Georgia Institute of Technology College of Sciences

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

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".

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

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

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

TBA by Cheng Mao

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

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.