Find Academic Supervisors

Kim Jee-SeonView profile

Professor

• Multilevel Modeling
• Latent Variable Modeling
• Causal Inference
• +5 more

Jee-Seon Kim is a Professor in the Department of Educational Psychology at the University of Wisconsin-Madison, specializing in quantitative methods. She holds a BS and MS in Statistics from Ewha Womans University and a PhD in Quantitative Psychology from the University of Illinois at Urbana-Champaign. Her research focuses on developing statistical methods for social and behavioral sciences, including multilevel modeling, latent variable modeling, causal inference, and machine learning applications with clustered data. Key areas include treatment effect estimation, longitudinal data analysis, and unobserved heterogeneity. Dr. Kim has contributed to over 50 peer-reviewed publications, with recent work addressing hybrid machine learning models for heterogeneous treatment effects and causal inference strategies in clustered observational data. Her work bridges methodological innovation and practical applications, such as improving opioid use disorder treatment policies and healthcare provider capacity. Awards: Vilas Faculty Mid-Career Investigator Award (2019) Fellow, National Academy of Education/Spencer Foundation (2004) Grants & Advising: Extensive grants focused on causal inference methods, substance use disorder policy, and educational measurement. Advised graduate students on multilevel modeling and latent variable applications. Labs/Teams: Active in interdisciplinary collaborations, including opioid treatment research and psychometric method development.

• Probability
• Statistics
• Biostatistics
• +3 more

Bee Leng Lee is a Professor in the Department of Mathematics and Statistics at San José State University. She holds a Ph.D. and M.S. in Statistics from the University of Wisconsin-Madison and a B.Soc.Sci. in Statistics from the National University of Singapore. Her research focuses on probability, statistics, and biostatistics, with a strong emphasis on Bayesian methods, clinical trial design, and survival analysis. She has contributed to high-impact works such as dose-finding algorithms for phase I trials and applications of statistical methods in resolving legal disputes. Dr. Lee teaches a wide range of courses including Applied Probability and Statistics, Bayesian Data Analysis, Survival Analysis, and Computational Statistics. She has advised numerous student projects, including studies on stepwise variable selection limitations, generalized linear mixed models for categorical data, and Monte Carlo methods for financial derivatives. Her teaching also extends to foundational courses like Calculus I and Finite Mathematics. Her research projects include collaborations with industry sponsors like IBM (Almaden Research Center) on simulations and metamodeling. She actively publishes in top journals, with recent focus on balancing effective sample sizes in Bayesian designs and resolving contract disputes via statistical experimental design. Her work bridges theoretical statistics with practical applications in medicine, engineering, and public policy. Dr. Lee's contributions to statistical education and clinical trial methodology have positioned her as a key figure in advancing data-driven solutions at both academic and applied levels. She mentors students in cutting-edge statistical programming (R) and oversees special study projects that tackle real-world data challenges.

Seul ki KangView profile

Assistant Professor

• Actuarial Mathematics
• Extreme Value Theory
• Statistical Modeling
• +3 more

Seul ki Kang is an Assistant Professor in the Mathematics Department at the University of St. Thomas's College of Arts and Sciences. With dual PhDs in Risk Management and Insurance from Georgia State University and Applied Mathematics from Texas A&M, Kang's research spans actuarial mathematics, extreme value theory, and statistical modeling of financial losses. His publications focus on innovative risk analysis methodologies for insurance applications, developing multi-step approaches for ratemaking and risk assessment. Kang's computational modeling expertise includes spectral methods for analyzing flows in heterogeneous media. Courses taught include Foundations of Actuarial Mathematics (ACSC 451), covering life insurance mathematics, annuities, and premium calculations using probabilistic approaches.

Soumendra LahiriView profile

Professor

• High-Dimensional Statistics
• Machine Learning
• Resampling Methods
• +5 more

Soumendra Lahiri is a Professor and Stanley A. Sawyer Professor in Mathematics and Statistics at Washington University in St. Louis's Department of Statistics and Data Science. He earned his PhD from Michigan State University in 1989 and has held faculty positions at Iowa State University, Texas A&M University, and North Carolina State University before joining WashU in 2019. His research spans theoretical and applied statistics with cross-disciplinary impact. Lahiri's research integrates higher-order asymptotic theory , resampling methods , and high-dimensional inference . He develops novel statistical methodologies for complex data structures in neuroscience, astrophysics, and econometrics. His recent work focuses on bootstrap techniques for modern data challenges like network analysis and machine learning model uncertainty. His publication portfolio shows a consistent focus on: Advancing resampling methods for dependent and high-dimensional data Developing theoretical guarantees for machine learning algorithms Creating inference tools for spatial, temporal, and network-structured data Bridging statistical theory with applications in natural and social sciences Honors include the endowed Stanley A. Sawyer Professorship. He maintains an active research program with recent publications in statistical methodology and interdisciplinary applications, particularly in electoral modeling, network analysis, and extreme value theory.

