In the first part of this talk we introduce a classical family of symmetric polynomials called Schur polynomials and discuss some of their properties, and a problem that arises from their study, namely the combinatorial interpretation of Littlewood–Richardson coefficients.

In the second part, we explain how the work of Robinson, Schensted, Knuth, Lascoux, and Schuetzenberger on words in an ordered alphabet led to a solution of this combinatorial problem. We will then mention some relatively recent developments in this subject.

Weyl’s law gives an asymptotic formula for the number of eigenfunctions of the Laplace-Beltrami operator on a Riemannian manifold. In 2005, Elon Lindenstrauss and Akshay Venkatesh gave a proof of this law for quite general quotients of semisimple Lie groups. The proof crucially uses the fact that solutions of the corresponding wave equation propagate with finite speed. I will try to explain what they did in the simplest setting of the upper half plane. I will also try to explain why such eigenfunctions, also known as automorphic forms, are of central importance in number theory. The first 45 minutes of the talk should be accessible to students who have a knowledge of some basic complex analysis and calculus.

A geodesic conjugacy between two closed manifolds is a homeomorphism between their unit tangent bundles that takes geodesic flow orbits of one to that of the other in a time-preserving manner. One of the central problems in Riemannian geometry is to understand the extent to which a geodesic conjugacy determines a closed Riemannian manifold itself. While an answer to the question in this generality has yet remained elusive, we give an overeview of results on closed surfaces – the most important illustrative case where a complete picture about questions of geodesic conjugacy rigidity is available.

I will present work done with students and colleagues on the collective behaviour of motile organisms, viewed as interacting particles with an autonomous velocity and noise. The talk will include a bit of stochastic processes, some statistical mechanics, and some hydrodynamics. I will discuss experiments, analytical theory and some computation.

Let X1 , X2 , X3, … Xn be iid random variables. Laws of large numbers roughly state that the average of these variables converges to the expectation value of each of them when n is large. Various forms of these laws have many applications. The strong and weak laws along with the following three applications will be discussed: a) Coin-tossing. b) The Weierstrass approximation theorem. c) The Glivenko–Cantelli theorem.

In the second half of this talk, a law of large numbers is proven for spaces with infinite “volume” (measure) as opposed to the above version for probability measures (“volume” =1).

Terence Tao posted on his blog a question of Apoorva Khare, asking whether the free group on two generators has a length function $l: F_2 \to\mathbb{R}$ (i.e., satisfying the triangle inequality) which is *homogeneous*, i.e., such that $l(g^n) = nl(g)$. A week later, the problem was solved by an active collaboration of several mathematicians (with a little help from a computer) through Tao’s blog. In fact a more general result was obtained, namely that any homogeneous length function on a group $G$ factors through its abelianization $G/[G, G]$.

I will discuss the proof of this result and also the process of discovery (in which I had a minor role).

In this talk, we prove the unique factorization property of Schur functions. This fundamental property of Schur functions was first observed and proved by C. S. Rajan in 2004. I give a different proof of this beautiful fact which I jointly obtained with my adviser S. Viswanath. I begin my talk with introducing the Schur functions and their connections with representation theory of general linear groups. Basic knowledge of elementary algebra will be assumed like group theory and linear algebra. If time permits, I will tell you about the possible generalizations of this result.

We consider the computation of n-variate polynomials over a field F via a sequence of arithmetic operations such as additions, subtractions, multiplications, divisions, etc. It has been known for at five decades now that a random n-variate polynomial of degree n is hard to compute. Yet not a single explicit polynomial is provably known to be hard to compute (although we have a lot of good candidates). In this talk we will first describe this problem and its relationship to the P vs NP problem. We will then describe several partial results on this problem, both old and new, along with a more general approach/framework that ties together most of these partial results.

I will give a gentle historical (and ongoing) account of matrix positivity and of operations that preserve it. This is a classical question studied for much of the past century, including by Schur, Polya-Szego, Schoenberg, Kahane, Loewner, and Rudin. It continues to be pursued actively, for both theoretical reasons as well as applications to high-dimensional covariance estimation. I will end with some recent joint work with Terence Tao (UCLA).

