Bayesian Methods Professors and Supervisors in Canada

Alán Aspuru-Guzikمشاهده پروفایل

استاد

McGill UniversityCanada

Alán Aspuru-Guzik is Professor of Chemistry and Computer Science at the University of Toronto, holding the Canada 150 Research Chair in Theoretical Chemistry, Canada CIFAR AI Chair at the Vector Institute, and Google Industrial Research Chair in Quantum Computing. He directs the Acceleration Consortium, a strategic initiative uniting industry, government, and academia for pre-competitive research on the 'lab of the future'. His educational background includes a PhD in Physical Chemistry from UC Berkeley and BSc in Chemistry from the National Autonomous University of Mexico. Research spans quantum information, chemistry, and machine learning—pioneering quantum algorithms for chemical simulations, investigating quantum coherence in photosynthesis, and accelerating discovery of organic semiconductors, photovoltaics, and batteries. Current focus centers on autonomous chemical laboratories integrating AI and robotics. Recent publications (2024-2025) reveal dominant trends: self-driving laboratories for materials synthesis, quantum computing applications in molecular simulation, and generative AI for metal-organic frameworks and drug discovery. Key methodologies include reinforcement learning for experimental design and vision-language models for reaction mining. Major awards include: Chemical Engineering Medal (2023) John C. Polanyi Award (2023) MIT TR35 (2010) Sloan Research Fellowship (2009) Canada 150 Research Chair (2018) As Director of the Acceleration Consortium, he leads multi-institutional efforts in autonomous research systems, supported by Canadian government chairs and Google funding. His editorial role at Digital Discovery and co-founding of Zapata AI (quantum software) and Kebotix (robotic labs) demonstrate commitment to transforming scientific discovery through AI-driven automation.

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Rui Huمشاهده پروفایل

دانشیار

MacEwan UniversityCanada

Rui Hu is an Associate Professor at the Department of Mathematics and Statistics within MacEwan University's Faculty of Arts and Science. Their expertise lies in Experimental Design , Robustness in Statistics , and Spatial Statistics , with a strong focus on mathematical analysis and modeling. Education: PhD in Statistics (2016), with a thesis on Robust Designs for Model Discrimination and Prediction of Threshold Probability. Rui's research bridges fractional calculus, functional analysis, and epidemiological modeling, particularly through applications of partial differential equations and Sobolev/Besov space inequalities. Their recent work explores nonlocal operators and extensions via the Caffarelli–Silvestre framework. Rui has published extensively on topics including metapopulation disease models , robust experimental design , and mathematical biology , with a chronological focus from 2011 to 2025. Key trends include stability analysis in population dynamics and innovative applications of probability density functions in sound detection.

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Ying Zhangمشاهده پروفایل

استاد

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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Stephanie Clelandمشاهده پروفایل

استادیار

University of British ColumbiaCanada

Stephanie Cleland is an Assistant Professor and Legacy for Airway Health Chair in Promotion of Lung Health at Simon Fraser University's Faculty of Health Sciences . She also serves as a Research Scientist at the Vancouver Coastal Health Research Institute and an Affiliate Investigator with Environmental Health Services at BC Centre for Disease Control (BCCDC) . PhD and MSPH in Environmental Sciences and Engineering from University of North Carolina at Chapel Hill BS in Computer Science and Community Health from Tufts University Former Oak Ridge Institute for Science and Education (ORISE) research fellow at US EPA Her research integrates environmental epidemiology , exposure assessment , and health impact assessment to analyze climate change-related health risks including wildfire smoke and extreme heat . She employs advanced statistical methods and novel datasets to: Identify harmful exposure levels/durations Determine at-risk population characteristics Evaluate mitigation strategies Develop spatiotemporal exposure models Quantify community resilience factors Assess cognitive and cardiovascular impacts Current research emphasizes machine learning applications for exposure assessment and environmental health communication strategies. Her work has been published in leading environmental health journals focusing on: PM2.5 and ozone health effects Climate change health modeling Wildfire smoke epidemiology Heat-related health impacts Indoor air quality interventions Geospatial exposure analysis

