Nancy Margaret Reid is a University Professor of Statistical Sciences at the University of Toronto, holding the Canada Research Chair in Statistical Theory and Applications. She has served as Scientific Director of the Canadian Statistical Sciences Institute (2015–2019) and led the Department of Statistical Sciences as Chair (1997–2002). Her research focuses on theoretical statistics, particularly likelihood inference and foundational aspects of statistical methodology. Reid earned her PhD from Stanford University (1979) under Rupert G. Miller, with Brad Efron and Vernon Johns on her committee. Reid's accolades include Fellowships from the Royal Society, Royal Society of Canada, and National Academy of Sciences, as well as the Guy Medal in Gold (2022) and David R. Cox Award (2023). She has authored influential books like *Theory of the Design of Experiments* and contributed to courses on mathematical statistics and likelihood inference. Active in academic service, she teaches graduate-level courses and has advised numerous students and postdocs in theoretical and applied statistical research.
Anna Gottard is an Associate Professor of Statistics at the University of Florence, where she leads the Department of Statistics, Computer Science, and Applications. She directs the Florence Center for Data Science (FDS) and participates in the Technical Scientific Committee of the Tuscan Center for Big Data, Data Science, and AI (CBDAI). Her research focuses on multivariate statistical models, particularly graphical models, and extends to statistical machine learning, fair models, and directional data analysis. She is an Associate Editor for the Journal of the Royal Statistical Society Series A (JRSSA) and Statistical Methods & Applications (SMA). Her recent work includes Bayesian approaches for mixed graphical models, uncertainty-aware classification trees, and methodological advancements in latent uncertainty models. Her contributions span theoretical developments and applied research in interdisciplinary areas like biostatistics and sustainability. Her research interests emphasize bridging statistical theory with practical applications, including fairness in machine learning, interpretable models, and tree-based methodologies. She has actively contributed to open-source software, notably the Mix3Trees R package for mixed-effect tree models. Her work addresses challenges in variable selection, graphical model inference, and ethical AI practices. Current projects explore Bayesian frameworks for complex data structures and methodological improvements in graphical model interpretability. Anna has advised on interdisciplinary collaborations, such as studies on GDPR compliance in biobanking and epidemiological modeling of the SARS-CoV-2 pandemic in Tuscany. She collaborates with institutions like the CBDAI to advance data science applications in regional policy and healthcare. Her research trajectory reflects a commitment to both foundational statistical theory and real-world problem-solving across diverse domains.
Professor Jiti Gao is a Donald Cochrane Chair in Econometrics & Business Statistics at Monash University's Faculty of Business and Economics. He leads the Department of Econometrics and Business Statistics, specializing in non- and semi-parametric econometrics, time-series analysis, and panel data methodologies. His research focuses on developing statistical models for climate change, energy demand, and financial forecasting. Affiliations: Monash University, Impact Labs Grants: Multiple ARC Discovery Projects (e.g., 2020–2025 on climate-energy time series, 2017–2020 on econometric model building) Collaborations: CSIRO, Yale University, and international partners from China, Norway, and Singapore Research interests include climate econometrics, financial time series, and policy evaluation. Over 136 publications span econometric theory and applications, with recent work on nonlinear trending models and quantile regression. His grants emphasize methodological advancements in time series and panel data analysis. Awards: Not explicitly mentioned, but recognition includes Australian Professorial Fellow status and international research leadership roles. Advising/Grants: Primary Investigator on multiple ARC-funded projects, focusing on climate modeling and financial econometrics Labs/Teams: Part of Monash's Impact Labs and collaborates with global institutions on climate and econometric initiatives
University of California, Los AngelesUnited States
Zhipeng Liao is a Professor of Economics at the University of California, Los Angeles (UCLA), where he contributes to the Department of Economics. He holds a Ph.D. from Yale University and specializes in econometric theory and applied econometrics. His research focuses on developing statistical methods for evaluating economic models, nonstationary time series analysis, and robust inference in semi/nonparametric frameworks. Professor Liao's work has been published in leading journals such as the Annals of Statistics , Econometrica , and the Review of Economic Studies . He serves on the editorial boards of several prestigious journals, including Econometric Reviews , Econometric Theory , and Journal of Business & Economic Statistics . His research interests span econometric theory, time series analysis, panel data modeling, and nonparametric inference, with applications to financial economics and macroeconomic modeling. His recent publications emphasize methodological advancements in hypothesis testing, model selection, and robust estimation techniques. These include contributions to the analysis of spatially dependent panel data, instrumental variables methods, and the evaluation of macro-finance models. His work bridges theoretical econometrics with practical applications, addressing challenges such as endogeneity, model misspecification, and computational efficiency. Liao’s editorial roles reflect his influence in shaping the direction of econometric research. His research has implications for policy analysis, financial market modeling, and empirical studies requiring rigorous statistical foundations. Despite the breadth of his contributions, no specific awards or grants are explicitly mentioned in the provided text.
