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Quan Zhang is an Assistant Professor in the Department of Accounting and Information Systems at the Broad College of Business, Michigan State University. With a Ph.D. in Information, Risk and Operations Management from the University of Texas at Austin (2020), an M.S. in Biostatistics from the University of Minnesota (2015), and a B.S. in Biological Science and Economics from Peking University (2012), his academic journey bridges diverse disciplines. Education Ph.D., Information, Risk and Operations Management - University of Texas at Austin (2020) M.S., Biostatistics (Ph.D. study) - University of Minnesota at Twin Cities (2015) B.S., Biological Science and Economics - Peking University (2012) His research focuses on interpretable machine learning , particularly balancing model interpretability and capacity using Bayesian and variational inference methods. Applications span telemedicine , quantitative marketing , medical data analysis , and online finance , with recent work on watermarking for copyright protection and robust representation learning. Selected publications include his 2025 paper on Beyond Invisibility: Learning Robust Visible Watermarks , 2023 work on Weibull Racing Survival Analysis , and foundational 2018 contributions to Nonparametric Bayesian Lomax Delegate Racing . His methodological innovations in Bayesian multinomial regression and survival modeling have been published in Advances in Neural Information Processing Systems and the Journal of Machine Learning Research . He contributes to software development with Python and R implementations for Bayesian models, available in his GitHub repository. His teaching includes graduate-level ITM885 Machine Learning and Optimization , emphasizing practical applications in business analytics.

• Machine Learning
• Statistics
• Interpretable Learning
• +7 more

David Bickel is a Professor in the School of Information, Library, and Research Sciences at the University of North Carolina at Greensboro. With a PhD from the University of North Texas and undergraduate studies at Baylor University, he has established himself as a leading researcher in statistical methodology for genomic and biological data analysis. Dr. Bickel's research focuses on empirical Bayes methods, false discovery rates, uncertainty quantification, and information theory. His work bridges theoretical statistics with practical applications in genomics, molecular evolution, metabolomics, and proteomics. He has developed several influential software packages including LFDRenrich, LFDRhat, and LFDREmpiricalBayes that implement his methodological contributions. His publications reveal a consistent focus on improving statistical inference in high-dimensional biological data, with particular attention to proper calibration of statistical evidence and uncertainty quantification. His work helps researchers navigate the challenges of multiple testing that are ubiquitous in modern genomics. Dr. Bickel has authored three major books: Phylogenetic Trees and Molecular Evolution: A Hands-on Introduction with Uncertainty Quantification , Genomics Data Analysis: False Discovery Rates and Empirical Bayes Methods , and a book on Bayes-frequentist statistics that explores integrating these complementary approaches. His research has practical impact across biological sciences, providing methodological tools that help scientists extract reliable insights from complex data while properly accounting for statistical uncertainty. His software implementations make advanced statistical methods accessible to domain scientists who may not have deep statistical expertise. Dr. Bickel maintains an active research program as evidenced by recent publications and software updates, continuing to contribute to the evolving field of statistical genomics and bioinformatics.