Carlos Misael Madrid PadillaView profile

Assistant Professor

• High-dimensional Statistics
• Nonparametric Statistics
• Change Point Detection
• +3 more

Dr. Carlos Misael Madrid Padilla is Assistant Professor of Statistics & Data Science at Washington University in St. Louis, where he researches high-dimensional statistics, change point detection, and Bayesian methods. He holds a PhD in Mathematics from the University of Notre Dame and a BS from CIMAT Mexico. His recent publications focus on advanced change point detection methodologies and functional data analysis. Current research explores temporal-spatial modeling using trend filtering and neural networks.

Xuming HeView profile

Professor

• Robust Statistics
• Quantile Regression
• Bayesian Inference
• +7 more

Xuming He is the inaugural Chair of the Department of Statistics & Data Science at Washington University in St. Louis, holding the Kotzubei-Beckmann Distinguished Professorship. Previously, he served as the H.C. Carver Collegiate Professor at the University of Michigan and held roles at the National University of Singapore and the National Science Foundation. His research focuses on robust statistics, quantile regression, Bayesian inference, and interdisciplinary applications in bioinformatics, public health, and atmospheric science. Education: BS in Applied Mathematics (Fudan University, 1984); MS and PhD in Mathematics/Statistics (University of Illinois, 1988-1989). He is a Fellow of the American Statistical Association and the American Association for the Advancement of Science, and currently serves as President of the International Statistical Institute (2023-2025). His work bridges theoretical advancements in statistical methodology with practical applications in clinical trials, environmental science, and sports medicine. Notable contributions include developing scalable quantile regression techniques and promoting reproducible statistical practices through initiatives like the ASA Task Force on Statistical Significance. Awards: Kotzubei-Beckmann Professorship, ISI Presidency, ASA/AAAS Fellowships. His advising and grants focus on advancing statistical methods for complex data structures, with collaborations in concussion research and interdisciplinary data science. He leads the SDS department’s mission to integrate cutting-edge statistical theory with real-world problem-solving.

Sarah FilippiView profile

Professor

• Statistical Machine Learning
• Bayesian Statistics
• Biostatistics
• +5 more

Sarah Filippi is a Professor and Reader in Statistical Machine Learning at the Department of Mathematics, Imperial College London, and Joint Director of the EPSRC Centre for Doctoral Training in Statistics and Machine Learning (StatML) at Imperial and Oxford. She holds affiliations with multiple interdisciplinary networks, including the CNRS-Imperial Abraham de Moivre UMI, AI for Healthcare, and the Wound Healing and Regeneration Network. Her research focuses on Bayesian methods, nonparametric approaches, and their applications in biomedical problems, including systems biology, pharmacology, and epidemiology. Dr. Filippi earned her Ph.D. in Statistical Machine Learning from Télécom ParisTech in 2010. She has held roles at Imperial College London (2011–2014 as an MRC Fellow, 2017–present in various senior positions) and the University of Oxford (2014–2017). Her work bridges statistical theory and practical biomedical challenges, emphasizing uncertainty quantification and scalable algorithms. Her research group develops methods for causal inference, kernel-based learning, and decision-making under uncertainty. Recent publications highlight contributions to clustering algorithms, reinforcement learning, and epidemiological studies. She has received the Medical Research Council Fellowship (2011–2014) and leads collaborative projects with clinicians and computational biologists. Dr. Filippi advises a vibrant research group, including Ph.D. students working on continual learning, Bayesian optimization, and healthcare applications. Her interdisciplinary collaborations span computational biology, chronic disease modeling, and material flow analysis, reflecting her commitment to translational research.

• Machine Learning
• Spatial Statistics
• Bayesian Inference
• +3 more

James Martin is an Honorary Research Fellow in the Statistics Section of the Department of Mathematics at Imperial College London, part of the Faculty of Natural Sciences. He previously served as a Senior Teaching Fellow, leading modules on the MSc in Machine Learning and Data Science. His research focuses on machine learning applications in engineering, spatial statistics for ecological systems, and Bayesian inference methods using likelihood-free approaches, particularly sequential Monte Carlo techniques. He is affiliated with the Artificial Intelligence Network, Mathematics research and teaching staff, and the Statistics group. His work bridges theoretical statistical methodologies with practical applications in environmental and engineering domains. Recent publications (2012–2025) emphasize advancements in Bayesian computational methods, spatial point processes, and network analysis, reflecting his interdisciplinary approach to solving complex data-driven problems. While no formal advising or grant details are provided, his academic contributions underscore a commitment to advancing computational and applied statistics. He collaborates within Imperial’s mathematics and AI communities, contributing to cutting-edge research initiatives in data science.