Ismael Castillo is a Professor of Statistics at Sorbonne Université , affiliated with the Laboratoire de Probabilités, Statistique et Modélisation (LPSM) and its Statistics, Data, Algorithms team. He serves as Associate Editor for Annals of Statistics , Bernoulli , and co-Editor for Bayesian Analysis . Research Interests : Mathematical statistics with emphasis on Bayesian nonparametrics , inference in high-dimensional structures , uncertainty quantification , and applications in signal processing and life sciences . Recent Work spans deep neural networks with heavy-tailed weights , posterior and variational inference , fractional posteriors in semiparametric models , and deep Gaussian processes . His publications demonstrate expertise in multiple testing procedures , Spike and Slab priors , and nonparametric Bayesian methods . Awards : IMS Fellow , Honorary Fellow of Institut Universitaire de France , and Best Paper Prize (2021) for research on Pólya tree posterior distributions. Students : Supervised PhD candidates Paul Egels , Thibault Randrianarisoa , and co-supervised Bo Ning (FSMP postdoc) and Kweku Abraham (Hadamard postdoc). Grants : ANR BACKUP (2023-2027, coordinator) and ANR GAP (2021-2025, member).
Debdeep Pati is a Professor in the Department of Statistics at the University of Wisconsin-Madison, affiliated with the School of Computer, Data & Information Sciences. His research focuses on Bayesian methods, high-dimensional data analysis, machine learning, and computational statistics, with applications in health data and network analysis. He has contributed to approximate Bayesian computation, graphical models, and fair algorithms. Key research interests include Bayes theory in high dimensions, hierarchical modeling, efficient Bayesian computation, and real-time tracking algorithms. His work bridges theoretical advancements with practical applications in areas like electronic health records and nuclear physics constraints. Recent work emphasizes Wasserstein-guided nonparametric Bayes, fair clustering algorithms, and variational inference in singular models. He has developed software for covariate-dependent Gaussian graphical modeling, published in ACM Transactions on Mathematical Software . Grants: NSF proposal on Wasserstein-guided nonparametric Bayes, NIH R01/R21 grants on periodontal disease and diabetes comorbidity. Advising: No named advisees listed but actively supervising research in Bayesian computation and high-dimensional statistics. Awards: 2024 JASA reproducibility award for 'Covariate-Assisted Bayesian Graph Learning.' He is an Associate Editor for Journal of Computational and Graphical Statistics and has organized workshops at Banff International Research Station (BIRS) and the Institute for Mathematics and its Applications (IMSI).
Naisyin Wang is a Professor of Statistics at the University of Michigan, where she has been since 2009. Previously, she served as a faculty member in Statistics and Toxicology at Texas A&M University from 1992. She holds a Ph.D. in Statistics from Cornell University (1992), an M.A. in Statistics from Ohio State University (1987), and a B.S. in Mathematics from National Tsing-Hua University, Taiwan (1986). Her research focuses on longitudinal and functional data analysis, measurement error models, semiparametric methods, and applications in biological and medical fields, particularly genomics and metabolomics. Key contributions include methodologies for handling missing data, mixed effects models, and clustering techniques. Education: Ph.D. in Statistics, Cornell University (1992) M.A. in Statistics, Ohio State University (1987) B.S. in Mathematics, National Tsing-Hua University (1986) Dr. Wang’s honors include the College of Science Distinguished Alumni Award (2012), the Distinguished Achievement Award in Research (2003), and fellowships from the AAAS, ASA, and IMS. She has held leadership roles, including Co-editor of Statistica Sinica (2011–2014) and President of the International Chinese Statistical Association (2010). Her teaching includes courses such as Applied Statistics (STATS 500), Linear Models (STATS 600), and Special Topics in Applied Statistics (STATS 700). She has advised numerous students and contributed to research on cancer genomics, dietary interventions, and statistical methodology.
