Xiaoming Liu is an Associate Professor in the Department of Statistical and Actuarial Sciences at Western University's Faculty of Social Science. His research focuses on actuarial science, stochastic processes, and mathematical finance, particularly in mortality modeling and interest rate risk analysis. Ph.D., University of Toronto (2008) Research interests include stochastic mortality modeling, regime-switching dynamics, and guaranteed annuity option pricing. His work integrates comonotonicity theory, Markov processes, and probability measure transformations to address correlated financial and demographic risks. Notable awards include the Associateship of the Society of Actuaries (ASA, 2016). He has supervised graduate students in actuarial research and published extensively in top-tier journals like Insurance: Mathematics and Economics and Stochastics.
Lisha Chen is an Assistant Professor in the Department of Statistics at Yale University, located at 24 Hillhouse Avenue, New Haven. Her academic work focuses on developing statistical methodologies for high-dimensional data analysis, with particular emphasis on applications in machine learning and medical research. She maintains active research collaborations as evidenced by her publication record in top statistical journals. Her core research explores: Dimension reduction techniques including multidimensional scaling and sparse modeling Advanced regression methods such as reduced-rank regression and variable selection Statistical learning applications in autism research using eye-tracking data Novel algorithms for imbalance learning and multivariate testing Her work bridges theoretical statistics with practical applications in medicine and social sciences. Analysis of her 12 most recent publications (2008-2014) reveals three primary research streams: Development of dimension reduction frameworks for visualization and analysis Innovation in variable selection methodologies for regression and classification Application of statistical learning to autism spectrum disorder research Her publications demonstrate consistent focus on solving high-dimensional problems through novel statistical computing approaches. At Yale, she has taught multiple courses including: Data Analysis (Fall semesters: 2006-2008, 2011-2013) Introductory Statistics (Spring 2013) Data Mining and Machine Learning (Spring semesters: 2007-2009, 2011-2013) Unsupervised Learning: Dimension Reduction and Clustering Analysis (Spring 2009)
Vasilis Syrgkanis is an Assistant Professor at the Department of Management Science and Engineering , Stanford University, with courtesy appointments in Computer Science and Electrical Engineering. He leads the Stanford Causal AI Lab and is an Associated Director of the Stanford Causal Science Center . His research spans machine learning, causal inference, econometrics, online learning, reinforcement learning, game theory/mechanism design, and algorithm design . Education PhD in Computer Science, Cornell University (advised by Eva Tardos) Diploma in EECS, National Technical University of Athens Research Focus Develops methods for causal machine learning , including courses on Applied Causal Inference and Foundations of Causal ML. Works on treatment effect estimation , instrumental variable regression , and robust policy learning under unobserved heterogeneity. Explores intersections of game theory and machine learning , particularly in auction design and strategic exploration. Scientific Contributions Proposes neural causal partial identification and adaptive instrument design frameworks. Develops doubly robust learning and minimax IV regression algorithms with theoretical guarantees. Introduces incentive-aware synthetic control and structure-agnostic causal effect estimation methods. Awards & Recognition 2023 Bodossaki Distinguished Young Scientist Award in Applied Sciences 2022 Amazon Research Award in Machine Learning Algorithms and Theory Best Paper Awards at COLT 2019, EC 2015, NeurIPS 2015, and others PhD Advisees Ravi Sojitra (MS&E, co-advised with Guido Imbens) Hui Lan (ICME) Jikai Jin (ICME) Jiyuan Tan (MS&E, co-advised with Jose Blanchet) Keertana Veeramony Chidambaram (MS&E) Contact Email: vsyrgk@stanford.edu Office: Huang Engineering Center, Room 252, Stanford, CA 94305
Azeem M. Shaikh is the Ralph and Mary Otis Isham Professor and Chairman of the Department of Economics at the University of Chicago. He co-directs the Big Data Initiative at the Becker Friedman Institute. His research spans econometric theory, causal inference, and experimental design, with applications to early childhood education and economic mobility. B.S. in Mathematics, Duke University Ph.D. in Economics, Stanford University (2006) His research interests include: Randomization and resampling methods (bootstrap, subsampling) Multiple hypothesis testing and partial identification Design and analysis of experiments with matched pairs Evaluation of social programs like the HighScope Perry Preschool Recent publications focus on: Advances in randomization inference and stratified experiments Ranking methodologies for political parties and neighborhoods Handling imperfect compliance in experimental settings Software tools like csranks for statistical inference Scientific awards include: Dennis J. Aigner Award for Applied Econometrics Hoover National Fellowship Alfred P. Sloan Fellowship Elected Fellow of the Econometric Society (2018) Elected Fellow of the International Association for Applied Econometrics (2018) Grants from the National Science Foundation, Stanford Institute for Economic Policy Research, and the Hoover Institution supported his work. He held editorial positions at Journal of Political Economy , Econometrica , and Journal of Econometrics .