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Eric Cokerمشاهده پروفایل

استاد مدعو

University of British ColumbiaCanada

Dr. Eric Coker is a senior scientist with the Environmental Health Services program at the British Columbia Centre for Disease Control and an adjunct professor in the School of Population and Public Health at the University of British Columbia. His work integrates environmental epidemiology, exposure science, and maternal-child health to understand how early-life chemical exposures affect lifelong health and to illuminate environmental drivers of health disparities. Education & Training PhD, Public Health – Oregon State University MS, Global Health Science – University of California, San Francisco MS, Occupational & Environmental Health Exposure Science – University of Washington Post-doctoral training, Maternal Child Health & Global Health – University of California, Berkeley Research Focus Dr. Coker’s research centres on children’s environmental health, especially prenatal and early-childhood exposures to complex chemical mixtures such as metals, PFAS, bisphenols, phthalates and air pollutants. By leveraging advanced epidemiologic and statistical methods—including machine learning and Bayesian modelling—he examines how these exposures contribute to neurodevelopmental impairment, growth restriction, respiratory disease, and later-life mortality. A unifying theme is the elucidation of social and environmental inequalities that shape disproportionate risk in Indigenous, racialized and low-income communities. Recent work has expanded to climate-health intersections, investigating co-exposures to wildfire smoke and extreme heat and developing risk-communication strategies to protect vulnerable populations. Additional studies employ wastewater-based epidemiology for community-level COVID-19 surveillance and explore occupational respiratory risks among agricultural workers. Publications Across more than 30 peer-reviewed articles (2014-2025), Dr. Coker’s scholarship spans five interconnected domains: (1) maternal–fetal and child health effects of metals and endocrine-disrupting chemicals; (2) air pollution and respiratory outcomes in both children and adults; (3) epigenetic mechanisms linking early-life exposures to later disease; (4) climate change, wildfire smoke and heat-health; and (5) novel methodological advances in mixture epidemiology and wastewater surveillance. Current Affiliations & Labs Dr. Coker leads and collaborates on studies within the BCCDC Environmental Health Services, working closely with multi-disciplinary teams that include toxicologists, data scientists, Indigenous health researchers, and public health practitioners. He maintains adjunct faculty status at UBC, where he mentors graduate students and contributes to curriculum on environmental health and epidemiologic methods.

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Mike Irvineمشاهده پروفایل

استاد مدعو

University of British ColumbiaCanada

Dr. Mike Irvine serves as a senior scientist in Data & Analytics Services at the BC Centre for Disease Control (BCCDC) and holds adjunct professor appointments in the Faculty of Science at the University of British Columbia and the Faculty of Health Sciences at Simon Fraser University. His interdisciplinary work bridges mathematical modeling, biostatistics, and machine learning to address critical public health challenges across multiple disease domains. Dr. Irvine earned his MSci in Mathematics from University College London, followed by a PhD in Mathematical Ecology and Complexity Science and an MSc in Complexity Sciences from the University of Warwick. His postdoctoral training included evaluating neglected tropical disease elimination campaigns with the Bill & Melinda Gates Foundation modeling consortium. He received a CIHR Health Systems Impact Fellowship to collaborate with BCCDC and the BC Ministry of Mental Health & Addictions on overdose response evaluation. His research focuses on Bayesian evidence synthesis methods that integrate diverse data sources for disease epidemiology and public health applications. This methodological expertise supports intervention evaluations across vector-borne diseases, sexually transmitted and blood-borne infections, respiratory infections, and opioid-related overdoses. His publications demonstrate consistent methodological innovation applied to pressing public health issues. Recent publication trends reveal strong emphasis on pandemic response (particularly COVID-19 transmission dynamics and vaccine safety) and the opioid crisis (overdose surveillance and naloxone distribution modeling), reflecting his responsiveness to evolving public health emergencies through quantitative approaches. CIHR Health Systems Impact Fellowship Dr. Irvine's collaborations with BCCDC and provincial health authorities translate complex modeling insights into actionable public health strategies. His work with the BC Ministry of Mental Health & Addictions on overdose response evaluation exemplifies his commitment to evidence-based policy development through rigorous quantitative analysis. At BCCDC, he leads analytical efforts in Data & Analytics Services, applying advanced statistical methods to public health surveillance data across multiple disease programs, strengthening British Columbia's capacity for data-driven decision-making in public health practice.