The entire talk should be accessible given a basic understanding of linear algebra/matrices and one-variable calculus. That said, I will occasionally insert technical details for the more advanced audience. For example: this journey connects many seemingly distant mathematical topics, from Schur (products and complements), to spheres and Gram matrices, to Toeplitz and Hankel matrices, to rank one updates and Rayleigh quotients, to Cauchy-Binet and Jacobi-Trudi identities, back full circle to Schur (polynomials).

We shall consider sampling procedures to construct subgraphs to infer properties of a network. Specifically, we shall consider sampling procedures in the context of dense and sparse graph limits. We will explore open questions and interesting explorations at the undergraduate level. The talk will be accessible to a general audience.

Given a metric space (X, d), there are several notions of it being negatively curved. In this talk, we single out certain consequences of negative curvature – which are themselves weaker than (X, d) being negatively curved – that turn out to be very useful in proving results about holomorphic maps. We shall illustrate what this means by giving a proof of the Wolff-Denjoy theorem. (This theorem says that given a holomorphic self-map f of the open unit disc, either f has a fixed point in the open unit disc or there exists a point p on the unit circle such that ALL orbits under the successive iterates of f approach p.) For most of this talk, we shall focus on metric spaces or on geometry in one complex variable. Towards the end, we shall briefly point out what can be proved in domains in higher dimensions that have the aforementioned negative-curvature-type properties. This part of the talk is joint work with Andrew Zimmer.

I will introduce the concepts of backward, forward, and covariant Lyapunov vectors (that are basically “eigenfunctions” of certain operators) for a dynamical system and discuss their relevance in studying various stability results, both for the system itself and for nonlinear filtering problem, which will also be introduced along the way. The “relevance” to nonlinear filters is in the form of open questions whose answers (as a set of conjectures) may be gleaned from numerical results and linear filtering theory.

Given n random points on a manifold embedded in a Euclidean space, we wish to understand what topological features of the manifold can be inferred from the geometry of these points. One procedure is to consider union of Euclidean balls of radius r around the points and study the topology of this random set (i.e., the union of balls). A more robust method (known as persistent homology) of inferring the topology of the underlying manifold is to study the evolution of topology of the random set as r varies. What topological information (for example, Betti numbers and some generalizations) about the underlying manifold can we glean for different choices of r ? This question along with some partial answers in the recent years will be the focus of the talk. I shall try to keep the talk mostly self-contained assuming only knowledge of basic probability and point-set topology.

The formalism of an “abelian category” is meant to axiomatize the operations of linear algebra. From there, the notion of “derived category” as the category of complexes “upto quasi-isomorphisms” is natural, motivated in part by topology. The formalism of t-structures allows one to construct new abelian categories which are quite useful in practice (giving rise to new cohomology theories like intersection cohomology, for example). In this talk we want to discuss a notion of punctual (=”point-wise”) gluing of t-structures which is possible in the context of algebraic geometry. The essence of the construction is classical and well known, but the new language leads to useful constructions in the motivic world.

Graph-partitioning problems are a central topic of research in the study of algorithms and complexity theory. They are of interest to theoreticians with connections to error correcting codes, sampling algorithms, metric embeddings, among others, and to practitioners, as algorithms for graph partitioning can be used as fundamental building blocks in many applications. One of the central problems studied in this field is the sparsest cut problem, where we want to compute the cut which has the least ratio of number of edges cut to size of smaller side of the cut. This ratio is known as the expansion of the cut. In this talk, I will talk about higher order variants of expansion (i.e. notions of expansion corresponding to partitioning the graph into more than two pieces, etc.), and how they relate to the graph’s eigenvalues. The proofs will also show how to use the graph’s eigenvectors to compute partitions satisfying these bounds. Based on joint works with Prasad Raghavendra, Prasad Tetali and Santosh Vempala.

Understanding the origins of intermittency in turbulence remains one of the most fundamental challenges in applied mathematics. In recent years there has been a fresh attempt to understand this problem through the development of the method of Fourier decimation. In this talk, we will review these recent results and analyse the role of precise numerical simulations in understanding the mathematics of the Navier-Stokes and Euler equations.

It is generally well known that there is an innate notion of things like isomorphisms or epimorphisms. This allows us to talk about isomorphisms or epimorphisms of various objects: groups, rings, algebras, etc. In other words, “isomorphism” is really a categorical notion. However, it is not so well known that finiteness itself is alsocategorical. In this talk, we will discuss how finiteness applies to various categories. This will allow usto see finite sets, finite dimensional vector spaces, finitely generated algebras and compact sets as manifestations of the same basic idea.