Yue (Ray) Wang is an Assistant Professor at the School of Information and Library Science, University of North Carolina at Chapel Hill. He holds a PhD in Computer Science and Engineering from the University of Michigan, and prior degrees from Shanghai Jiao Tong University and Georgia Institute of Technology. His research focuses on text data mining, machine learning, and health informatics, with an emphasis on developing interactive and interpretable algorithms to reduce human effort in data analysis. He has contributed to clinical NLP, high-recall information retrieval, and computer-assisted qualitative analysis, winning awards such as the WSDM Best Paper Award (2016) and the Deborah Barreau Award for Teaching Excellence (2021). Dr. Wang’s work bridges computational methods with real-world applications, including environmental policymaking through extreme systematic reviews and NeuroBridge—a platform for neuroimaging data discovery. He teaches courses like INLS 509 (Information Retrieval) and INLS 690-270 (Data Mining). His recent research explores user-centered explainability in AI, leveraging crowdsourced experiments and in-lab studies to understand how users interact with machine predictions. He collaborates with organizations like the EPA on machine-assisted literature screening for environmental policy. Education: BS/BA in Information Security & English (Shanghai Jiao Tong University) MS in Computer Applied Technology (Shanghai Jiao Tong University) MS in Electrical and Computer Engineering (Georgia Tech) PhD in Computer Science and Engineering (University of Michigan) Awards: UNC Junior Faculty Development Award (2022) Outstanding Reviewer Award (WSDM 2022) Deborah Barreau Teaching Excellence Award (2021) WSDM Best Paper Award & Outstanding Reviewer (2016) Dow Distinguished Award (2015) Key Research Themes: User-centric AI interpretability Health informatics applications Large-scale data screening for policy Explainable retrieval models
Dr. Jin Piao is a Clinical Associate Professor in the Department of Population and Public Health Sciences at the Keck School of Medicine, University of Southern California. Her research focuses on biostatistical methodologies for clinical trials, survival analysis, and meta-analysis in pediatric oncology. Division of Biostatistics Children’s Oncology Group (COG) collaborator NCI-COG Pediatric MATCH trial participant Her work includes: Developing statistical models for clinical trials Collaborating on pediatric solid tumor studies Advancing miR-371a-3p as a diagnostic biomarker for germ cell tumors Addressing publication bias in systematic reviews Key article trends: Recent publications emphasize precision oncology in rare pediatric tumors (2025-2023) Focus on genomic alterations in MAPK, PI3K/mTOR, and DNA repair pathways Methodological innovations in survival analysis and network meta-analysis Clinical implementation of circulating microRNA diagnostics
David Haziza is a Full Professor in the Department of Mathematics and Statistics at the University of Ottawa, affiliated with the Faculty of Science. He holds a PhD from Carleton University (2005). His research focuses on survey sampling theory and applications, including methodologies for handling missing data, influential units, resampling techniques, and machine learning integration in statistical analysis. His work bridges theoretical advancements and practical applications in survey methodology. No specific awards, grants, or advisees are listed in the provided information. His professional activities include editorial board roles and conference participation, though details are not elaborated here.
