# Colloquia

Our department is proud to host weekly colloquium talks featuring research by leading mathematicians from around the world. Most colloquia are held on Fridays at 4pm in ACLC, Room 010 (unless otherwise advertised) with refreshments preceding at 3:15pm in Parker Hall, Room 244.

**Upcoming Colloquia**

**Recent Colloquia**

**DMS Colloquium: Chris Kapulkin**

**Aug 30, 2024 04:00 PM**

*Friday*.

**Chris Kapulkin**(University of Western Ontario).

Abstract: For many years, mathematicians have used computers to perform computations that inform their research and lead to new conjectures. More recently, computers have been used to formally verify correctness of proofs via software known as proof assistants. This talk will be an introduction to proof assistants - what they are, why they are used, and what the mathematical community has accomplished with them.

A short introduction to the most commonly used proof assistant, Lean, will also be given. Together, we will use Lean to formally verify that every natural number is either even or odd, a fact that shouldn't surprise many but is a good illustration of what working with a proof assistant is like. Those interested in following the proof on their own computers are encouraged to install Lean ahead of the talk by going to:

**DMS Colloquium: Jordan Awan**

**Apr 26, 2024 04:00 PM**

(please note special location--228 Parker Hall)

Speaker: **Jordan Awan** (Purdue)

Abstract: Privacy protection methods, such as differentially private mechanisms, introduce noise into resulting statistics which often produces complex and intractable sampling distributions. In this paper, we propose a simulation-based "repro sample" approach to produce statistically valid confidence intervals and hypothesis tests, which builds on the work of Xie and Wang, 2022. We show that this methodology is applicable to a wide variety of private inference problems, appropriately accounts for biases introduced by privacy mechanisms (such as by clamping), and improves over other state-of-the-art inference methods such as the parametric bootstrap in terms of the coverage and type I error of the private inference. We also develop significant improvements and extensions for the repro sample methodology for general models (not necessarily related to privacy), including (1) modifying the procedure to ensure guaranteed coverage and type I errors, even accounting for Monte Carlo error, and (2) proposing efficient numerical algorithms to implement the confidence intervals and \(p\)-values.

**DMS Colloquium: Haomin Zhou**

**Apr 19, 2024 04:00 PM**

*Please note special location for colloquium--Parker 228 *

Speaker: **Haomin Zhou** (Georgia Tech)

Title: Analysis and Computation of Parameterized Wasserstein Geometric Flow

Abstract: We introduce a new parameterization strategy that can be used to design algorithms simulating geometric flows on Wasserstein manifold, the probability density space equipped with optimal transport metric. The framework leverages the theory of optimal transport and the techniques like the push-forward operators and neural networks, leading to a system of ODEs for the parameters of neural networks. The resulting methods are mesh-less, basis-less, sample-based schemes that scale well to higher dimensional problems. The strategy works for Wasserstein gradient flows such as Fokker-Planck equation, and Wasserstein Hamiltonian flow like Schrodinger equation. Theoretical error bounds measured in Wasserstein metric is established.

This presentation is based on joint work with Yijie Jin (Math, GT), Shu Liu (UCLA), Has Wu (Wells Fargo), Xiaojing Ye (Georgia State), and Hongyuan Zha (CUHK-SZ).

**DMS Colloquium: Dr. Grady Wright**

**Apr 12, 2024 04:00 PM**

**Dr. Grady Wright**(Boise State University)

**DMS Colloquium: Dr. Lateefah Id-Deen**

**Apr 05, 2024 04:00 PM**

**Dr. Lateefah Id-Deen**(Kennesaw State University, Georgia)

**Bio:**

**DMS Colloquium: Dr. Melinda Lanius**

**Mar 29, 2024 04:00 PM**

**Dr. Melinda Lanius**(Auburn)

Title: Playing the villain: Approaches to validity in undergraduate mathematics education research

Abstract: In the setting of education research, a measure assigns a numerical value to represent the degree to which a target attribute appears; For example, a psychometric scale for math anxiety assigns a numerical value to indicate if an undergraduate student has high, medium, or low/no math anxiety. How do we know if a measure means what we think it means? We can collect evidence of validity! In this highly interactive talk, I will share my journey in understanding validity and the types of evidence we can use to assess the soundness of our measures. This reflective work has required me to look at my study designs unenthusiastically and critically, "playing the villain" in my own research program. Please come join me for an afternoon of fun, where we put on our critic hats and explore validity in undergraduate mathematics education research!