In this talk I will present algorithmic applications of an approximate version of Caratheodory’s theorem. The theorem states that given a set of $d$-dimensional vectors $X$, for every vector in the convex hull of $X$ there exists an epsilon-close (under the $p$-norm, for $2 \leq p < \infty$) vector that can be expressed as a convex combination of at most $b$ vectors of $X$, where the bound $b$ depends on epsilon and the norm $p$, and is independent of the ambient dimension $d$. This theorem can be obtained by instantiating Maurey’s lemma (c.f. Pisier 1980/81 and Carl 1985).

I will describe how this approximate version of Caratheodory’s theorem leads novel additive approximation algorithms for finding (i) Nash equilibria in two-player games (ii) dense subgraphs.

A classical theorem in Riemannian geometry asserts that products of compact manifolds cannot admit Riemannian metrics with negative sectional curvature. A fibration (or a fibre bundle) is a natural generalization of a product and hence one can ask if a fibration can admit such a metric. This question is still open and I will discuss it in the context of Kahler manifolds. This leads to the study of graphs of holomorphic motions, which originally arose in complex dynamics. I will sketch a proof that the graph cannot be biholomorphic to a ball or more generally, a strongly pseudoconvex domain.

This talk is about computing (approximately) the “best” map between two polygons taking vertices to vertices. It arises out of a real-life problem, namely, surface registration. Our notion of “best” is extremal quasiconformality (least angle-distortion). I will try to keep the talk as self-contained as possible. It is based on a joint-work with M. Goswami, G. Telang, and X. Gu.

One of the earliest instance of integral geometric discussion can be traced back to Buffon through the “needle problem”. The solution, as we know now, is based on a platform of theory of measures on geometrical spaces which are invariant under some group operations. In this talk, we shall walk through this subject by following a specific line of problems/results called “kinematic fundamental formulae” by studying some specific examples.

Inverse spectral theory in one dimension is to recover a self-adjoint operator given its spectrum and some spectral measure. In general there are lots of self-adjoint operators (of a given form) this collection is called an iso-spectral set. We will give a brief introduction to the questions in this area and also some connections to other areas of mathematics.

I will introduce minimal surfaces and explain the Weierstrass-Enneper representation of a minimal surface using hodographic coordinates. I will mention an interesting link between minimal surfaces and Born-Infeld solitons. If time permits, I will explain my work (with P. Kumar and R.K. Singh) on interpolation of two real analytic curves by a minimal surface of the Bjorling-Schwartz type.

Owing to several applications in large scale learning and vision problems, fast submodular function minimization (SFM) has become a very important problem. Theoretically, unconstrained SFM is polynomial time solvable, however, these algorithms are not practical. In 1976, Wolfe proposed a heuristic for projection onto a polytope, and in 1980, Fujishige showed how Wolfe’s heuristic can be used for SFM. For general submodular functions, this Fujishige-Wolfe heuristic seems to have the best empirical performance. Despite its good practical performance, very little is known about Wolfe’s projection algorithm theoretically.

In this talk, I will describe the first analysis which proves that the heuristic is polynomial time for bounded submodular functions. Our work involves the first convergence analysis of Wolfe’s projection heuristic, and proving a robust version of Fujishige’s reduction theorem.

There exists various possible methods to distribute seats proportionally between states (or parties) in a parliament. In the first half of the talk I will describe some often used methods and discuss their pros and cons (it’s all in the rounding).

One easy method is called Hamilton’s method (also known as the method of largest reminder or Hare’s method). It suffers from a drawback called the Alabama paradox, which e.g. made USA abandon it for the distribution of seats in the house of representatives between states. It is still in use in many other countries including Sweden.

In the second half of the talk I will describe a joint work with Svante Janson (Uppsala Univ.) where we study the probability that the Alabama paradox will happen. We give, under certain assumptions, a closed formula for the probability that the Alabama paradox occurs given the vector $p_1,\dots,p_m$ of relative sizes of the states.