• random measures
• non-parametric Bayesian analysis
• extreme value theory
• +2 more

Riccardo Passeggeri is an Assistant Professor (with tenure) in Statistics at the Department of Mathematics, Imperial College London, within the Faculty of Natural Sciences. His research focuses on probability and mathematical statistics, particularly random measures, non-parametric Bayesian analysis, extreme value theory, robust statistics, and stochastic analysis. Research Interests: Random measures and their applications Non-parametric Bayesian methods Extreme value theory and robust statistical techniques Stochastic processes and analysis He is actively seeking PhD students interested in his research areas and encourages applications via email. His work spans theoretical advancements and applied methodologies in probability and statistics. Advising & Grants: While no grants are explicitly listed, Dr. Passeggeri emphasizes student mentorship and collaboration in his research endeavors.

• Statistics
• Econometrics
• Numerical and Computational Mathematics
• +7 more

Alastair Young is a Professor and Chair in Statistics at Imperial College London's Department of Mathematics within the Faculty of Natural Sciences. His research focuses on advanced frequentist statistical methods, including bootstrap techniques, saddlepoint approximations, and spatial data inference. He has held prior roles as Reader in Methodological Statistics at the University of Cambridge (pre-2005). Key research areas include computer-intensive statistical methods, parametric and non-parametric inference, and approximation strategies for complex models. He is a Fellow of the Institute of Mathematical Statistics and previously served as Joint Editor of the Journal of the Royal Statistical Society, Series B. Alastair's work bridges theoretical developments (e.g., hybrid bootstrap methods, quantile estimation) with applications in econometrics, artificial intelligence, and bioinformatics. Recent publications emphasize causal inference in clinical trials, high-dimensional statistics, and principled data science methodologies. Awards: Fellow of the Institute of Mathematical Statistics Editorial Role: Former Joint Editor, JRSS Series B Key Themes: Bootstrap methodology, likelihood-based inference, spatial statistics

Moulinath BanerjeeView profile

Professor

• Non-standard statistical problems
• Empirical process theory
• Threshold and boundary estimation
• +7 more

Moulinath Banerjee is a Professor of Statistics at the University of Michigan, affiliated with the Department of Statistics within the College of Literature, Science, and the Arts (LSA). He holds a B.Stat and M.Stat from the Indian Statistical Institute and a Ph.D. from the University of Washington. His research focuses on non-standard statistical problems, empirical process theory, threshold estimation, graphical networks, and the intersection of statistics with machine learning, particularly in distributed computing and data integration. Education: B.Stat (Hons), Indian Statistical Institute, 1995 M.Stat (Mathematical Statistics and Probability), Indian Statistical Institute, 1997 Ph.D. in Statistics, University of Washington, 2000 Research Interests: Inference under shape-restrictions and non-differentiable models Statistics-ML interface, including transfer learning and weak supervision High-dimensional and low-dimensional statistical methods Applications in crime modeling, policy evaluation, and causal inference Recent work emphasizes statistical methodologies for modern challenges like distributed computing, data integration, and performativity in predictive systems. His articles span topics such as change-plane regression, Hawkes processes, and posterior drift in transfer learning, reflecting contributions to both theory and applied domains. Scientific Awards: 2011 IISA Young Investigators Award Fellow of the Institute of Mathematical Statistics (IMS) Fellow of the American Statistical Association (ASA) IMS Medallion Lecture Awardee (2024) Teaching includes advanced courses in statistical theory (e.g., Stats 610, 611, 612) and applied methods (e.g., Stats 412, 425). Collaborations span academia and industry, addressing interdisciplinary challenges in data science and statistical methodology.

Colin FogartyView profile

Assistant Professor

• Causal Inference
• Observational Studies
• Experimental Design
• +3 more

Colin Fogarty is an Assistant Professor of Statistics at the University of Michigan, Department of Statistics within the College of Literature, Science, and the Arts. Previously, he held positions at MIT Sloan School of Management and completed his Ph.D. at the Wharton School of the University of Pennsylvania. His research focuses on causal inference, particularly in observational studies, experimental design, and applications in medicine and public health. He has contributed significantly to sensitivity analysis, heterogeneous treatment effects, and robust statistical methodologies. Education includes a Ph.D. in Statistics from Wharton (2016) and an A.B. in Statistics from Harvard University (2011). Awards include the Biometrics Early-Stage Investigator Award (2018), Tom Ten Have Award (2017), and J. Parker Bursk Memorial Prize (2015). His work often addresses methodological challenges in causal inference, with applications to healthcare outcomes and policy analysis. Teaching spans undergraduate and graduate levels, including courses like Applied Regression Analysis and Statistical Thinking. Fogarty has developed R software packages for Bayesian hierarchical models and sensitivity analysis, reflecting his commitment to computational tools for statistical practice.

