Bayesian Methods Professors and Supervisors in Canada | ApplySharif

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استاد راهنما، استاد دانشگاه یا پژوهشگر مناسب برای مسیر پژوهشی‌تان را پیدا کنید و با او ارتباط بگیرید.

نمایش ۳۱-۴۵ از ۴۶۰ استاد راهنما

اطلاعات این پروفایل‌ها از صفحات عمومی دانشگاه‌ها جمع‌آوری شده و ممکن است به‌روز نباشد.

Mahmoud El-Sakkaمشاهده پروفایل

دانشیار

Western UniversityCanada

Mahmoud El-Sakka is an Associate Professor at the Department of Computer Science, University of Western Ontario since 1999. Previously, he was a faculty member at the University of Waterloo (1997–1999). He holds a B.Sc. and M.Sc. from Alexandria University (Egypt) and a Ph.D. in Systems Design Engineering from the University of Waterloo. His research focuses on medical imaging, image processing, and computer-aided diagnostics. He has served as Chair of the graduate program (2002–2007) and undergraduate program (2017–present) in Computer Science at Western Ontario. El-Sakka is a Senior Member of the IEEE and a licensed Professional Engineer in Ontario. His work spans grants from NSERC, internal university funding, and industry collaborations. Major research areas include image compression, segmentation, and medical applications like vascular analysis and echocardiography. He has led over 20 funded projects since 1999, emphasizing interdisciplinary approaches in healthcare technology. Academic contributions include advisory roles in summer programs, thesis evaluations, and conference participation. His service includes roles as Pro-Chancellor at convocations and involvement in equipment purchasing committees. Collaborations include consulting with NCR Canada and VRP Web Technology.

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Samuel W.K. Wongمشاهده پروفایل

دانشیار

University of WaterlooCanada

Samuel W.K. Wong is an Associate Professor in the Department of Statistics and Actuarial Science at the University of Waterloo. He holds a Ph.D. in Statistics from Harvard University (2013) under Prof. Samuel Kou. His research focuses on statistical methodology for complex data science challenges in protein structure modeling, dynamic systems inference, and reliability engineering of wood-based products. He has held academic positions at the University of Florida (2013–2018) and has been at Waterloo since 2018. His research interests include Bayesian computation, statistical inference for dynamic systems, and spatial-temporal data analysis. Notable contributions include the development of manifold-constrained Gaussian processes (MAGI package) and sequential Monte Carlo methods for protein folding studies. He has advised over 15 graduate students and researchers, many of whom are now in academic or industry roles worldwide. Wong has received teaching distinctions at Harvard and holds awards including the Nash Medal (2008) for academic excellence. His work bridges computational statistics with applications in bioinformatics, structural engineering, and environmental science. He has published extensively in top-tier journals like Journal of Computational and Graphical Statistics and Biometrics , and collaborates with wood scientists to improve real-time lumber quality assessment using laser imaging data. His teaching portfolio includes courses on probability theory, statistical inference, and spatial data analysis at both undergraduate and graduate levels. Beyond academia, he maintains an active passion for classical piano performance, having performed recitals combining music with his statistical research interests.

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Ben Bloem-Reddyمشاهده پروفایل

استادیار

University of British ColumbiaCanada

Ben Bloem-Reddy is an Assistant Professor in the Department of Statistics at the University of British Columbia (UBC), Vancouver Campus. His research focuses on statistical theory and applications in machine learning, particularly in causal inference, Bayesian methods, neural networks, and probabilistic models. He advises current students Quanhan (Johnny) Xi, Kenny Chiu, and Gian Carlo Diluvi. His work bridges foundational statistical theory with practical machine learning challenges, including causal discovery, model identifiability, and uncertainty quantification. Recent research explores topics such as latent variable models, generative processes, and symmetry in data and algorithms. His contributions span interdisciplinary areas like particle physics applications and information theory-based compression techniques. Ben’s research trends emphasize advancing theoretical guarantees for modern machine learning systems while addressing real-world problems. His publications frequently intersect with algebraic topology (e.g., cocycles in causal inference) and nonparametric methods. He maintains an active lab within the Department of Statistics, fostering collaborations across UBC’s academic ecosystem. No scientific awards are explicitly listed in the provided information. His advising and grant activities focus on statistical methodology development, as evidenced by his student supervision and published work. His office is located in ESB 3168, and he can be reached at benbr@stat.ubc.ca.

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Salem Lahlouمشاهده پروفایل

استادیار

University of MontrealCanada

Salem Lahlou is an Assistant Professor in the Machine Learning Department at the Mohamed bin Zayed University of Artificial Intelligence (MBZUAI), having joined in September 2024. He previously served as a Senior Researcher at the Technology Innovation Institute (TII) in 2024. His academic background includes a PhD from Mila and Université de Montréal (UdeM) under Yoshua Bengio (2023), with prior studies in applied mathematics at École Polytechnique and statistical learning at École Normale Supérieure Paris-Saclay. His research focuses on developing more capable and reliable AI systems through three interconnected pillars: Novel Method Development : Core contributions to Generative Flow Networks (GFlowNets), uncertainty estimation techniques (DEUP), and curriculum learning frameworks Large Language Model Advancement : Enhancing reasoning capabilities and alignment through preference optimization and trace-based learning Community Tooling : Creation of torchgfn library for GFlowNets and benchmarks including BabyAI, FinChain, and LLM-BabyBench Core research areas span Machine Learning, GFlowNets, Uncertainty Estimation, LLM Reasoning, Reinforcement Learning, and AI for Science. Recent publications (2023-2025) demonstrate strong emphasis on GFlowNet theory/improvements (8+ papers), LLM reasoning evaluation (FinChain, LLM-BabyBench), uncertainty quantification, and societal AI impacts. Key application domains include mathematical reasoning, financial systems, privacy preservation, and cognitive science. He currently advises graduate students including Junyi (privacy risks in SNNs) and Abhijith (LLM reasoning). His group collaborates with MBZUAI faculty (Nils Lukas, Alham Fikri, Mingming Gong, Martin Takac) and industry partners on projects involving Conversational AI, Personalization, and Affective AI.