Dr. Emmanuel Ogundimu is an Associate Professor in the Department of Mathematical Sciences at Durham University and a Fellow of the Wolfson Research Institute for Health and Wellbeing. His research focuses on applied statistics, biostatistics, machine learning, causal inference, missing data methodology, and models for rare events. He has contributed to high-impact studies in healthcare, finance, and statistical theory, including clinical trials, predictive modeling in credit scoring, and methodological advancements in sample selection and imputation techniques. Key publications include work on randomized controlled trials for autism interventions, colonoscopy performance optimization, and regularization techniques in econometric models. His interdisciplinary approach bridges statistical theory with practical applications in healthcare and finance. Dr. Ogundimu currently supervises three students: Adam Iqbal, Adam Stone, and Duaa Nadhrah. He collaborates widely, evidenced by his co-authored publications across medical journals and statistical methodologies. His research emphasizes rigorous statistical methods for addressing real-world challenges, such as improving healthcare outcomes through robust data analysis and enhancing predictive accuracy in rare event scenarios. He is actively involved in advancing statistical models for bounded outcomes, missing data, and skewed distributions, contributing to both theoretical and applied statistical literature.
Rohan Dalpatadu is a Professor in the Department of Mathematical Sciences at the University of Nevada, Las Vegas (UNLV). His research expertise spans statistical methodology, applied mathematics, and interdisciplinary applications in healthcare, environmental science, and gaming. He holds a terminal academic rank and has contributed to both theoretical and applied research areas. Research Interests include: Statistical modeling and machine learning applications in biomedicine Numerical methods for optimal control and environmental systems Probability distributions and their applications in gambling and actuarial science Data analysis techniques for imbalanced datasets and customer satisfaction studies Recent work focuses on cancer classification using gene expression data, contaminant transport modeling, and innovative statistical approaches for medical diagnostics. His publications demonstrate a strong interdisciplinary orientation across mathematics, engineering, and health sciences. Advising and Grants: No specific advising or grant information is explicitly listed in the provided text. His research has been applied to diverse sectors including airport operations, agricultural economics, and ergonomic design. Labs/Teams: No dedicated lab or collaborative team affiliations are mentioned in the current data.
Bo Honoré is the Class of 1913 Professor of Political Economy and Professor of Economics at Princeton University's Department of Economics. He has held leadership roles such as Director of Graduate Studies, Director of the Gregory C. Chow Econometric Research Program, and Department Chair. Previously, he taught at Northwestern University and held visiting positions at the University of Chicago and University of Copenhagen. He earned his Ph.D. from the University of Chicago. His research focuses on econometric theory, panel data analysis, duration models, and censored regression. Notable contributions include work on interdependent durations, binary outcome models, and identification strategies. He has been recognized with awards like the Richard E. Quandt Teaching Prize and the Rigmor and Carl Holst-Knudsen Award. His articles explore topics such as retirement planning dynamics, pandemic labor market impacts, and econometric methodology. He has served on editorial boards and contributed to policy-related research. His work bridges theoretical econometrics with applied issues in labor and health economics.
Han Hong is a Professor of Economics at Stanford University's Department of Economics. He holds a Ph.D. in Economics (1998), M.S. in Computer Science (1998), and M.S. in Statistics (1997) from Stanford University, and a B.A. in International Trade from Zhongshan University (1993). His research focuses on econometric methodology, health econometrics, statistical modeling, and computational economics. He has developed innovative approaches for panel data analysis, structural estimation, and decision-making algorithms. Professor Hong has received numerous honors including the Willard G. Manning Memorial Award (2017), Arrow Award Honorable Mention (2015), and multiple NSF grants. He serves as Co-Editor of the Journal of Econometrics and is a Fellow of the Econometric Society. He teaches courses in Advanced Econometrics, Data Science, and Econometric Methods, and mentors students through honors thesis research and directed reading programs.
Kenneth Lange is a Professor at the University of California, Los Angeles (UCLA) in the departments of Computational Medicine and Human Genetics . He holds the Maxine and Eugene Rosenfeld Endowed Chair in Computational Genetics and focuses on genomic data analysis , statistical genetics , and optimization algorithms for biomedical applications. His research interests span Computational Genetics Biomedical Big Data Statistical Methods for Gene Mapping Optimization Algorithms Machine Learning . He has developed advanced methods for genetic admixture estimation, genotype imputation, and cancer stem cell therapy modeling. Dr. Lange's publications (2024-2013) emphasize statistical genetics , computational biology , and optimization techniques . Key trends include haplotype analysis , neuroimage registration , ancestry-informative markers , and penalized regression methods . Scientific awards include the Maxine and Eugene Rosenfeld Endowed Chair in Computational Genetics . He has advised graduate students such as Seyoon Ko , Benjamin Chu , and Jeanette Papp , with significant contributions to genomic analysis and biomedical informatics . Dr. Lange leads NIH-funded projects like R35GM141798 (Modeling, Inference, and Optimization for Genomic and Biomedical Big Data, 2021-2026) and co-led T32HG002536 (Genomic Analysis Training Grant, 2002-2022). He has also participated in grants for statistical methods (R01GM053275, 1995-2021) and integrative biology (T32GM008185, 1987-2023).
