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Series: CDSNS Colloquium

Some relevant Hamiltonian systems in Celestial Mechanics have first integrals in involution. A classic technique to study such systems, known as symplectic reduction, is based in reducing the number of degrees of freedom by using the first integrals. In this talk we present two a posteriori KAM theorems for Hamiltonian systems with first integrals in involution, including the isoenergetic case, without using symplectic reduction. The approach leads to efficient numerical methods and validating techniques.This is a joint work with Alejandro Luque.

Series: ACO Student Seminar

Studying random samples drawn from large, complex sets is one way to begin to learn about typical properties and behaviors. However, it is important that the samples examined are random enough: studying samples that are unexpectedly correlated or drawn from the wrong distribution can produce misleading conclusions. Sampling processes using Markov chains have been utilized in physics, chemistry, and computer science, among other fields, but they are often applied without careful analysis of their reliability. Making sure widely-used sampling processes produce reliably representative samples is a main focus of my research, and in this talk I'll touch on two specific applications from statistical physics and combinatorics.I'll also discuss work applying these same Markov chain processes used for sampling in a novel way to address research questions in programmable matter and swarm robotics, where a main goal is to understand how simple computational elements can accomplish complicated system-level goals. In a constrained setting, we've answered this question by showing that groups of abstract particles executing our simple processes (which are derived from Markov chains) can provably accomplish remarkable global objectives. In the long run, one goal is to understand the minimum computational abilities elements need in order to exhibit complex global behavior, with an eye towards developing systems where individual components are as simple as possible.This
talk includes joint work with Marta Andrés Arroyo, Joshua J. Daymude,
Daniel I. Goldman, David A. Levin, Shengkai Li, Dana Randall,
Andréa Richa, William Savoie, Alexandre Stauffer, and Ross Warkentin.

Series: Other Talks

This is a workshop designed to provide an introduction to the use of
modern tools from Dynamical Systems in the design of space exploration
missions. More details and a detailed schedule is found in http://people.math.gatech.edu/~rll6/JPL/jpl.html

Series: Other Talks

Series: Other Talks

Series: Job Candidate Talk

Semiparametric regressions enjoy the flexibility of nonparametric models as well as the in-terpretability of linear models. These advantages can be further leveraged with recent ad-vance in high dimensional statistics. This talk begins with a simple partially linear model,Yi = Xi β ∗ + g ∗ (Zi ) + εi , where the parameter vector of interest, β ∗ , is high dimensional butsufficiently sparse, and g ∗ is an unknown nuisance function. In spite of its simple form, this highdimensional partially linear model plays a crucial role in counterfactual studies of heterogeneoustreatment effects. In the first half of this talk, I present an inference procedure for any sub-vector (regardless of its dimension) of the high dimensional β ∗ . This method does not requirethe “beta-min” condition and also works when the vector of covariates, Zi , is high dimensional,provided that the function classes E(Xij |Zi )s and E(Yi |Zi ) belong to exhibit certain sparsityfeatures, e.g., a sparse additive decomposition structure. In the second half of this talk, I discussthe connections between semiparametric modeling and Rubin’s Causal Framework, as well asthe applications of various methods (including the one from the first half of this talk and thosefrom my other papers) in counterfactual studies that are enriched by “big data”.Abstract as a .pdf

Series: School of Mathematics Colloquium

The probability of outcomes of repeated
fair coin tosses can be computed exactly using binomial coefficients.
Performing asymptotics on these formulas uncovers the Gaussian
distribution and the first instance of the central limit theorem. This
talk will focus on higher version of this story. We will consider random
motion subject to random forcing. By leveraging structures from representation theory and quantum integrable systems
we can compute the analogs of binomial coefficients and extract new and
different asymptotic behaviors than those of the Gaussian. This model
and its analysis fall into the general theory of "integrable
probability".

Series: Other Talks

Series: Geometry Topology Seminar

Series: Other Talks

This is a preliminary talk for the Workshop "Introduction to Dynamical Systems Methods for Mission Design" that will take place Jan 16-19 in the school of Mathematics. In this talk, we will present the basics of Hamiltonian dynamics and why it is useful. It ishoped that it will be accesible for people with background in undergraduate differential equations who want to participate in the workshop.