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Matthew Joseph Kusnerمشاهده پروفایل

دانشیار

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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Michel C. Desmaraisمشاهده پروفایل

استاد

Polytechnic School of MontrealCanada

Michel C. Desmarais is a Full Professor in the Department of Computer Engineering and Software Engineering at Polytechnique Montréal, where he has been faculty since 2002. With a PhD in Psychology from Université de Montréal, his research bridges artificial intelligence, educational technology, and human-computer interaction. He holds affiliations with IVADO and LAMA-WeST research groups, and has held visiting positions at Sorbonne University, Eindhoven Technical University, and other European institutions. His research focuses on three interconnected pillars: 1) Cognitive modeling and educational data mining , developing algorithms for student knowledge assessment and adaptive learning systems; 2) AI-driven educational tools , including automated grading systems and peer instruction platforms; and 3) Recommendation systems and user modeling , particularly for personalized learning interfaces. His work consistently applies machine learning to solve practical challenges in technology-enhanced education. Analysis of his 150+ publications reveals strong trends in educational NLP (sentence similarity for short-answer grading), generative AI (LLM-generated code validation), and Bayesian modeling (Q-matrix refinement). Recent work increasingly focuses on transformer architectures and real-world educational datasets. He maintains an active supervision record, having graduated 35+ graduate students. Current PhD candidates work on NLP for educational applications (Bakhtiari, Kamdem) and AI for engineering (Wang). His teaching covers user interface design, recommender systems, and intelligent interfaces. Professional service includes editorial leadership (JEDM journal), conference co-chairing (UMAP 2017, EDM founding), and grant review panels for NSERC, MITACS, and EU programs. Industry experience includes prior roles as R&D Director at MVM Inc. and researcher at Montreal Computer Research Center.

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Guillaume de Lafontaineمشاهده پروفایل

استاد

University of Quebec in Abitibi-TémiscamingueCanada

Guillaume de Lafontaine is a Professor at the University of Quebec in Rimouski (UQAR) and holds the Canada Research Chair in Integrative Biology of Northern Flora. His work focuses on the ecological, biogeographical, and evolutionary responses of arcto-boreal flora to historical and contemporary climate change. He is affiliated with the Department of Biology, Chemistry and Geography at UQAR and is a regular member of the Centre d'Étude de la Forêt (CEF), Collaborating member of the Centre for Nordic Studies (CEN), Associate member of the Quebec Biodiversity Science Center (CSBQ), Member of the BORÉAS northern environments research group, and Member of the Quebec Biodiversity Institute (IQBIO). De Lafontaine's research employs an integrative biology approach combining field ecology, population genetics/genomics, modeling, and paleoecology to assess ecosystem responses to climate change and disturbances. His work primarily targets Quebec/Canadian biogeographic models but also extends to Alaska, Europe, Asia, and cold climates of tropical latitudes through collaborations. His research program is structured around two main axes: threats of extinction at the warm margin and expansion capacity at the northern limit of boreal species. Analysis of his recent publications reveals a strong focus on climate change impacts on boreal ecosystems, with particular attention to species responses at range margins, historical biogeography through paleoecological reconstructions, and genetic adaptation to changing environments. His work spans multiple spatial and temporal scales, from contemporary ecological dynamics to Quaternary climate change impacts. As an academic mentor, Professor de Lafontaine has supervised numerous doctoral and master's students, postdoctoral researchers, and interns, contributing significantly to the training of the next generation of forest scientists. His research has been published extensively in high-impact journals across ecology, evolution, biogeography, and paleoecology.

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Zeinab Abboudمشاهده پروفایل

پژوهشگر

HEC MontréalCanada

Zeinab Abboud is a Senior Academic Member and PhD researcher at Polytechnique Montréal affiliated with Mila (Québec Artificial Intelligence Institute). Her work focuses on Bayesian neural networks, computer vision, and AI applications in healthcare, particularly medical image analysis. PhD in Artificial Intelligence from Polytechnique Montréal Supervised by Hervé Lombaert Her research addresses computational efficiency in medical image analysis through sparse Bayesian networks, combining deterministic and Bayesian parameter representations to reduce training complexity while maintaining predictive accuracy. She has contributed to multi-label classification (ChestMNIST) and segmentation tasks (ISIC, LIDC-IDRI) in healthcare imaging. The publication Sparse Bayesian Networks: Efficient Uncertainty Quantification in Medical Image Analysis (2024) demonstrates her work on reducing Bayesian parameter usage by 95% compared to traditional methods, advancing scalable uncertainty estimation in AI models for medical applications.

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