Shili Lin is a Professor of Statistics at The Ohio State University's Department of Statistics, within the College of Arts and Sciences. She joined the faculty in 1995 after serving as the Neyman Visiting Assistant Professor at the University of California, Berkeley. Her expertise spans statistical genomics, bioinformatics, high-dimensional data analysis, Bayesian statistics, and Monte Carlo methods. Lin collaborates extensively with medical researchers to address challenges in genomic data such as ultra-high dimensionality, complex dependencies, and sparsity, focusing on diseases like cancer, multiple sclerosis, tuberculosis, and diabetes. She has contributed to developing computational tools for analyzing chromatin interactions, methylation patterns, and metagenomic samples. Lin holds a PhD from the University of Washington (1993). Her professional roles include serving as an Associate Editor for Biometrics , Statistical Applications in Genetics and Molecular Biology , and Statistics in Biosciences , as well as an Editorial Board member for Genetic Epidemiology . She is a standing member of NIH's Biostatistical Methods and Research Design Study Section and has served on multiple NSF and NIH grant review panels. Additionally, she is President Elect of the Caucus for Women in Statistics and has been a member of the ASA Committee on AAAS representation for six years. Her research interests emphasize statistical methodologies tailored to genomic data, including model selection, epigenetic analysis, and integrative approaches for multi-omics data. Lin's work often combines theoretical advancements with practical applications, such as predicting relapse in immune-mediated disorders and improving imputation techniques for single-cell Hi-C analysis. She has pioneered software tools like TopKLists and GrammR to facilitate ranked list aggregation and metagenomic data analysis. Lin's scientific accolades include ASA Fellowship (2004), AAAS Fellowship (2009), and membership in the International Statistical Institute (2014). Her contributions to statistical genetics and epigenomics have been recognized through grants and editorial leadership roles. While her research group focuses on cutting-edge methods, no formal advisees or students are explicitly listed in the provided materials.
Dan Kowal is an Associate Professor in the Department of Statistics and Data Science at Cornell University, joining in 2024. His research focuses on Bayesian models for large/dependent data, mixed data modeling, and interpretable uncertainty quantification. Key areas include public health, environmental justice, epidemiology, and economics. He holds a PhD from Cornell University (2017) and previously served as an Assistant Professor at Rice University. Awards include the Blackwell-Rosenbluth Award (2021), Army Research Office Young Investigator Award (2020), and Lindley Prize Honorable Mention (2024). Notable grants include NSF funding for adaptive dependent data models (2022–2025) and Army Research Office support for Bayesian prediction methods (2020–2022). His work addresses racial inequities in statistical modeling and has been published in top journals like JASA and Bayesian Analysis. He advises multiple PhD students and develops R packages (e.g., SeBR, countSTAR) for Bayesian regression and data synthesis. Teaching roles include Bayesian Statistics at both undergraduate and graduate levels.
Byung-Jun Kim is an Assistant Professor in the Department of Mathematical Sciences at Michigan Technological University, where he joined as a tenure-track faculty in August 2020. His research focuses on statistical methodologies for complex observational data, particularly in nonparametric/semiparametric regression frameworks under high-dimensional and measurement error scenarios. PhD in Statistics from Virginia Tech (2020) BS/MS in Statistics from Chung-Ang University Research Expertise: Multivariate data analysis Covariance matrix estimation and graphical modeling Kernel regression in machine learning Statistical inference with measurement errors
Prabir Burman is a Professor in the Department of Statistics at the University of California, Davis, with a career spanning over three decades. His research focuses on nonparametric function estimation, model fitting/selection, image analysis, time series, and discrete data. Education: Ph.D. (1982) and Master of Statistics (1977) from University of California, Berkeley; Bachelor of Statistics (1976) from Indian Statistical Institute, Calcutta. His work bridges theoretical statistics and applied problems, including ecological studies (e.g., coyote parasites, mountain lion tracking), biomedical research (e.g., metabolic syndrome in bipolar patients), and time series forecasting. He has secured multiple NSF and NSA grants for projects on multivariate analysis, shape modeling, and covariance estimation. Recent publications highlight his expertise in predictive model fitting, stock return analysis, and stroke survivor studies. While not explicitly listing awards, his editorial roles (e.g., Journal of Multivariate Analysis) and collaborative grants underscore his academic leadership.
Thomas C. M. Lee is a Distinguished Professor of Statistics and Associate Dean of the Faculty in Mathematical and Physical Sciences at the University of California, Davis, within the College of Letters and Science. He holds a prominent position in the Department of Statistics and serves as a key academic leader at UC Davis. Education: B.App.Sc. (Math) from University of Technology, Sydney, Australia (1992) B.Sc. (Hons) (Math) with University Medal from University of Technology, Sydney, Australia (1993) Ph.D. from Macquarie University and CSIRO Mathematical and Information Sciences, Sydney, Australia (1997) Professor Lee's research spans multiple areas of statistics with a focus on developing innovative methodologies. His work particularly emphasizes nonparametric and semiparametric modeling , statistical learning , and statistical image and signal processing . He has made significant contributions to applying statistical methods across various scientific disciplines, demonstrating the versatility and power of statistical approaches in solving complex real-world problems. His research often bridges theoretical developments with practical applications, creating methodologies that are both mathematically sound and practically useful. Scientific Awards and Honors: Elected Fellow of the American Association for the Advancement of Science (AAAS, 2019) Elected Fellow of the American Statistical Association (ASA) Elected Fellow of the Institute of Mathematical Statistics (IMS) Elected Senior Member of the IEEE Professor Lee has held significant editorial roles including serving as Editor-in-Chief for the Journal of Computational and Graphical Statistics (2013-2015) and currently as Review Editor for the Journal of the American Statistical Association. From 2015 to 2018, he chaired the Department of Statistics at UC Davis. He has taught numerous statistics courses including STA 13 (Elementary Statistics), STA 131C (Introduction to Mathematical Statistics), STA 243 (Computational Statistics), and STA 401 (Statistical Consulting). His leadership extends beyond research to academic administration, where he has shaped statistics education and departmental direction at UC Davis.
