Nonparametric Bayesian Inference Professors and Supervisors in Canada

James Hugh McVittieView profile

Assistant Professor

University of ReginaCanada

James Hugh McVittie serves as an Assistant Professor in the Department of Mathematics and Statistics within the Faculty of Science at the University of Regina. His academic profile features active research engagement with contact details including email (james.mcvittie@uregina.ca) and office phone (306-585-5080), though no courses are scheduled for Spring/Summer 2025. McVittie's research program centers on Survival Analysis and Biostatistics, with specialized expertise in Statistical Inference for Combined Cohort Methodologies and Measurement Error. His methodological work addresses partially observed data structures, particularly focusing on right-censored and length-biased failure time data. Applications span health studies (including Parkinson's disease research using the Canadian Longitudinal Study on Aging) and modeling sports career durations, demonstrating translational impact across medical and social domains. Analysis of his 2022-2025 publications reveals three dominant research thrusts: (1) Advanced survival modeling for combined cohort data (incident/prevalent cohorts), (2) Bayesian nonparametric estimation techniques for censored data, and (3) Extreme value theory applications in environmental statistics with missing observations. His work consistently addresses methodological gaps in sample size determination, hazard modeling, and survival function estimation under complex data constraints. Scientific Awards No scientific awards were documented in the provided sources Advising and grant activities remain unreported in the available materials, with no mention of student supervision or funded research projects. Laboratory infrastructure and research team compositions were not specified in the institutional profiles or publication records.

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J. E. PaguyoView profile

Research Fellow

McMaster UniversityCanada

J. E. Paguyo is a Britton Postdoctoral Fellow in the Department of Mathematics & Statistics at McMaster University, mentored by Shui Feng. He holds a PhD in Mathematics from the University of Southern California (supervised by Jason Fulman) and a BS in Mathematics from the University of California, Davis. His research focuses on probability and combinatorics, with a particular emphasis on limit theorems and random discrete structures. He describes himself as a 'West Coast Coin Flipper' and has an Erdős number of 3. His research interests span the intersection of probability and combinatorics, including topics like random parking functions, hierarchical models, and stochastic processes. His recent work applies Stein's method to analyze limit theorems across various random structures. Notable themes in his articles include asymptotic behavior, cycle structures in discrete systems, and stochastic convergence in hierarchical models. Paguyo's academic journey includes postdoctoral research at McMaster and prior education on the West Coast of the U.S. He is reachable at paguyoj@mcmaster.ca and typically resides in Hamilton Hall 311.

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Boris LevitView profile

Professor

Queen's University at KingstonCanada

Boris Levit is a Professor in the Department of Mathematics and Statistics at Queen's University, located in Kingston, Ontario. He holds a Ph.D. from the Institute of Information Transmission in Moscow and a Dr.Sc. from Vilnius University. His research focuses on non-parametric estimation, semi-parametric models, optimal design in non-parametric regression, and optimal interpolation methods. Levit has made significant contributions to statistical theory, particularly in applying differential geometry and partial differential equations to statistical problems. Levit's academic journey includes positions at Moscow State University, the University of Utrecht in the Netherlands, and Queen's University. His work emphasizes modern non-asymptotic approaches to statistical estimation, utilizing Jacobi elliptic functions and addressing challenges in moderate or small sample sizes. He is currently recruiting graduate students. His research has been recognized through awards, including the Dr.Sc. from Vilnius University for integrating differential geometry into statistical research. His publications span over five decades, addressing topics like minimax estimation, spline interpolation, and adaptive filtering. He teaches courses in statistical inference and nonparametric methods.

