Seminars and Colloquia by Series

Bias in cubic Gauss sums: Patterson's conjecture

Series
Job Candidate Talk
Time
Wednesday, January 18, 2023 - 11:05 for 1 hour (actually 50 minutes)
Location
Skiles 006
Speaker
Alexander DunnCaltech

Large sieve inequalities are a fundamental tool used to investigate prime numbers and exponential sums. I will explain my work that resolves a 1978 conjecture of S. Patterson (conditional on the Generalized Riemann hypothesis) concerning the bias of cubic Gauss sums. This explains a well-known numerical bias first observed by Kummer in 1846. One important byproduct of my work is a proof that

Heath-Brown's famous cubic large sieve is sharp, contrary to popular belief.  This sheds light on some of the mysteries surrounding large sieve inequalities for certain families of arithmetic harmonics and gives strong clues on where to look next for further progress. This is based on joint work with Maksym Radziwill. 

Randomness in Ramsey theory and coding theory

Series
Job Candidate Talk
Time
Tuesday, January 17, 2023 - 11:00 for 1 hour (actually 50 minutes)
Location
Skiles 006
Speaker
Xiaoyu HePrinceton University

Two of the most influential theorems in discrete mathematics state, respectively, that diagonal Ramsey numbers grow exponentially and that error-correcting codes for noisy channels exist up to the information limit. The former, proved by Erdős in 1947 using random graphs, led to the development of the probabilistic method in combinatorics. The latter, proved by Shannon in 1948 using random codes, is one of the founding results of coding theory. Since then, the probabilistic method has been a cornerstone in the development of both Ramsey theory and coding theory. In this talk, we highlight a few important applications of the probabilistic method in these two parallel but interconnected worlds. We then present new results on Ramsey numbers of graphs and hypergraphs and codes correcting deletion errors, all based on probabilistic ideas.

Stochastic partial differential equations in supercritical, subcritical, and critical dimensions

Series
Job Candidate Talk
Time
Friday, January 13, 2023 - 11:05 for 1 hour (actually 50 minutes)
Location
Skiles 006
Speaker
Alexander DunlapCourant Institute, NYU

A pervading question in the study of stochastic PDE is how small-scale random forcing in an equation combines to create nontrivial statistical behavior on large spatial and temporal scales. I will discuss recent progress on this topic for several related stochastic PDEs - stochastic heat, KPZ, and Burgers equations - and some of their generalizations. These equations are (conjecturally) universal models of physical processes such as a polymer in a random environment, the growth of a random interface, branching Brownian motion, and the voter model. The large-scale behavior of solutions on large scales is complex, and in particular depends qualitatively on the dimension of the space. I will describe the phenomenology, and then describe several results and challenging problems on invariant measures, growth exponents, and limiting distributions.

Symmetric group representations and break divisors on graphs

Series
Job Candidate Talk
Time
Tuesday, January 10, 2023 - 11:00 for 1 hour (actually 50 minutes)
Location
Skiles 006
Speaker
Vasu TewariUniversity of Hawaii

Please Note: Live streamed but not recorded: https://gatech.zoom.us/j/93724280805

The last decade has witnessed great interest in the study of divisors of graphs and a fascinating combinatorially-rich picture has emerged. The class of break divisors has attracted particular attention, for reasons both geometric and combinatorial. I will present several representation-theoretic results in this context.

I will demonstrate how certain quotients of polynomial rings by power ideals, already studied by Ardila-Postnikov, Sturmfels-Xu, Postnikov-Shapiro amongst others, arise by applying the method of orbit harmonics to break divisors. These quotients then naturally afford symmetric group representations which are not entirely understood yet. By describing the invariant spaces of these representations in terms of break divisors, I will answer a combinatorial question from the setting of cohomological Hall algebras.

Dynamics, number theory, and unlikely intersections

Series
Job Candidate Talk
Time
Monday, January 9, 2023 - 11:00 for 1 hour (actually 50 minutes)
Location
Skiles 006 and https://gatech.zoom.us/j/99998037632?pwd=Q2VNMVRCQUdUeWVpUW8xRzVIanBwQT09
Speaker
Myrto MavrakiHarvard

Fruitful interactions between arithmetic geometry and dynamical systems have emerged in recent years. In this talk I will illustrate how insights from complex dynamics can be employed to study problems from arithmetic geometry. And conversely how arithmetic geometry can be used in the study of dynamical systems. The motivating questions are inspired by a recurring phenomenon in arithmetic geometry known as `unlikely intersections' and conjectures of Pink and Zilber therein. More specifically, I will discuss work toward understanding the distribution of preperiodic points in subvarieties of families of rational maps.

Prediction problems and second order equations

Series
Job Candidate Talk
Time
Thursday, December 15, 2022 - 11:00 for 1 hour (actually 50 minutes)
Location
Skiles 006 or https://gatech.zoom.us/j/98373229920
Speaker
Ibrahim EkrenFlorida State University

We study the long-time regime of the prediction with expert advice problem in both full information and adversarial bandit feedback setting. We show that with full information, the problem leads to second order parabolic partial differential equations in the Euclidean space. We exhibit solvable cases for this equation and discuss the optimal behavior of both agents. In the adversarial bandit feedback setting, we show that the problem leads to second order parabolic equations in the Wasserstein space which allows us to obtain novel regret bounds. Based on joint works with Erhan Bayraktar and Xin Zhang.

