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Michael Baron is Professor and Department Chair at American University, where he arrived from the University of Texas at Dallas. He conducts research in sequential analysis, change-point problems, and Bayesian inference, with occasional applications in epidemiology, clinical trials, insurance, energy finance, and semiconductor manufacturing. This last application brought him to IBM T. J. Watson Research Center, where he was a one-year Academic Visitor. Baron is credited for extending stepwise multiple hypothesis testing methods to sequential analysis and introducing asymptotically pointwise optimal stopping rules in change-point detection. Also, he elaborated several classes of sequentially planned statistical procedures. These are flexible group sequential sampling schemes with dynamically determined group sizes that result in substantial time and cost saving, when the cost nonlinear. Group sequential clinical trials can be accelerated by this method, without sacrificing the probabilities of Type I and Type II errors. Baron participated in the design and analysis of several clinical trials. He authored a probability and statistics textbook for computer scientists and co-authored a series of books studying applications of statistics in sociology and marketing, classifying and exploring lifestyles and consumer behaviors. M. Baron is a Fellow of the American Statistical Association and a recipient of Abraham Wald award. |
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Professor Christopher Jennison pursues research into the design and analysis of clinical trials. He has worked on sequential methods, which are used to monitor trials and make decisions on when to stop a study. His book with Professor Bruce Turnbull, "Group Sequential Methods with Applications to Clinical Trials", is a standard text on this topic, widely used by practicing statisticians. More recently, he has worked on adaptive clinical trial designs which allow a broader range of interim decision making, such as treatment selection or re-defining the target population, during the course of a trial. This research is informed by experience of clinical trial analysis at the Dana Farber Cancer Institute, Boston, consultancy with medical research institutes and pharmaceutical companies, and participation in clinical trial data monitoring committees. He has been a member of the DIRECT consortium (funded by the EU Innovative Medicines Initiative) and the MASTERMIND project (funded by the MRC and ABPI), which explored personalised treatment strategies for patients with type 2 diabetes. Professor Jennison is also interested in statistical image analysis, spatial statistics and the computational methods used to fit complex models to large data sets, for which the applications range from medical and biological data to remote sensing and modelling the atmosphere. |
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Prof. Mei’s research focuses on statistics and machine learning, particularly, sequential analysis, change-point detection and streaming data analysis, and their applications to engineering, operation research, biomedical and health sciences. In his earlier career, Dr. Mei influenced the theoretical development on the foundation of sequential analysis and change-point detection, especially when the data could be dependent or not identically distributed. Later, motivated by sensor networks in engineering and biosurveillance, he has pioneered the development of updated theory of sequential analysis and change-point detection in the context of online monitoring high-dimensional data. He proposed the first family of scalable high-dimensional change-point detection schemes by raising a global alarm based on the sum of the local CUSUM statistics and also developing its asymptotic optimal properties. He also worked with his Ph.D. students to develop general scalable methodologies that apply shrinkage methods to address the sparsity issues when online monitoring high-dimensional data. Currently he is interested in developing useful theories and scalable methodologies for efficient real-time or online data-driven decision-making under various constraints on computing, communication, sampling, or privacy. Dr. Mei’s work has received several recognitions, including 2009 Abraham Wald prize in Sequential Analysis, 2010 NSF CAREER Award, 2023 ASA Fellow, and multiple best paper awards. |
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Yao Xie is the Coca-Cola Foundation Chair, Professor at Georgia Institute of Technology in the H. Milton Stewart School of Industrial and Systems Engineering, and Associate Director of the Machine Learning Center. From September 2017 until May 2023, she was the Harold R. and Mary Anne Nash Early Career Professor. She received her Ph.D. in Electrical Engineering (minor in Mathematics) from Stanford University in 2012 and was a Research Scientist at Duke University. Her research lies at the intersection of statistics, machine learning, and optimization in providing theoretical guarantees and developing computationally efficient and statistically powerful methods for problems motivated by real-world applications. She received the National Science Foundation (NSF) CAREER Award in 2017, INFORMS Wagner Prize Finalist in 2021, and the INFORMS Gaver Early Career Award for Excellence in Operations Research in 2022. She is currently an Associate Editor for IEEE Transactions on Information Theory, IEEE Transactions on Signal Processing, Journal of the American Statistical Association-Theory and Methods, Sequential Analysis: Design Methods and Applications, INFORMS Journal on Data Science, and an Area Chair of NeurIPS and ICML. |
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Professor Zhuang obtained her doctoral degree in 2018 under the supervision of Professor Nitis Mukhopadhyay. She conducts research in theory and applications in statistics, including applied probability, sequential analysis, statistical inference, and interdisciplinary research at Connecticut College, New London, Connecticut. She has published more than 16 peer-reviewed full-length journal articles on sufficiency to ancillarity to distribution theory to sequential estimation and tests, along with deep collaborative research in leading international journals. She has developed statistical methodologies to make the statistical point and interval estimation and hypothesis testing more efficient and accurate under different scenarios. In applications of statistics, Professor Zhuang has completed an extensive array of interdisciplinary collaborative research projects with researchers from areas such as chemistry, environmental engineering, agriculture, and food science. She has organized and chaired 10 invited sessions at various international and regional conferences including the International Workshop in Sequential Methodologies (IWSM) and the New England Statistical Symposium (NESS). She has delivered more than 10 full-length invited research presentations at international and regional conferences and departmental colloquia. IWSM warmly welcomes this bright and young researcher to present a major lecture. |
