Speakers

Dr. Abhyuday Mandal is a Professor of Statistics at the University of Georgia. He earned his undergraduate and master’s degrees from the Indian Statistical Institute in Kolkata, obtained another master’s degree from the University of Michigan, and completed his Ph.D. in 2005 at the Georgia Institute of Technology. His research interests include design of experiments, computer experiments and optimization techniques, with applications in drug discovery, small area estimation, agriculture and engineering. Currently he serves as an Associate Editor of Statistics and Probability Letters, Sankhya – Series B, Journal of Statistical Theory and Practice and Journal of Applied Statistics. He has received the Sandy Beaver Excellence in Teaching Award from the Franklin College of UGA. His research has been funded by the National Science Foundation and the National Security Agency.

Talk: Modeling and Active Learning for Experiments with Quantitative-Sequence Factors

Dr. Aditya Mishra is an environmental AI cluster hire faculty in the Department of Statistics. Prior to joining the department, Dr. Mishra was a computational/data scientist at the Platform for Innovative Microbiome and Translational Research at the University of Texas MD Anderson Cancer Center. At MD Anderson, Aditya was involved in developing and applying statistical methods to understand the role of the human microbiome in cancer onset, progression, and response to therapy. Aditya’s research mainly focuses on developing statistical methods and computational tools using the framework of High-dimensional statistics, Multivariate analysis, Reduced-rank regression, Regularization, Robust statistics, Multi-omics, Computational biology, Causal inference and Variational inference. He has extensive interdisciplinary research experience in a variety of fields, including microbiome data analysis, genomics, cancer genomics, public health, ocean microbiology, and dietary intervention study. As a part of environmental, Aditya will continue to understand and investigate the role of microbiome various context including cancer genomics, marine science, soil science and agriculture science.

Talk: Tree Aggregated Factor Regression Model with application to microbiome data analysis

Vidya Muthukumar is an Assistant Professor in the School of Electrical and Computer Engineering and H. Milton Stewart School of Industrial and Systems Engineering at Georgia Institute of Technology. Dr. Muthukumar’s broad interests are in game theory, online and statistical learning. She is particularly interested in designing learning algorithms that provably adapt in strategic environments, fundamental properties of overparameterized models, and the foundations of multi-agent decision-making.

Dr. Muthukumar received the B.Tech (with honors) degree from the Indian Institute of Technology, Madras and the Ph.D. degree in Electrical Engineering from University of California, Berkeley. She interned at IBM Research in the summer of 2018 as a Science for Social Good fellow. Before joining Georgia Tech, she spent a semester at the Simons Institute for the Theory of Computing as a research fellow for the program “Theory of Reinforcement Learning.” She is the recipient of a NSF CAREER Award, Amazon Research Award, Adobe Data Science Research Award, and Simons-Berkeley Google Research Fellowship.

Dr. Muthukumar has served/is serving as an Area Chair for COLT (2021-2024), NeurIPS 2023 and ALT 2024. She also co-organizes several mentorship workshops as part of the Learning Theory Alliance team.

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

Ashwin Pananjady is an assistant professor at Georgia Tech, with a joint appointment between the H. Milton Stewart School of Industrial and Systems Engineering and the School of Electrical and Computer Engineering. He received the Ph.D. degree in Electrical Engineering and Computer Science from UC Berkeley, for which he was awarded the David J. Sakrison Prize. His research interests include high-dimensional statistics, statistical machine learning and reinforcement learning, mathematical optimization, and information theory. He has received an Amazon Research Award, an Adobe Data Science Faculty Award, a Best Paper Prize Prize (runner-up) for Young Researchers in Continuous Optimization from the Mathematical Optimization Society, the inaugural Lawrence D. Brown Award from the Institute of Mathematical Statistics, a Simons-Berkeley Research Fellowship in Probability, Geometry and Computation in High Dimensions, and has been recognized for his teaching at both Berkeley and Georgia Tech.

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

Dr. Limin Peng is Professor in the Department of Biostatistics and Bioinformatics at the Rollins School of Public Health, Emory University. Dr. Peng joined Emory Biostatistics faculty in 2005 after receiving her PhD in Statistics from the University of Wisconsin-Madison. Dr. Peng’s research expertise spams across statistical method developments in the areas of survival analysis, quantile regression, high-dimensional inference, and nonparametric and semiparametric statistics, and their applications to public health and biomedical research. Dr. Peng was named American Statistical Association Fellow in 2016 and Institute of Mathematical Statistics Fellow in 2022, and was the recipient of 2017 Mortimer Spiegelman Award of American Public Health Association.

Talk: Partial Quantile Tensor Regression with Applications to Neuroimaging Data

Molei Tao received B.S. in Math and Physics (Academic Talent Program) in 2006 from Tsinghua University, China, and Ph.D. in Control & Dynamical Systems with a minor in Physics in 2011 from California Institute of Technology. Afterwards, he worked as a postdoc in Computing & Mathematical Sciences at Caltech from 2011 to 2012, and then as a Courant Instructor/Assistant Professor at New York University. From 2014 on, he has been working as an assistant, and then associate professor in School of Math at Georgia Institute of Technology, also affiliated with {GT Machine Learning Center}, {Algorithms & Randomness Center (ARC)}, {Algorithms, Combinatorics & Optimization (ACO) Program}, {GT Decision & Control Lab} . He is a recipient of W.P. Carey Ph.D. Prize in Applied Mathematics (2011), American Control Conference Best Student Paper Finalist (2013), the NSF CAREER Award (2019), AISTATS best paper award (2020), IEEE EFTF-IFCS Best Student Paper Finalist (2021), Cullen-Peck Scholar Award (2022), and GT-Emory AI.Humanity Award (2023).