Eigenvalues and eigenvectors appear in many physical and engineering problems, beginning with the noted study by Euler in 1751 of the kinematics of rigid bodies. From a mathematical point of view, for an operator of a suitable class, acting in a vector space, its point spectrum and associated subspaces refer to its “eigenvalues and eigenvectors”, while the subspaces associated with the continuous spectrum of the operator is said to consist of “eigenfunctions”. The basic ideas will be discussed mostly through examples, in some of which a natural connection with the representation of appropriate groups lurks behind.

This talk shall be an introduction to some aspects of Teichmüller theory, which is concerned with the parameter space of marked Riemann surfaces. I shall describe Wolf’s parametrization of Teichmüller space using harmonic maps, and discuss how it extends to the Thurston compactification. If time permits, I will describe some recent joint work with Wolf.

We get useful insight about various algebraic structures by studying their automorphisms and endomorphisms. Here we give a brief introduction to the theory of semigroups of endomorphisms of the algebra of all bounded operators on a Hilbert space.

The usual foundations of mathematics based on Set theory and Predicate calculus (and extended by category theory), while successful in many ways, are so far removed from everyday mathematics that the possibility of translation of theorems to their formal counterparts is generally purely a matter of faith. Homotopy type theory gives alternative foundations for mathematics. These are based on an extension of type theory (from logic and computer science) using an unexpectedly deep connection of Types with Spaces discovered by Voevodsky and Awodey-Warren. As a consequence of this relation we also obtain a synthetic view of homotopy theory. In this lecture, I will give a brief introduction to this young subject.

It is well known that finding an explicit solution for partial differential equations is almost impossible in most of the cases. In this talk we will give a brief introduction to the analysis of certain nonlinear PDEs using various tools from analysis. We will discuss some concrete examples and some open problems (depending on time). The talk should be accessible to senior undergraduate students.

Independent component analysis (ICA) is a basic problem that arises in several areas including signal processing, statistics, and machine learning. In this problem, we are given linear superpositions of signals. E.g., we could be receiving signals from several sensors but the receivers only get the weighted sums of these signals. The problem is to recover the original signals from the superposed data. In some situations this turns out to be possible: the main assumption being that the signals at different sensors are independent random variables. While independent component analysis is a well-studied problem, one version of it was not well-understood, namely when the original signals are allowed to be heavy-tailed, such as those with a Pareto distribution. Such signals do arise in some applications. In this talk, I will first discuss the previously known algorithms for ICA and then a new algorithm that applies also to for the heavy-tailed case. The techniques used are basic linear algebra and probability.

We consider integral lattices $L$ in an Euclidean space $V = \mathbb{R}^m$, i.e. $\mathbb{Z}$-submodules of full rank in $V$ such that all vectors in $L$ have integral length. It is impossible to classify such lattices up to isometry, there are just too many of them in general, even if we ﬁx additional invariants such as the discriminant. Therefore one looks for interesting subclasses of lattices, in particular “extremal lattices”, characterized by the property that the smallest length of a non-zero vector in $L$ is “as large as possible”. There are several ways to make this more precise, we will focus on analytic extremality, where modular forms come in. In particular, we will consider extremality for maximal lattices.

Our vision is unsurpassed by machines because we use a sophisticated object representation. This representation is unlike the retinal image: on the one hand, two out-of-phase checkerboards, maximally different in image pixels, appear perceptually similar. On the other hand, two faces, similar in their image pixels, appear perceptually distinct. What is then the nature of perceptual space? Are there principles governing its organization? To address these questions, we have been using visual search to measure similarity relations between objects.

I will summarize a line of research from our laboratory indicative of a surprising linear rule governing distances in perceptual space. In the first study, we found that search time is inversely proportional to the feature difference between the target and distracters. The reciprocal of search time is therefore linear and interestingly, it behaved like a mathematical distance metric. It also has a straightforward interpretation as a saliency signal that drives visual search (Arun, 2012). In a second study, complex searches involving multiple distracters were explained by a linear sum of pair-wise dissimilarities measured from simpler searches involving homogeneous distracters (Vighneshvel & Arun, 2013). In a third study, dissimilarities between objects differing in multiple features were found to combine linearly. This was also true for integral features such as the length and width of a rectangle upon including aspect ratio as an additional feature (Pramod & Arun, 2014). Finally, I will describe some recent results extending these findings to more naturalistic objects.