David A. Stephens is a Professor in the Department of Mathematics and Statistics at McGill University, Montreal. He served as Chair of the Department from 2015 to 2019 and as Vice-Dean in the Faculty of Science from 2019 to 2025. His research focuses on Bayesian inference, biostatistics, causal inference, bioinformatics, and statistical genetics. He holds prestigious fellowships: International Statistical Institute (2015), American Statistical Association (2019), and Royal Society of Canada (2024). His work addresses challenges in epidemiology, HIV transmission dynamics, and clinical trial design. Key research themes include: Bayesian hierarchical modeling for infectious diseases (e.g., SARS-CoV-2, HIV) Causal inference in dynamic treatment regimes Survival analysis and censored data methods Statistical genomics and epigenetics His publications analyze public health trends, such as HIV transmission clusters in Quebec and SARS-CoV-2 seroprevalence in Canada. Methodologically, he develops novel techniques for time-series analysis, recruitment forecasting in clinical trials, and computational statistics. Notable contributions include: Advancing phylogenetic cluster inference in HIV studies Optimizing warfarin dosing strategies via SMART trials Modeling gut microbiota impacts on growth faltering in infants His academic leadership includes roles at McGill and prior experience at Imperial College London. His work bridges statistical theory and practical healthcare applications, emphasizing interdisciplinary collaboration.
Assoc Prof Xiang Liming is an Associate Professor in the Division of Mathematical Sciences at Nanyang Technological University (NTU), Singapore, serving as Assistant Chair (Students). She holds editorial roles at *Computational Statistics & Data Analysis* and *Statistics in Medicine*. With a PhD in Statistics (City University of Hong Kong, 2002), her research focuses on survival analysis, longitudinal data analysis, and biostatistical methods. Notable contributions include methodologies for semi-competing risks, interval-censored data, and mixture models. Her work bridges statistical theory with biomedical applications, addressing challenges in clinical trials and public health. Awards include the 2009 IIE Transactions Best Paper Award and the Outstanding Research Thesis Award (2002–2003, CityU). Education: PhD in Statistics, City University of Hong Kong (2002) Postdoctoral Research: Hong Kong University of Science and Technology (2002–2003) and CityU (2003–2006) Research Interests: Survival analysis methodologies, including frailty models, cure models, and quantile regression for censored data. She develops robust statistical approaches for clustered/longitudinal data, addressing missingness and overdispersion. Applications span biomedical research, epidemiology, and quality management. Grants & Collaborations: Her grants include work on robotic-assisted stroke rehabilitation (2021) and LNG cold energy utilization systems (2017–2019). She collaborates with clinical teams on trials involving upper limb neurorehabilitation technologies. Labs & Teams: Leads statistical method development for multi-center clinical trials, particularly in biostatistics and survival analysis frameworks. Active in NTU’s School of Physical & Mathematical Sciences research initiatives.
Oliver Linton is the Chair of the Faculty and Professor of Political Economy at the University of Cambridge's Faculty of Economics. He coordinates the Empirical Analysis of Financial Markets theme at the Janeway Institute and holds a position at Trinity College. His research primarily focuses on econometric theory and empirical finance , with applications in market microstructure, asset pricing, and volatility modeling. His research interests span: Development of novel econometric methods for high-dimensional and dynamic data Analysis of financial market behavior, including liquidity and trading patterns Applications in policy-relevant contexts such as quantitative easing and pandemic forecasting Linton's recent publications demonstrate a strong focus on: Advanced time-series methodologies (e.g., GARCH, nonparametric regression) Financial market microstructure and high-frequency trading Economic impact analysis of major events (e.g., Brexit, COVID-19) He has received prestigious awards including: Humboldt Research Award (2015) Thousand Talents Plan recognition from Renmin University of China (2016) Linton actively advises doctoral students, with current supervisees including Xinyi Su, Zhaocheng Zhang, and Kilian Bachmair. He secured significant funding such as the European Commission FP7 grant for Nonparametric and Semiparametric Methods in Economics and Finance (2011–2014).