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Qinglong TianView profile

Assistant Professor

University of WaterlooCanada

Qinglong Tian is an Assistant Professor in the Department of Statistics at the University of Waterloo. His research focuses on statistical methodology development with applications in reliability engineering, machine learning, and predictive modeling. He specializes in areas such as nonparametric estimation, domain adaptation, privacy-preserving data analysis, and Bayesian reliability analysis. His work bridges theoretical statistics with practical challenges in engineering and healthcare sectors. Key research interests include semi-supervised learning, ROC curve analysis, label shift adaptation, and prediction interval methods. He has contributed novel techniques for handling imperfect reference standards, continuous target shift, and heterogeneous reliability data. His recent publications address challenges in post-randomization data privacy and model-agnostic parameter estimation. Qinglong's methodological innovations are applied to real-world problems like pipeline life assessment, diagnostic testing accuracy, and failure prediction in complex systems. His interdisciplinary approach combines rigorous statistical theory with computational methods to solve modern data science challenges.

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Jean-François AngersView profile

Professor

University of MontrealCanada

Dr. Jean-François Angers is a Full Professor in the Department of Mathematics and Statistics at the University of Montreal. With a Ph.D. in Statistics from Purdue University (1987) and an M.Sc. and B.Sc. in Applied Mathematics from the University of Sherbrooke (1984, 1982), his academic foundation is robust in statistical theory and its applications. P.Stat. (Statistical Society of Canada, 2005) His research spans Bayesian statistics, climate change impact analysis, and interdisciplinary applications in hydrology, epidemiology, and actuarial science. Key contributions include Bayesian hazard function estimation using wavelets, non-stationary climate modeling, and statistical methods for zero-modified continuous data. The 15 most recent publications highlight his work in environmental statistics (air pollution effects), climate modeling, survival analysis, and actuarial risk assessment. His methodological innovations in Bayesian hierarchical models, wavelet estimation, and hidden Markov random fields have advanced statistical approaches in ecological and medical research. Dr. Angers has supervised over 40 graduate and undergraduate students, including Jonathan Jalbert (non-stationary climate modeling), Alain Desgagné (Bayesian econometrics), and James Merleau (hydrograph synchronization). His collaborations extend to institutions like Hydro-Québec, IREQ, and INRS-ETE.

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Paul MarriottView profile

Professor

University of WaterlooCanada

Paul Marriott is a Professor in the Department of Statistics and Actuarial Science at the University of Waterloo. His research focuses on integrating geometric principles, particularly differential and convex geometry, into statistical methodologies, with a recent emphasis on mixture models and information geometry. He has published extensively across diverse journals such as Biometrika, Annals of Statistics, and Psychological Medicine, bridging theoretical and applied statistics. Education: PhD, University of Warwick (1989) MA, University of Oxford (1984) Research Trends: His work explores geometric frameworks for statistical inference, mixture model parameterization, and robustness analysis. Recent publications highlight causal modeling, neural spike train analysis, and high-dimensional data applications. Contact: Office: Mathematics & Computer Building (M3) 4204, Phone: 519-888-4911 x35545, Email: pmarriot@math.uwaterloo.ca

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Ying ZhangView profile

Professor

Acadia UniversityCanada

Ying Zhang is a Professor in the Department of Mathematics and Statistics at Acadia University, maintaining an office in Huggins Science Hall, Room 151. She earned her BSc from Shandong Normal University and advanced degrees (MA, MSc, PhD) from Western University, complemented by P.Stat. certification (Certificate #78) from the Statistical Society of Canada. Her educational background includes: BSc from Shandong Normal University MA, MSc, PhD from Western University Professor Zhang's research centers on Time Series Analysis and Applied Statistics , extending to Statistical Computing, Symbolic Algebra Computing, and Statistical Consulting in Biostatistics, Survey Design, and Research Methodology. Her work demonstrates significant applications in environmental science (water resources trend analysis), health sciences (drug safety and utilization studies), and ecological modeling (wildlife population dynamics), with methodological innovations in nonparametric testing and hierarchical modeling. Analysis of her 2013-2018 publications reveals a dominant focus on developing novel time series methodologies for environmental and health contexts, particularly seasonal trend detection, medication utilization patterns, and ecological data analysis. Her work consistently bridges theoretical statistics with practical applications across disciplines. She actively contributes through the Statistical Consulting Centre and the CANSSI Maritime Statistical and Health Sciences Collaborating Centre , holding P.Stat. designation from the Statistical Society of Canada. While her collaborative publications indicate interdisciplinary engagement, specific details of grant funding and student advising are not documented in available sources.