• Statistics
• Bioinformatics
• Genomics
• +7 more

Lili Yu is the Karl E. Peace Endowed Chair of Biostatistics and Professor in the Department of Biostatistics, Epidemiology & Environmental Health Sciences at Georgia Southern University, where she has been faculty since 2007. Her academic appointments include serving as Principal Investigator for the Office of International Chinese Statistical Association (2007-2017) and as Co-Principal Investigator for current research projects including 'Hierarchical Bayes Regression Models for Geospatial Inquiries' at Georgia Southern University. Dr. Yu's educational background is highly interdisciplinary, with degrees spanning medicine and statistics: PhD in Biostatistics from Ohio State University (2007) MS in Statistics from Ohio State University (2004) MS in Neuroscience from Capital Medical University (2001) MD in Clinical Medicine from Tianjin Medical University (1995) Her research interests focus on biostatistical methodology development and application, particularly in survival analysis, categorical data analysis, and multivariate data analysis. Dr. Yu has made significant contributions to accelerated failure time models, Bayesian statistics, and spatiotemporal modeling. Her work often addresses important public health questions related to disease modeling, mortality risk factors, and health outcomes assessment. Analysis of her publication trends shows a consistent trajectory from methodological development in survival analysis to applied studies addressing significant health issues. Dr. Yu has received recognition for her scholarly contributions, with an h-index of 11 according to Scopus data, and 1,876 citations across her publications. Her work has appeared in numerous high-impact journals across biostatistics, epidemiology, and public health disciplines. Her research funding includes multiple successful grant awards, demonstrating her ability to secure competitive research support. Notable projects include 'Hierarchical Bayes Regression Models for Geospatial Inquiries' (2022-2023), 'Efficient statistical methods in AFT model for cancer data' (2009-2010), and leadership of the Office of International Chinese Statistical Association (2007-2017). Dr. Yu's collaborative network is extensive, with co-authors from multiple institutions across the United States and internationally. Her work bridges methodological statistics with practical applications in public health and medicine, contributing to United Nations Sustainable Development Goals related to health and well-being.

• Biostatistics
• Survival Analysis
• Categorical Data Analysis
• +5 more

Dr. Tri M. Le serves as Associate Professor of Mathematics and Computer Science and Program Coordinator for the M.S. in Data Science at Mercer University's College of Professional Advancement, Department of Informatics and Mathematics. He joined Mercer in 2017 after working as a Predictive/Computational Statistician at the University of Nebraska-Lincoln, bringing expertise in statistical software (SAS, SPSS, R) and extensive teaching experience at undergraduate and graduate levels. His educational background includes: PhD and MA in Statistics, University of Missouri-Columbia (2014) MS in Probability and Statistics, Ho Chi Minh City University of Natural Sciences (2002) BS in Mathematics and Informatics, Ho Chi Minh City University of Natural Sciences (1999) Dr. Le's research spans Bayesian analysis, decision theory, spatio-temporal modeling, and machine learning. His work addresses fundamental questions in model uncertainty, prediction reliability, and the interpretability-performance trade-off in statistical learning. He has published in leading journals including Journal of Machine Learning Research and Bayesian Analysis, with recent focus on model averaging superiority over selection and theoretical foundations of ensemble methods. Analysis of his 2016-2022 publications reveals consistent focus on Bayesian predictive modeling, with increasing emphasis on interpretable machine learning. His work bridges theoretical statistics and practical applications in healthcare analytics and environmental systems, demonstrating interdisciplinary relevance through collaborations in geoscience and criminal justice research. Dr. Le actively contributes to the academic community as reviewer for Bayesian Analysis and International Conference on Fuzzy Systems and Data Mining, and as Session Chair for the Joint Statistical Meetings. His leadership extends to Mercer's Tenure and Promotion committee and the M.S. in Data Science program coordination. While no dedicated research lab is mentioned, Dr. Le's role as Program Coordinator provides structured opportunities for students through the M.S. in Data Science curriculum. His courses in Data Analytics and Healthcare Data Analytics offer practical training grounded in his research on predictive modeling and statistical inference.

• Bayesian analysis
• Data mining
• Machine learning
• +3 more

Alan M. Polansky is an Associate Professor in the Department of Statistics and Actuarial Science at Northern Illinois University (NIU), where he has maintained a continuous faculty appointment since 1995. His research program centers on advancing nonparametric statistical methodologies with significant contributions to bootstrap theory, smoothing techniques, and observed confidence levels. His academic credentials include a Ph.D. from Southern Methodist University and M.S./B.S. degrees from the University of Texas at San Antonio. As an Honors Faculty Fellow (2022-2023), he developed and taught an innovative seminar on Data and Social Justice for the University Honors Program. Polansky's research spans several interconnected domains: Foundational work on bootstrap methodology and confidence interval construction Development of observed confidence levels as an alternative to multiple comparison techniques Asymptotic theory for statistical limit theorems Emerging research on network data analysis and Bayesian inference for stochastic processes His 25+ year publication record shows consistent evolution from core nonparametric methods toward contemporary applications, with recent work focusing on network statistics and Bayesian approaches while maintaining theoretical rigor. Publications appear in premier journals including the Journal of the Royal Statistical Society, Technometrics, and Computational Statistics and Data Analysis. Key recognitions include: Honors Faculty Fellowship (2022-2023) Authorship of two influential monographs: 'Observed Confidence Levels' (2007) and 'Introduction to Statistical Limit Theory' (2011) As an educator, Professor Polansky maintains regular office hours in DuSable Hall and has advised students across statistics and actuarial science programs. His research demonstrates sustained methodological innovation with practical applications in insurance, manufacturing, and emerging network-based domains, reflecting both deep theoretical expertise and commitment to real-world problem solving.

