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Georgia Statistics Day 2023

Event Flyer (1.71 MB)

The Georgia Statistics Day Conference Series

Gathering Minds Across Georgia: Promoting Interdisciplinary Statistics Research

You are cordially invited to the 2023 Georgia Statistics Day, a one-day workshop that brings together top researchers across the state to foster collaboration and innovation in statistics, data science, and related disciplines.  

Our workshop provides a unique opportunity for faculty and graduate students from Georgia’s leading institutions to present their latest work, connect with peers, and gain exposure to cutting-edge developments shaping their fields.

With a focus on mentorship and networking, Georgia Statistics Day facilitates idea exchange and partnership building among statisticians, data scientists, and interconnected domain experts in Georgia and the Southeast. Attendees will experience invited talks, panel discussions, and poster sessions that spark new interdisciplinary perspectives and opportunities for growth.

Don’t miss this chance to contribute to and be inspired by the thriving statistics and data science community thriving in the Peach State! We look forward to welcoming you at the 2023 Georgia Statistics Day on Oct.9 in the Exhibition Hall of Georgia Tech.

The purpose of this event is to promote interdisciplinary research within the flagship institutions of the state of Georgia. Our conference will enable junior researchers in the Southeast region of the United States, including graduate students, to present their work, to see state of the art developments in research on statistics and related scientific areas, and to interact with some of the key players in the area. Georgia Statistics Day puts emphasis on mentoring of junior researchers and on interaction between senior and junior researchers.

The H. Milton Stewart School of Industrial and Systems Engineering welcomes you to Georgia Tech for Georgia Statistics Day 2023.

We appreciate your attendance and look forward to a successful and productive conference!

Organizing Committee

Workflow Committee

University of Georgia Coordinator

Emory Coordinator

Keynote Speaker

S.C. Samuel Kou

Chair of Department of Statistics, Harvard University
Professor of Biostatistics, Harvard T.H. Chan School of Public Health

Samuel Kou is Professor of Statistics at Harvard University. He received a bachelor’s degree in computational mathematics from Peking University in 1997, followed by a Ph.D. in statistics from Stanford University in 2001. After completing his Ph.D., he joined Harvard University as an Assistant Professor of Statistics and was promoted to a full professor in 2008.
His research interests include big data analytics; digital disease tracking; stochastic inference in biophysics, chemistry and biology; protein folding; Bayesian inference for stochastic models; nonparametric statistical methods; model selection and empirical Bayes methods; and Monte Carlo methods.
He is the recipient of the COPSS (Committee of Presidents of Statistical Societies) Presidents’ Award, the highest honor for a statistician under the age of 40; the Guggenheim Fellowship; a US National Science Foundation CAREER Award; the Institute of Mathematical Statistics Richard Tweedie Award; the Raymond J. Carroll Young Investigator Award; and the American Statistical Association Outstanding Statistical Application Award. He is an elected Fellow of the American Statistical Association, an elected member of the International Statistical Institute, and an elected Fellow and a Medallion Lecturer of the Institute of Mathematical Statistics.

Catalytic Prior Distributions for Bayesian Inference

The prior distribution is an essential part of Bayesian statistics, and yet in practice, it is often challenging to quantify existing knowledge into pragmatic prior distributions. In this talk we will discuss a general method for constructing prior distributions that stabilize the estimation of complex target models, especially when the sample sizes are too small for standard statistical analysis, which is a common situation encountered by practitioners with real data. The key idea of our method is to supplement the observed data with a relatively small amount of “synthetic” data generated, for example, from the predictive distribution of a simpler, stably estimated model. This general class of prior distributions, called “catalytic prior distributions” is easy to use and allows direct statistical interpretation. In the numerical evaluations, the resulting posterior estimation using catalytic prior distribution outperforms the maximum likelihood estimate from the target model and is generally superior to or comparable in performance to competitive existing methods. We will illustrate the usefulness of the catalytic prior approach through real examples and explore the connection between the catalytic prior approach and a few popular regularization methods.


Abhyuday Mandal (University of Georgia)

Modeling and Active Learning for Experiments with Quantitative-Sequence Factors

Aditya Mishra (University of Georgia)

Tree Aggregated Factor Regression Model with application to microbiome data analysis

Vidya Muthukumar (Georgia Institute of Technology)

Classification versus regression in overparameterized regimes: Does the loss function matter?

Ashwin Pananjady (Georgia Institute of Technology)

Statistics meets optimization: Sharp convergence predictions for iterative algorithms with random data

Limin Peng (Emory)

Partial Quantile Tensor Regression with Applications to Neuroimaging Data

Molei Tao (Georgia Institute of Technology)

Constrained Sampling and Constrained Diffusion Generative Modeling via Mirror Map

Ke Wang (Wells Fargo)

An empirical study on imbalanced data impact and treatment

Julia Wrobel (Emory)

Analysis of wearable device data using functional data models

Yanbo Xu (Microsoft)

Large-Scale Domain-Specific Pretraining for Biomedical Vision-Language Processing

Chao Zhang (Georgia Institute of Technology)

LLMs as Autonomous Agents: Decision-Making through Adaptive Closed-Loop Planning

Yichuan Zhao (Georgia State University)

Novel Empirical Likelihood Inference for the Mean Difference with Right-Censored Data

Tuo Zhao (Georgia Institute of Technology)

Steering the Attention of Large Language Models

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