Tags: Colloquium Series

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


Departmental Colloquium - Dr. Xinran Li

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Dr. Emma Zhang


Departmental Colloquium - Dr. Vasileios Pavlopoulos

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Departmental Colloquium - Dr. Dennis Lin

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Departmental Colloquium - Dr. João M. Pereira

Method of Moments: From Sample Complexity to Efficient Implicit Computations Abstract: The focus of this talk is the multivariate method of moments for parameter estimation. First from a theoretical standpoint, we show that in problems where the noise is high, the sample complexity, that is, the number of observations necessary to estimate parameters, is dictated by the moments of the distribution. This follows from a Taylor expansion of the KL…


The Bradley Lecture - Lars Peter Hansen

Agenda: 3:30 - 3:50pm - Arrival and reception, Brooks Hall 327 3:50 - 4:00pm - Group photo in front of Brooks Hall 4:00 - 4:15pm - Opening Remarks by UGA Vice Provost Elizabeth Weeks, Dean Anna Stenport, and Dean Margaret Amstutz, Brooks Hall 145 4:15 - 5:05pm - Lecture, Dr. Lars Hansen, University of Chicago, Brooks Hall 145  5:05 - 5:30pm - Break, and proceed to the Delta Innovations Hub 5:30 - 7:00pm - Dinner, Delta Innovations Hub 7:00…


Susmita Datta

Unraveling Disease Mysteries: Statistical Models Reveal Cellular Conversations using Spatial Transcriptomics data. Abstract: Understanding cell microenvironments from spatially resolved transcriptomics data is a cutting-edge approach in biomedical research. This innovative method enables scientists to investigate the spatial organization of cells near diseased tissues and identify their inter - and intracellular communications through…


Ying Guo

A Regularized Blind Source Separation Method for Disentangling Dynamic Functional Connectome Abstract: Brain connectomics has become increasingly popular in neuroimaging studies to advance understanding of neural circuits and their association with neurodevelopment, mental illnesses, and aging. These analyses often face major challenges, including the high dimensionality of brain networks, unknown latent sources underlying the observed…


Zhen Xiang

Toward Trustworthy Machine Learning Under Training-Time Adversaries Abstract: Machine learning has demonstrated remarkable performance across various applications. However, significant concerns have arisen about its trustworthiness, such as risks posed by training-time adversaries. Facilitated by the need for 'big data', whether publicly available or locally accessible, training-time adversaries can easily manipulate a machine learning model by…


Xiuwei Zhang

Studying single cells through multi-condition and spatial context Abstract: With the advances in single cell technologies, cells are profiled through multiple modalities, and data on samples from an increasing number of individuals are obtained. I will present our method, scDisInFact, that disentangles variation in multi-batch multi-condition scRNA-seq datasets, and predicts data under unseen conditions. I will also present a few methods…
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