Classical Developments of Compressible Fluid Flow

Series
Job Candidate Talk
Time
Tuesday, December 13, 2022 - 11:00 for 1 hour (actually 50 minutes)
Location
Skiles 006
Speaker
Leonardo AbbresciaVanderbilt University

The flow of compressible fluids is governed by the Euler equations, and understanding the dynamics for large times is an outstanding open problem whose full resolution is unlikely to happen in our lifetimes. The main source of difficulty is that any global-in-time theory must incorporate singularities in the PDEs, a fact we have known even in one spatial dimension since Riemann’s 1860 work. In this 1D setting, mathematicians have successfully spent the past 160 years painting a nearly-full picture of fluid dynamics that incorporates singularities.

 

There is a monumental gap in our understanding of compressible fluids in the physical 3D setting compared to the 1D case. This is due in large to the (provable) inaccessibility of the technical PDE tools used in 1D when quantifying the dynamics in 3D. Nevertheless, Christodoulou’s 2007 celebrated breakthrough on shock singularities for the Euler equation has sparked a dramatic wave of results and ideas in multiple space dimensions that have the potential to make the first meaningful dent in the global-in-time theory of compressible fluids. Roughly, shocks are a form of singularity where the fluid solution remains regular but certain first derivatives blow up.

 

In this talk I will discuss the recent culmination of the wave of results initiated by Christodoulou: my work on the maximal classical development (MCD) for compressible fluids, joint with J. Speck. Roughly speaking, the MCD describes the largest region of spacetime where the Euler equations admit a classical solution. For an open set of smooth data, my work reveals the intimate relationship between shock singularity formation and the full structure of the MCD. This fully solves the 162 year old open problem of extending Riemann’s historic 1D result to 3D without symmetry assumptions. In addition to the mathematical contribution, the geo-analytic information of the MCD is precisely the correct “initial data” needed to physically describe the fluid “past” the initial shock singularity in a weak sense. I will also briefly discuss the countless open problems in the field, all of which can be viewed as “building blocks” which will shine the first lights onto the outstanding global-in-time open problem of fluids.

Quantum algorithms for Hamiltonian simulation with unbounded operators

Series
Job Candidate Talk
Time
Thursday, December 8, 2022 - 11:00 for 1 hour (actually 50 minutes)
Location
Skiles 006 or https://gatech.zoom.us/j/98355006347
Speaker
Di FangUC Berkeley

Recent years have witnessed tremendous progress in developing and analyzing quantum computing algorithms for quantum dynamics simulation of bounded operators (Hamiltonian simulation). However, many scientific and engineering problems require the efficient treatment of unbounded operators, which frequently arise due to the discretization of differential operators. Such applications include molecular dynamics, electronic structure theory, quantum control and quantum machine learning. We will introduce some recent advances in quantum algorithms for efficient unbounded Hamiltonian simulation, including Trotter type splitting and the quantum highly oscillatory protocol (qHOP) in the interaction picture. The latter yields a surprising superconvergence result for regular potentials. In the end, I will discuss briefly how Hamiltonian simulation techniques can be applied to a quantum learning task achieving optimal scaling. (The talk does not assume a priori knowledge on quantum computing.)

Structure for dense graphs: forbidding a vertex-minor

Series
Job Candidate Talk
Time
Tuesday, December 6, 2022 - 11:00 for 1 hour (actually 50 minutes)
Location
Skiles 006 / hybrid
Speaker
Rose McCartyPrinceton University

Structural graph theory has traditionally focused on graph classes that are closed under both vertex- and edge-deletion (such as, for each surface Σ, the class of all graphs which embed in Σ). A more recent trend, however, is to require only closure under vertex-deletion. This is typically the right approach for graphs with geometric, rather than topological, representations. More generally, it is usually the right approach for graphs that are dense, rather than sparse. I will discuss this paradigm, taking a closer look at classes with a forbidden vertex-minor.

Mathematical and Statistical Challenges on Large Discrete Structures

Series
Job Candidate Talk
Time
Wednesday, March 16, 2022 - 11:00 for 1 hour (actually 50 minutes)
Location
https://bluejeans.com/348214744/2450
Speaker
Miklos RaczPrinceton University

From networks to genomics, large amounts of data are abundant and play critical roles in helping us understand complex systems. In many such settings, these data take the form of large discrete structures with important combinatorial properties. The interplay between structure and randomness in these systems presents unique mathematical and statistical challenges. In this talk I will highlight these through two vignettes: (1) inference problems on networks, and (2) DNA data storage.

First, I will discuss statistical inference problems on edge-correlated stochastic block models. We determine the information-theoretic threshold for exact recovery of the latent vertex correspondence between two correlated block models, a task known as graph matching. As an application, we show how one can exactly recover the latent communities using multiple correlated graphs in parameter regimes where it is information-theoretically impossible to do so using just a single graph. Furthermore, we obtain the precise threshold for exact community recovery using multiple correlated graphs, which captures the interplay between the community recovery and graph matching tasks. 

Next, I will give an overview of DNA data storage. Storing data in synthetic DNA is an exciting emerging technology which has the potential to revolutionize data storage. Realizing this goal requires innovation across a multidisciplinary pipeline. I will explain this pipeline, focusing on our work on statistical error correction algorithms and optimizing DNA synthesis, highlighting the intimate interplay between statistical foundations and practice.

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