Arun and Olson (2010) conducted a visual search experiment where human subjects were asked to identify, as quickly as possible, an oddball image embedded among multiple distractor images. The reciprocal of the search times for identifying the oddball (in humans) and an ad hoc neuronal dissimilarity index, computed from measured neuronal responses to component images (in macaques), showed a remarkable correlation. In this talk, I will describe a model, an active sequential hypothesis testing model, for visual search. The analysis of this model will suggest a natural alternative neuronal dissimilarity index. The correlation between the reciprocal of the search times and the new dissimilarity index continues to be equally high, and has the advantage of being firmly grounded in decision theory. I will end the talk by discussing the many gaps and challenges in our modeling and statistical analysis of visual search. The talk will be based on ongoing work with Nidhin Koshy Vaidhiyan (ECE) and S. P. Arun (CNS).

Weyl discovered a decomposition of the invariant probability measure on a connected compact Lie group, according to elements of a maximal torus and their conjugacy classes. In the case of the unitary group, it allowed Diaconis and Shahshahani to access the distribution of the eigenvalues of a random unitary matrix. We explore these ideas in the lecture.

Consider the problem of n cards labelled 1 through n, lying face up on a table. Suppose two integers a and b are chosen independently and uniformly between 1 and n. The cards labelled a and b are switched. If many such transpositions are made the row of cards will tend to appear in random arrangement. Then question is how many steps are required until the deck is well mixed up (i.e. the permutation is close to random)? Diaconis and Shahshahani used tools of representation theory of symmetric groups to prove that at least 1/2(n log n) steps are required before the deck will be well mixed up for large n. I will explain ideas of their proof and few related problems.

A Kronecker coefficient counts the number of times a representation of a symmetric group occurs in the tensor product of two others. Finding a fast algorithm to determine when a Kronecker coefficient is positive is an open problem. There has been an increased interest in this problem over the last few years as it comes up in the geometric approach to the complexity conjecture P = NP due to Mulmuley and Sohoni. I will explain how a higher dimensional analogue of the Robinson–Schensted–Knuth correspondence relates Kronecker coefficients to the problem of counting the number of integer arrays with specified slice sums.

In the first half of the talk, I will define the dimer model on planar graphs and prove Kasteleyn’s groundbreaking result expressing the partition function (i.e. the generating function) of the model as a Pfaffian. I will then survey various results arising as a consequence, culminating in the beautiful limit shape theorems of Kenyon, Okounkov and coworkers.

In the second half, I will define a variant of the monomer-dimer model on planar graphs and prove that the partition function of this model can be expressed as a determinant. I will use this result to calculate various quantities of interest to statistical physicists and end with some open questions.

simpcomp is an official extension to the computer algebra system GAP, that is, simpcomp is part of every full GAP installation. The software allows the user to compute numerous properties of abstract simplicial complexes such as the f -, g- and h-vectors, the fundamental group, the automorphism group, (co-)homology, the intersection form, and many more. It provides functions to generate simplicial complexes from facet lists, orbit representatives, or permutation groups. In addition, functions related to slicings and polyhedral Morse theory as well as a combinatorial version of algebraic blowups and the possibility to resolve isolated singularities of 4-manifolds are implemented.

In this talk, I will give an overview over the existing features of the software and also discuss some ongoing projects such as (i) support for Forman’s discrete Morse theory for homology and fundamental group computations, and (ii) an extension of the features to analyse tightness of triangulated manifolds.

In this talk we explore new approaches to the old and difficult computational problem of unknot recognition. Although the best known algorithms for this problem run in exponential time, there is increasing evidence that a polynomial time solution might be possible. We outline several promising approaches, in which computational geometry, linear programming and greedy algorithms all play starring roles.

We finish with a new algorithm that combines techniques from topology and combinatorial optimisation, which is the first to exhibit “real world” polynomial time behaviour: although it is still exponential time in theory, exhaustive experimentation shows that this algorithm can solve unknot recognition for “practical” inputs by running just a linear number of linear programs.

Abstract: See https://drive.google.com/file/d/0Bx6ccM3uL81GNHZqQUtic3g5TndEMmF3Y0JKSDl3LTBTeFU4/view?usp=sharing.