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Matthew Joseph KusnerView profile

Associate Professor

Polytechnic School of MontrealCanada

Matt J. Kusner is an Associate Professor in the Department of Computer Engineering and Software Engineering at Polytechnique Montréal. He holds additional affiliations as a Senior Academic Member at Mila - Quebec Artificial Intelligence Institute and as a Member at the Institute for Data Valorization (IVADO). Previously, he served as an Associate Professor at University College London and the University of Oxford. Professor Kusner's research spans machine learning, with particular focus on causal inference, fairness in algorithms, representation learning, and domain adaptation. His work bridges theoretical foundations with practical applications in natural language processing, algorithmic fairness, and scientific computing domains including plasma physics and renewable energy systems. His research interests include Pattern Analysis and Artificial Intelligence, Algorithms, and Learning and Inference Theories. His publication record shows consistent output across top machine learning venues including NeurIPS, ICML, and ICLR, with recent work trending toward causal machine learning methods and applications in scientific domains. Professor Kusner has 42 publications spanning from 2014 to 2025, demonstrating sustained research productivity. Turner Dissertation Award for best doctoral dissertation in Computer Science & Engineering Professor Kusner received his PhD in Computer Science from Washington University in St. Louis in 2016 under Kilian Weinberger. His work has been featured in major media outlets including The Guardian, Forbes, and the Harvard Business Review, and he has presented at prestigious institutions including the Federal Reserve Banks, Cambridge Centre for Mathematical Sciences, and the Royal Society.

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Junxi ZhangView profile

Assistant Professor

Concordia UniversityCanada

Junxi Zhang is an Assistant Professor in the Department of Mathematics and Statistics at Concordia University's Faculty of Arts and Science. He joined Concordia University in August 2024 after completing a postdoctoral fellowship at the University of Alberta, where he continues to maintain academic connections. His academic journey includes: PhD in Statistics from the University of Alberta (2023) Master in Mathematics from the University of Kansas (2017) BS in Statistics from Huazhong University of Science and Technology Dr. Zhang's research spans several interconnected areas at the forefront of statistical methodology and machine learning: Bayesian nonparametric models: Conducting asymptotic analysis of random measures, studying consistency properties, and developing dependent and hierarchical models with practical applications Fairness in machine learning: Creating frameworks for fair prediction models, developing metrics for measuring fairness, and applying these approaches to health science domains where algorithmic bias can have serious consequences Reinforcement learning: Addressing fundamental challenges in optimal control within reinforcement learning frameworks Emerging interests: Expanding into conformal inference, causal inference, and time series analysis using machine learning tools His publication record demonstrates a productive trajectory with research appearing in top venues including NeurIPS and Bayesian Analysis. His work bridges theoretical statistical foundations with practical applications, particularly in healthcare and social domains where fairness considerations are increasingly critical. Dr. Zhang has received recognition through publications in prestigious venues and is actively contributing to the academic community through conference presentations and collaborative research. His work on fairness in AI addresses one of the most pressing challenges in contemporary machine learning. As an educator, Dr. Zhang teaches courses including Data Science Lab, Time Series and Forecasting, and specialized topics in statistics with focuses on Bayesian modeling and reinforcement learning. He is currently seeking graduate students for the Mathematics and Statistics MA, MSc, and PhD programs at Concordia University. His research group provides students with opportunities to engage in meaningful research that addresses both theoretical challenges in statistics and practical applications in machine learning, with particular emphasis on developing fair and trustworthy AI systems.

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