Kengo Kato is a Professor in the Department of Statistics and Data Science at Cornell University , affiliated with the College of Arts and Sciences. Previously, he held a position at the Faculty of Economics at The University of Tokyo and served as a visiting scholar at MIT’s Department of Economics. His research focuses on Mathematical Statistics, Applied Probability, and Econometrics , with an emphasis on high-dimensional statistical models and optimal transport theory. His work spans topics such as statistical inference in optimal transport, Gromov-Wasserstein distances, bootstrap methods, and high-dimensional data analysis. Notably, he is Editor-in-Chief of the journal Bernoulli (2025-2027) and serves as an associate editor for Japanese Economic Review and Journal of Statistical Planning and Inference . His research bridges theoretical advancements with practical applications in econometrics and machine learning. Recent publications emphasize foundational results in optimal transport theory, including limit laws for Gromov-Wasserstein alignment, stability of entropic maps, and statistical guarantees for sliced Wasserstein distances. His work on bootstrap techniques addresses challenges in high-dimensional and spatial data analysis, offering robust inference methods for modern datasets. While no formal advisees are listed, his contributions to statistical theory and methodology suggest significant mentorship in graduate training programs. His research is supported by Cornell’s interdisciplinary environment and collaborations within affiliated institutes.
Dr. Theodore Karrison is a Professor in the Department of Public Health Sciences at the University of Chicago. He serves as Director of the Biostatistics Laboratory and Technical Director of the Biostatistics Core for the Comprehensive Cancer Center. His work focuses on clinical trial design, oncology research, and survival analysis methodologies. Dr. Karrison has contributed to pivotal studies in areas such as genitourinary cancers, phase II trial endpoints, and response-adaptive designs. Education: B.S. in Mathematics & Computer Science (University of Illinois at Chicago, 1974), M.S. and Ph.D. in Statistics (University of Chicago, 1975 and 1985). Research Interests: Development of statistical methods for clinical trials, restricted mean survival analysis, and collaborative oncology studies. Notable contributions include estimating insulin secretion rates, analyzing aneurysm rupture risks, and designing phase II cancer trials. Current Projects: Leading statistical work for NRG Oncology genitourinary trials, validating metformin in ovarian cancer, and studying azithromycin's effects on asthma microbiomes. Collaborates with multiple institutions on trial design and data analysis. Grants & Labs: Principal investigator on NIH-funded studies including cancer center support grants and early therapeutics development projects. Oversees Biostatistics Core operations supporting over 200 investigators.
Larry Wasserman is a UPMC University Professor at Carnegie Mellon University, jointly appointed in the Department of Statistics and Data Science and the Machine Learning Department. He received his Ph.D. from the University of Toronto in 1988 and is recognized as one of the leading statisticians of his generation. His research spans theoretical and applied statistics, with core interests in: Foundational inference : Nonparametric methods, asymptotic theory, causal frameworks Modern applications : Machine learning, high-dimensional statistics, astrostatistics Interdisciplinary domains : Bioinformatics, genomics, physical sciences via the STAMPS group His recent publications demonstrate strong emphasis on causal methodology, optimal transport, and robust inference, with applications ranging from particle physics to genomic analysis. Articles frequently develop novel nonparametric techniques with minimax optimality guarantees. Award highlights include: COPSS Presidents' Award (1999) - Top honor for statisticians under 40 CRM-SSC Prize (2002) - Landmark contributions to statistics Fellowships: American Statistical Association, Institute of Mathematical Statistics, AAAS He leads the Statistical Machine Learning Theory Group and founded STAMPS (Statistical Methods for Physical Sciences). His textbooks All of Statistics and All of Nonparametric Statistics are widely used in graduate programs globally.