• Nonparametric Statistics
• Bootstrap Methods
• Smoothing Methods
• +5 more

Michael Irwin Jordan is a Professor at the University of California, Berkeley, ranked #1 worldwide in Machine Learning and Distributed Computing, #36 in Computer Science, and #9 in Statistics according to Academic Influence rankings. His interdisciplinary research bridges theoretical foundations with practical applications across machine learning, statistics, and artificial intelligence. His educational background includes a PhD in Computer Science from the University of California, San Diego, a Master's in Mathematics from Arizona State University, and a Bachelor's in Psychology from Louisiana State University. This diverse foundation has shaped his approach to integrating perspectives from computer science, statistics, neuroscience, and psychology. Professor Jordan's research spans several decades, beginning with foundational work in neural networks and connectionist models. He made seminal contributions to probabilistic graphical models, variational inference, and Bayesian nonparametrics. His 2001 paper 'Latent Dirichlet Allocation' has received over 34,000 citations, becoming one of the most influential papers in machine learning. More recently, his work has focused on applications to biological data analysis, particularly single-cell transcriptomics, where he has developed probabilistic models and software tools like scvi-tools. Member of the National Academy of Engineering (2010) Ranked #1 worldwide in Machine Learning Ranked #1 worldwide in Distributed Computing Ranked #36 worldwide in Computer Science Ranked #9 worldwide in Statistics As an advisor, Professor Jordan has mentored numerous PhD students who have become leaders in academia and industry. His recent work through 2022 shows continued innovation across multiple domains, including uncertainty quantification, theoretical analysis of transfer learning, and scalable frameworks for reinforcement learning. He also contributes significantly to data science education, having helped develop Berkeley's undergraduate data science curriculum.

• Machine Learning
• Statistics
• Artificial Intelligence
• +6 more

Huibin Zhou is the Henry Ford II Professor of Statistics and Data Science at Yale University, where he has held faculty positions since 2004. He served as Chair of the Department of Statistics and Data Science (2017-2021) and earlier as Chair of the Department of Statistics (2012-2017). Professor Zhou earned his Ph.D. from Cornell University in 2004, progressing through academic ranks from Assistant Professor to full Professor at Yale. His research spans high-dimensional statistics, nonparametric estimation, machine learning, and statistical decision theory. Key focus areas include minimax optimality in estimation, Bayesian methods, network analysis, and functional data. Zhou's work frequently bridges theoretical foundations with applications in social networks, signal processing, and computational biology. Analysis of his publications reveals consistent emphasis on theoretical guarantees for statistical methods, with recent work exploring spectral clustering, Gaussian mixtures, variational inference, and privacy-preserving estimation. His research demonstrates deep engagement with minimax optimality, high-dimensional challenges, and computational-statistical tradeoffs. Honors & Awards: Henry Ford II Professorship at Yale University Professor Zhou has taught graduate and undergraduate courses including Probability Theory, Statistical Decision Theory, Nonparametric Estimation, Data Mining, and Functional Data Analysis. He organized several NSF workshops including the 2012 Workshop on High-Dimensional Data and the 2015 Workshop on Empirical Processes.

• Statistics
• Data Science
• High-dimensional Statistics
• +5 more