Talk: Constrained Sampling and Constrained Diffusion Generative Modeling via Mirror Map

Ke Wang is a Quantitative Analytics Specialist at Wells Fargo. He received a PhD degree in Statistics and Applied Probability from University of California, Santa Barbara. His research interests include machine learning, high-dimensional statistics and optimization.

Talk: An empirical study on imbalanced data impact and treatment

Julia Wrobel is an Assistant Professor in the Department of Biostatistics and Bioinformatics at Emory University. She recently arrived at Emory from the Colorado School of Health, where she was an Assistant Professor in the Department of Biostatistics since 2019. She obtained her PhD in Biostatistics from the Columbia University Mailman School of Public Health. Dr. Wrobel’s two main methodological research areas are in functional data analysis and imaging statistics. Her application areas include wearable devices, neurophysiology, single cell spatial technologies, and impacts of recreational marijuana use.

Talk: Analysis of wearable device data using functional data models

Yanbo Xu is a Senior Applied Scientist at Microsoft Health AI. She received a PhD degree in machine learning from Georgia Institute of Technology in 2023. Her PhD research include developing cutting-edge ML/DL algorithms learning from large scale multi-modal time series data and making individualized predictions and recommendations for decision making. After joining Microsoft, she dedicates her career to developing AI for health. Her recent work focuses on the development of multimodal foundation models that integrate medical images with clinical text reports.

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

Chao Zhang is an Assistant Professor at the Georgia Institute of Technology’s School of Computational Science and Engineering. He is also a Visiting Academics at Amazon. Zhang received his Ph.D. in Computer Science from the University of Illinois at Urbana-Champaign in 2018. His research focuses on data-centric and trustworthy AI, specifically in the topics of large language models, learning from noisy data, uncertainty quantification, and spatiotemporal dynamics. Zhang’s work has been recognized with many awards, including the ACM SIGKDD Dissertation Runner-up Award, UbiComp Distinguished Paper Award, ECML/PKDD Best Student Paper Runner-up Award, and ML4H Outstanding Paper Award. He is also a recipient of the NSF Career Award and has received faculty awards from top companies such as Google, Facebook, and Amazon.

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

Dr. Yichuan Zhao is a Full Professor of Statistics, Georgia State University, Atlanta. He has a joint appointment as Associate Member of the Neuroscience Institute and he is also an affiliated faculty member of School of Public Health at Georgia State University. Dr. Zhao has a B.S. and an M.S. in Mathematics from Peking University, and an M.S. in Stochastics and Operations Research from Utrecht University. He received his Ph.D. in Statistics from the Department of Statistics at Florida State University. His current research interest focuses on survival analysis, empirical likelihood method, nonparametric statistics, statistical analysis of ROC curves, high-dimensional data analysis, bioinformatics, Monte Carlo methods, and statistical modeling of fuzzy systems. He has published 100 research articles in Statistics and Biostatistics research fields. Dr. Zhao has organized the Workshop Series on Biostatistics and Bioinformatics since its initiation in 2012. The ICSA Springer Book from the workshop can be found through New Frontiers of Biostatistics and Bioinformatics. He also organized the 25th ICSA Applied Statistics Symposium in Atlanta as the chair of both the organizing committee and program committee to great success. In addition, the 6th ICSA China Conference that he organized as the chair of both the organizing committee and program committee was a huge success. The ICSA Springer Book from the Symposium reflects new challenges in the contemporary data era, see the book: New Advances in Statistics and Data Science for details. A Springer Book reflects statistical modeling in biomedical research, see the book: Statistical Modeling in Biomedical Research. A Springer Book reflects advanced statistical methods for health research, see the book: Modern Statistical Methods for Health Research. A Springer Book reflects data analytics methods for management, banking and finance with novel applications to real-world problems, see the book: Data Analytics for Management, Banking and Finance. He served on program committee of numerous statistical conferences, and is currently serving on the editorial board, for several statistical journals including Electronic Journal of Statistics, Journal of Nonparametric Statistics and Journal of Applied Statistics. Dr. Zhao is a Fellow of the American Statistical Association and an elected member of the International Statistical Institute.

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

Tuo Zhao is an assistant professor at Georgia Tech. He received his Ph.D. degree in Computer Science at Johns Hopkins University. His research mainly focuses on developing methodologies, algorithms and theories for machine learning, especially deep learning. He is also actively working on neural language models and open-source machine learning software for scientific data analysis. He received several awards, including the winner of 2011 INDI ADHD-200 global competition, 2016 ASA best paper award on statistical computing, 2016 INFORMS best paper award on data mining, 2020 Google faculty research award, and the finalist of best paper competition of International Microwave Symposium 2021.

Talk: Steering the Attention of Large Language Models