The goal of these talks is to discuss the history, the circumstances, and (the main aspects of) the proof of a recent result of ours, which says: a proper holomorphic map between two nonplanar bounded symmetric domains of the same dimension, one of them being irreducible, is a biholomorphism.

Results of this character are motivated by a remarkable theorem of H. Alexander about proper holomorphic self-maps of the Euclidean ball. The first part of the talk will focus on the meanings of the terms in the above theorem. We will give an elementary proof that the automorphism group of a bounded symmetric domain acts transitively on it. But the existence of an involutive symmetry at each point is restrictive in many other ways. For instance, it yields a complete classification of the bounded symmetric domains. This follows from the work of Elie Cartan.

Earlier results in the literature that are special cases of our result focused on different classes of domains in the Cartan classification, and have mutually irreconcilable proofs. One aspect of our work is a unified proof: a set of techniques that works across the Cartan classification. One of those techniques is the use of an algebraic gadget: the machinery of Jordan triple systems.

We will present definitions and examples related to this machinery in the first part of the talk. In the second part of the talk, we shall state a result on finite-dimensional Jordan triple systems that is essential to our work. After this, we shall discuss our proof in greater depth.

Given n non-zero real numbers v_1,…,v_n, what is the maximum possible number of subsets {i_1,…,i_k} that can have the same subset sum v_{i_1}+…+v_{i_n}? Littlewood and Offord raised the question, showed that the answer is at most 2^nlog(n)/\sqrt{n} and conjectured that the log(n) can be removed. Erdos proved the conjecture and raised more questions that have continued to attract attention, primarily relating the arithmetic structure of v_1,…,v_n and the maximum number of subsets with a given subset-sum.

In the first lecture, we shall review and prove several of these results (due to Littlewood–Offord, Erdos, Moser, Stanley, Halasz, Tao–Vu, Rudelson–Vershynin…) and show an application to random matrices.

In the second part, we start with a question in random polynomials and see how it leads to a variant of the Littlewood–Offord problem that has not been considered before.

Most of the material presented should be accessible to undergraduate students. Much of the lecture is an outcome of my joint study in summer with Sourav Sarkar (ISI, Kolkata).

Motivated by applications, we introduce a class of optimization problems with constraints. Difficulties in solving these problems are highlighted. Mathematical developments to overcome these difficulties are discussed.

I will discuss the negative curvature properties of certain intrinsic metrics in complex analysis. The talk will be accessible to graduate students.

A great progress on the twin prime problem was made last year by Yitang Zhang and it made him famous in mathematical circles almost overnight. He proved the existence of a positive real number M such that there are infinitely many pairs of primes that differ by at most M. The twin prime conjecture predicts that M can be taken to be two. I shall give an overview of the works leading up to Zhang’s spectacular achievement.

Quadrature domains are those on which holomorphic functions satisfy a generalized mean value equality. The purpose of this talk will be to reflect on the Schwarz reflection principle and to understand how it leads to examples of quadrature domains.

In this talk we address the problem of integrating entire functions (of several complex variables) in ‘polar coordinates’!

Wolfgang Kuhnel introduced the notion of tightness and conjectured that all tight triangulations of closed manifolds are strongly minimal. In a recent paper with B. Datta (European J. Comb. 36 (2014), 294–313), the speaker obtained a very general combinatorial criterion for tightness and found results in partial confirmation of this conjecture. We shall discuss these results.

Given a L1 function f over an infinite measure space S how does one estimate the integral of f using statistical tools? In this talk we propose using regenerative sequences with heavy tails to do this. We obtain a consistent estimator and show the rate of convergence is slower than $1/\sqrt{n}$. When S is countable the SSRW works.

I will talk about techniques to approximate real functions such as $x^s,$ $x^{-1}$ and $e^{-x}$ by simpler functions and how these results can be used in the design of fast algorithms. The key lies in the fact that such approximations imply faster ways to approximate primitives such as $A^sv,$ $A^{-1}v$ and $\exp({-A})v$, and in the computation of matrix eigenvalues and eigenvectors. Indeed, many fast algorithms reduce to the computation of such primitives, which have proved useful for speeding up several fundamental computations, such as random walk simulation, graph partitioning, solving linear systems of equations, and combinatorial approaches for solving semi-definite programs.