Ya'acov RitovView profile

Professor

• Statistics
• Semiparametric Models
• High-Dimensional Data Analysis
• +4 more

Ya'acov Ritov is a Professor of Statistics at the University of Michigan, affiliated with the Department of Statistics under the Literature, Science, and the Arts (LSA) School. He also held the Francis Hock Emeritus Chair in Statistics at The Hebrew University of Jerusalem. His academic career includes positions as a Lecturer (1984), Senior Lecturer (1989), Associate Professor (1990), and Professor (1992) at Hebrew University before joining the University of Michigan as a Professor in 2015. Ritov's research focuses on statistical theory, semiparametric models, high-dimensional data analysis, and empirical Bayes methods. He has contributed to areas such as robust Bayes procedures, errors-in-variables models, and the analysis of contingency tables. His work bridges theoretical advancements with practical applications in fields like machine learning, biostatistics, and econometrics. Education: B.Sc. in Electrical Engineering (1973, Technion), M.Sc. in Electrical Engineering (1980, Technion), Ph.D. in Statistics (1983, Hebrew University of Jerusalem). His doctoral thesis, advised by Peter J. Bickel and Yosef Yahav, explored robust Bayesian procedures. Scientific Awards: Francis Hock Emeritus Chair in Statistics (Hebrew University). Students: Advised numerous Ph.D. and Master's students, including Michael Law, Hamid Eftekhari, and Debarghya Mukherjee. His academic mentorship spans foundational statistical theory and applied methodologies. Labs/Teams: Collaborates extensively with researchers in statistics and interdisciplinary fields, contributing to projects on algorithmic fairness, transfer learning, and high-dimensional inference.

Yixin WangView profile

Assistant Professor

• Bayesian Statistics
• Machine Learning
• Causal Inference
• +7 more

Yixin Wang is an Assistant Professor of Statistics at the University of Michigan, affiliated with the Department of Statistics within the College of Literature, Science, and the Arts. His research focuses on Bayesian statistics, causal inference, and machine learning. Previously, he was a postdoctoral researcher at UC Berkeley under Michael Jordan and earned his Ph.D. in Statistics from Columbia University (2020) and B.Sc. in Mathematics & Computer Science from Hong Kong University of Science and Technology (2014). Education: Ph.D., Columbia University (2020); B.Sc., HKUST (2014) Research Interests: Probabilistic generative modeling and Bayesian statistics, including large language models and diffusion models Causal machine learning, including causal representation learning and causal inference for language models Applications in recommender systems, computational biology, and human-AI interactions His work emphasizes robust statistical methods and causal approaches to address real-world challenges, such as bias mitigation in algorithms and equitable treatment allocation in healthcare. He currently leads a research group seeking to advance causal machine learning and probabilistic models, with postdoctoral openings available. Key contributions include developing robust Bayesian methods, causal inference frameworks for unstructured data, and applications in electronic health records and materials discovery. He has been instrumental in bridging theory and practice in areas like feedback loop mitigation in recommenders and causal fairness assessment.

• Robust Statistics
• Machine Learning
• Biostatistics
• +3 more

Matias Salibian-Barrera is a full Professor in the Department of Statistics at the University of British Columbia's Faculty of Science, where he leads research in robust statistical methodologies with applications across machine learning, biostatistics, and environmental sciences. His research focuses on developing computationally efficient robust techniques resistant to outliers and model misspecification. Key areas include: Robust regression and inference for high-dimensional data Robust causal inference with complex observational data Robust functional and spatial data analysis Integration of robust methods with deep learning architectures Applications in genomics, climate science, and finance His recent publications demonstrate a strong trend toward unifying robust statistics with modern machine learning, particularly in adversarial settings and high-dimensional inference, while maintaining rigorous theoretical foundations. Notable honors include: CRM–SSC Prize in Statistics Fellow of the American Statistical Association Killam Research Prize Coxeter-James Prize He actively supervises doctoral students and secures major grants from NSERC and CIHR, focusing on robust methods for biomedical and environmental data. His work involves collaborations with UBC's Machine Learning Group and the Pacific Institute for the Mathematical Sciences, where he develops open-source software for robust statistical computing.