficient assumptions to prove the desired properties, specifically the existence of an invariant distribution. This talk covers material in Stochastic Differential Equations and Stochastic Partial Differential Equations, but also in Real Algebraic Geometry and Perturbation Theory.

I will motivate the need to analyze data sets in high dimensions, their geometrical properties and the properties of functions on them with several examples. I will focus on techniques based on random walks on data sets, and introduce a new nonlinear system of coordinates based on heat kernels, similar in spirit to the GPS system, for parametrizing data sets. If time allows, I will also discuss simple but surprisingly successful applications of the heat kernel to fit functions on data, that performs at the state-of-art or better as a classifier on a variety of benchmark data sets.

Given a metric space and a positive scale parameter, the Vietoris-Rips simplicial complex has a vertex for each point in the metric space, and contains a set of vertices as a simplex if its diameter is less than the scale parameter. I will introduce these complexes and survey how they are used: originally to define a homology theory for metric spaces, later to study hyperbolic groups, and more recently to analyze high-dimensional data. A theorem of Jean-Claude Hausmann states that if the metric space is a Riemannian manifold and if the scale parameter is sufficiently small, then the Vietoris-Rips complex is homotopy equivalent to the original manifold. What happens for larger scale parameters? We will see that as the scale parameter increases, the Vietoris-Rips complex of the circle obtains the homotopy type of the circle, the 3-sphere, the 5-sphere, the 7-sphere, ..., until finally it is contractible.

The prevailing numerical methods for solving the reaction-diffusion systems in models of cardiac electrical activity currently use second-order adaptive mesh refinement, refining the spatial and temporal meshes only near the traveling wavefront(s). However, in two and three spatial dimensions under biologically relevant initial conditions and forcing, these wavefronts can constitute a relatively high percentage of the computational domain, limiting the effectiveness of the scheme. In this talk, I will present a numerical scheme based on higher order finite elements and spectral deferred correction designed to improve the efficiency in computing for domains of cardiac cells.

One fundamental object in general relativity is the notion of mass. Pointwise energy density and global mass of a spacetime are both well-defined notions of mass. However, the questions of what goes in between the two as the local mass of a given region, and how it relates to the pointwise and global mass are still not well understood. In the case that the spacetime admits a totally geodesic asymptotically flat spacelike slice, the Riemannian Penrose Inequality states that the mass of this slice is lower bounded by the mass of the blackholes. This inequality was proved by Huisken and Ilmanen using inverse mean curvature flow, and by Bray using a different flow. The general Spacetime Penrose Conjecture, which does not assume the existence of such a totally geodesic slice, is still open today. One viable approach is to use the inverse mean curvature vector flow. Such flows do not have a good existence theory. In this talk, we introduce the basic ideas of inverse mean curvature vector flow, and show that there exist many spacetimes in which smooth solutions to such flows exist for all time.

In many problems in fluids and electromagnetics, we may formulate solutions to the Dirichlet and Neumann problems in terms of double and single layer potentials. Such boundary integral representations can result in computational difficulties at points on and near the boundary due to singularities and near singularities. The case of a smooth closed boundary has been well-studied, but I will focus on computational issues that arise from a boundary that is only piecewise smooth, consisting of connected curve segments. I will give an overview of my research in approximating singular and nearly singular integrals, as well as discuss an approach for the computation of double layer potentials at points on and near a curve segment.

Category theory can be described as a general mathematical theory of structures and of systems of structures. Originally developed in the 40's by Saunders Mac Lane and Samuel Eilenberg in the context of algebraic topology, category theory has since grown to serve as both an organizational tool in many areas of mathematics and as a deep theory connecting these areas. The aims of this talk are 3-fold: first, to introduce the basic notions of category theory and to give a wide range of examples; second, to show how abstract results in category theory can influence the way we think about mathematics; finally, to show how a knowledge of some general results in category theory can save us time and effort in our day to day mathematical work. Since I will be starting with the basics, this talk should be accessible to a wide audience. Students who are considering working in algebra, geometry, or topology are particularly encouraged to attend, as are any students who have ever wondered why I love covering the chalkboards of 274F with crazy-looking diagrams or why the word `natural' is the fifth most used word in my vocabulary.

