Tags: Colloquium Series

The Statistics Department hosts weekly colloquia on a variety of statistcal subjects, bringing in speakers from around the world.


Jae-Kwang Kim


John Drake

This event is sponsored by UGA Statistics Club. Please join using this Zoom link.


Csaba Szepesvari

Professor & Canada CIFAR AI Chair, Amii 


Michael Hamada

Michael Hamada is an applied statistician who has worked at Los Alamos National Laboratory since 1998. He received his PhD in Statistics from the University of Wisconsin-Madison. Previously he worked at the US Department of Agriculture, the University of Waterloo, Bellcore, and the University of Michigan. He is a Fellow of the ASA, ASQ, and Los Alamos National Laboratory. He is also a co-author of the books Experiments: Planning, Analysis and…


Dr. Qiwei Yao

Autoregressive Networks We propose a first-order autoregressive model for dynamic network processes in which edges change over time while nodes remain unchanged. The model depicts the dynamic changes explicitly. It also facilitates simple and efficient statistical inference such as the maximum likelihood estimators which are proved to be (uniformly) consistent and asymptotically normal. The model diagnostic checking can be carried out easily…


Devdutt Yellurkar

Bringing Ideas to Fruition—An Investor's Perspective After a brief description of my long journey from a Statistics graduate to an entrepreneur to an investor, I will give examples of how our venture capitalist firm (CRV) helps bring some new ideas to fruition. I will also talk about how statisticians and data scientists can contribute to today's business environment.


Dr. Sheng Li

Knowledge-Guided Graph Representation Learning Graph-structured data are ubiquitous, which have been extensively used in many domains such as social networks and recommender systems. In this talk, I will present our recent work on graph representation learning with applications in multiple domains. First, we leverage the line graph theory and propose novel graph neural networks, which jointly learn embeddings for both nodes and edges. Second, we…


Dr. Wenguang Sun

Locally Adaptive Weighting and Screening Approach to Spatial Multiple Testing Exploiting spatial patterns promises to improve both power and interpretability of false discovery rate (FDR) analyses. This article develops a new class of locally–adaptive weighting and screening (LAWS) rules that directly incorporates useful local patterns into inference by constructing robust and structure-adaptive weights according to the estimated local sparsity…


Dr. Yuehua Cui

Kernel-based genome-wide gene-set association test With the radical breakthrough in biotechnology, high throughput genomic data are routinely generated. These data present unprecedented opportunities in disentangling the genetic secret of complex diseases, while also present daunting challenges in statistical modeling and inference. In the last few years, we have witnessed significant advancement in statistical methodology development for…


Dr. Ethan X. Fang

Statistical Modeling and Optimization for Optimal Adaptive Trial Design in Personalized Medicine We provide a new modeling framework and adopt modern optimization tools to attack an  important open problem in statistics. In particular, we consider the optimal adaptive trial design problem in personalized medicine. Adaptive enrichment designs involve preplanned rules for modifying enrollment criteria based on accruing data in a randomized…
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