Tags: Colloquium Series

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


Yuanzhe Xi

Accelerating Gaussian process regressions with preconditioning Kernel matrices can help handle nonlinearities in the data in many machine learning applications. The entries of the kernel matrix are defined as the values of a kernel function at all pairs of points from a given dataset. Since the spectrum of the kernel matrix associated with the same dataset can vary dramatically as the parameters of the kernel function change, developing robust…


Ghost Data

Biography Dr. Dennis K. J. Lin is a University Distinguished Professor and Head of the Statistics Department at Purdue University. His research interests are quality assurance, industrial statistics, data mining, and data science. He has published near 250 SCI/SSCI papers in a wide variety of journals. He currently serves or has served as associate editor for more than 10 professional journals and was co-editor for Applied Stochastic Models for…


2023 Bradley Lecture

Agenda 3:30 - 4:00pm - Arrival4:00 - 4:05pm - Opening remarks by Associate Dean Thomas Mote, University of Georgia4:05 - 5:00pm - Lecture, Dr. Dylan Small, University of Pennsylvania5:00 - 5:30pm - Break5:30 - 7:00pm - Dinner7:00 - 7:05pm - Remarks by Dean Alan Dorsey, University of Georgia7:05 - 7:30pm - After-Dinner Talk, Dr. Dylan Small, University of Pennsylvania Biography: Dr. Dylan Small is the Universal Furniture Professor of Statistics…


Jared Christensen, Vice President

Decision Making in Clinical Development: How Statisticians are Impacting the Pharmaceutical Business Abstract: One aspect of the pharmaceutical industry thatstatisticians can continue to refine and influence is how data-drivendecisions are made. These decisions can be made from the earliestpoints in preclinical development through full commercializationof drugs. While data-driven decisions are common for a singlehypothesis, there are cases…


Weihua An

Causal Inference with Networked Treatment Diffusion Causal inference under treatment interference (i.e., one unit’s potential outcomes depend on other units’ treatment) is a challenging but important problem. Past studies usually make strong assumptions on the structure of treatment interference. In this study, I highlight the importance of collecting data on actual treatment interference in order to make more accurate causal inference. I show…


Tirthankar Dasgupta

Randomization tests and their relevance in the age of data science Randomized experiment is a quintessential methodology in science, engineering, business and industry for assessing causal effects of interventions on outcomes. Randomization tests, conceived by Fisher, are useful tools to analyze data obtained from such experiments because they assess the statistical significance of estimated treatment effects without making any assumptions…


Jiping Wang

Ribosome footprint Differentiation and DNA Cyclizability Prediction High throughput sequencing has become a standard technology in many assays in biomedical research. In this talk I present recent work on two problems namely, ribosome footprint profiling and DNA cyclizability prediction. Ribosome is a protein that binds along the transcript to facilitate translation. Knowing its footprint and abundance provides a measurement of translation…


Ziwei Zhu

Recent Development of Rank-Constrained and Distributed Statistical Learning In this talk, I will present two recent works on rank-constrained least squares and distributed statistical learning respectively. The first part of the talk highlights a near optimal in-sample prediction error bound for the rank-constrained least squares estimator with no assumption on the design matrix. Lying at the heart of the proof is a covering number bound for the…


Ji Zhu

Fast Network Community Detection with Profile-Pseudo Likelihood Methods The stochastic block model is one of the most studied network models for community detection. It is known that most algorithms proposed for fitting the stochastic block model likelihood function cannot scale to large-scale networks. One prominent work that overcomes this computational challenge is Amini et al. (2013), which proposed a fast pseudo-likelihood approach for…


Alexander Aue

Random matrix theory aids statistical inference in high dimensions This talk is on bootstrapping spectral statistics in high dimensions. Spectral statistics play a central role in many multivariate testing problems. It is therefore of interest to approximate the distribution of functions of the eigenvalues of sample covariance matrices. Although bootstrap methods are an established approach to approximating the laws of spectral statistics in low…
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