There are many different ways to make a compact 2-manifold of genus g into a Riemann surface. In fact, there is an entire space of dimension 3g-3 (when g>1) of possible holomorphic structures. This space is called the moduli space of Riemann surfaces of genus g. We will give a definition of moduli spaces and briefly talk about their construction, starting with the "easy" examples of g=0 and g=1. We will also talk about mapping class groups, which play an important part in the construction of moduli spaces.

Data lying in a high-dimensional ambient space are commonly thought to have a much lower intrinsic dimension. In particular, the data may be concentrated near a lower-dimensional subspace or manifold. There is an immense literature focused on approximating the unknown subspace, and in exploiting such approximations in clustering, data compression, and building of predictive models. Most of the literature relies on approximating subspaces using a locally linear, and potentially multiscale, dictionary. In this talk, we propose a simple and general alternative, which instead uses pieces of spheres, or spherelets, to locally approximate the unknown subspace. Theory is developed showing that spherelets can produce dramatically lower covering numbers and MSEs for many manifolds. We develop spherical principal components analysis (SPCA) and spherical multiscale methods. Results relative to state-of-the-art competitors show dramatic gains in ability to accurately approximate the subspace with orders of magnitude fewer components. This leads to substantial gains in data compressibility, few clusters and hence better interpretability, and much lower MSE based on small to moderate sample sizes. A Bayesian nonparametric model based on spherelets will be introduced as an application.

Locally Linear Embedding(LLE), is a well known manifold learning algorithm published in Science by S. T. Roweis and L. K. Saul in 2000. In this talk, we provide an asymptotic analysis of the LLE algorithm under the manifold setup. We establish the kernel function associated with the LLE and show that the asymptotic behavior of the LLE depends on the regularization parameter in the algorithm. We show that on a closed manifold, asymptotically we may not obtain the Laplace-Beltrami operator, and the result may depend on the non-uniform sampling, unless a correct regularization is chosen. Moreover, we study the behavior of the algorithm on a compact manifold with boundary. This talk is based on the joint work with Hau-tieng Wu.

After a brief introduction of modular forms on the upper half plane and vector-space valued differential forms, we will explore a very classical result (independently due to Eichler and Shimura) which relates certain cohomology groups to cusp forms on the upper half plane of corresponding weight. We will then put our algebraic hat on, and recast this result in modern light, using the theory of Automorphic forms developed by (among others) Harish-Chandra and Langlands. I hope to make the talk accessible to most graduate students. Though the topics we will talk about are related to my research, it is not a research talk, more of an exposition. The first part of the talk should be a breeze for anyone with understanding of some fundamental concepts in Complex analysis and Algebraic Topology (holomorphic functions, differential forms, deRham cohomology.) A course in Representation Theory would be helpful in relating to the concepts in the second part of the talk.

The notion of conformal mapping is of fundamental importance in complex analysis. Conformal maps are used by mathematicians, physicists and engineers to change regions with complicated shapes into much simpler ones, and to do so in a way that preserves shape on a small scale (that is, when viewed up close). This makes it possible to ``transpose’’ a problem that was formulated for the complicated-looking region into another, related problem for the simpler region(where it can be easily solved) -- then one uses conformal mapping to ``translate'' the solution of the problem over the simpler region, back to a solution of the original problem (over the complicated region). The beauty of conformal mapping is that its governing principle is based on a very simple idea that is easy to explain and to understand (much like the statement of Fermat's celebrated last theorem) . In the first part of this talk I will introduce the notion of conformal mapping and will briefly go over its basic properties and some of its history (including a historical mystery going back to Galileo Galilei). I will then describe some of the many real-life applications of conformal maps, including: cartography; airplane wing design (transonic flow); art (in particular, the so-called ``Droste effect’’ in the work of M. C. Escher). Time permitting, I will conclude by highlighting a 2013 paper by McArthur fellow L. Mahadevan that uses the related notion of quasi-conformal mapping to link D'Arcy Thompson's iconic work On Shape and Growth (published in 1917) with modern morphometric analysis (a discipline in biology that studies, among other things, how living organisms evolve over time). No previous knowledge of complex analysis is needed to enjoy this talk.