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James Alexandre Gouletمشاهده پروفایل

استاد

Polytechnic School of MontrealCanada

James Alexandre Goulet is a Professor in the Department of Civil, Geological and Mining Engineering at Polytechnique Montréal. His research focuses on Machine Learning Methods for Civil Engineering applications such as structural health monitoring (SHM) and infrastructure maintenance planning. He leads the Canari project for online change point detection in SHM and contributes to open-source libraries like cuTAGI for Bayesian neural networks. Affiliations : Chair in Machine Learning for Infrastructure Monitoring at Polytechnique Montréal, IVADO Institute member, and GRS (Structural Engineering Research Group) member Expertise : Building engineering, structural safety, applied probability, learning theories Recent research trends include Bayesian state-space models, LSTM neural network integration for infrastructure forecasting, and uncertainty quantification in SHM systems. His work emphasizes probabilistic methods and analytical inference over black-box approaches. Teaching includes courses on structural reliability and probabilistic data analysis for civil engineers. He supervises graduate students in topics ranging from damage detection algorithms to stochastic deterioration modeling of infrastructures.

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Mark Crowleyمشاهده پروفایل

دانشیار

University of WaterlooCanada

Mark Crowley is an Associate Professor in the Department of Electrical and Computer Engineering at the University of Waterloo , with a cross-appointment in the Cheriton School of Computer Science . He is actively involved in the Waterloo Artificial Intelligence Institute (WAII) , the Waterloo Institute for Complexity and Innovation (WICI) , and serves as National Secretary for the Canadian Artificial Intelligence Association (CAIAC) , coordinating the Canadian Conference on AI . Research interests span the theoretical and applied aspects of Reinforcement Learning , Deep Learning , Manifold Learning , and Ensemble Methods . His work addresses challenges in domains with spatial dynamics, multi-agent systems, and uncertainty, particularly in Computational Sustainability (forest fire management, sustainable forestry), Autonomous Driving , Medical Imaging , and Material Design . Recent research focuses on integrating causal modeling with generative representation learning to improve out-of-distribution robustness in motion forecasting applications. Key publications include foundational work on ChemGymRL environments for safe chemical process reinforcement learning, Generative Causal Representation Learning for robust forecasting, and collaborative work on multi-advisor reinforcement learning in multi-agent settings. He co-authored a textbook Elements of Dimensionality Reduction and Manifold Learning (Springer, 2023) with Prof. Ali Ghodsi and Prof. Fakhri Karray. Teaching includes graduate and undergraduate courses in Algorithm Design , Computational Intelligence , Reinforcement Learning , and Data Modeling at the University of Waterloo since 2018. His research group has produced several notable graduates including Benyamin Ghojogh (2021), who continued as a postdoc until 2022.

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Carolina Osorioمشاهده پروفایل

استاد

HEC MontréalCanada

Carolina Osorio is a Professor at HEC Montréal, holding the Scale AI Research Chair in Artificial Intelligence for Urban Mobility and Logistics. She is affiliated with the Department of Decision Sciences and is a member of the Group for Research in Decision Analysis (GERAD) and the Interuniversity Research Centre on Enterprise Networks, Logistics and Transportation (CIRRELT). Her research focuses on transportation optimization, urban mobility, and data-driven simulation-based methods. She has been recognized among the world’s most influential researchers in 2023 and 2024. Education: Ph.D. in Mathematics, École Polytechnique Fédérale de Lausanne (EPFL) M.Sc. in Statistics, University College London (UCL) Bachelor’s in Engineering, École nationale supérieure d'informatique et de mathématiques appliquées de Grenoble (ENSIMAG) Research Interests: Her work emphasizes scalable transportation modeling, simulation-based optimization, and AI applications for urban logistics. She develops methods for large-scale network analysis, traffic demand estimation, and sustainable urban mobility solutions. Key areas include traffic signal optimization, car-sharing service design, and high-dimensional stochastic systems. Publications: Recent articles highlight advancements in scalable traffic demand estimation, Bayesian optimization for transportation systems, and simulation-based toll optimization. Her work addresses challenges in global highway networks, urban congestion dynamics, and multi-city calibration. Awards: Scale AI Research Chair (Artificial Intelligence for Urban Mobility and Logistics) Recognition as a world-leading researcher in transportation science Advising & Grants: Osorio collaborates on projects funded by Scale AI and leads research initiatives through GERAD and CIRRELT. Her supervision activities include teaching courses such as Decision Analysis and Sample Efficient Optimization at HEC Montréal. Labs & Teams: She contributes to interdisciplinary teams at GERAD and CIRRELT, focusing on integrating advanced analytics into urban transportation systems.

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Patrick Brownمشاهده پروفایل

دانشیار

University of TorontoCanada

Patrick Brown is an Associate Professor at the University of Toronto , affiliated with the Department of Statistical Sciences and cross-appointed to the Centre for Global Health Research and St. Michael's Hospital . His research focuses on spatio-temporal data modeling , Bayesian inference , and non-parametric methods for spatial epidemiology and environmental sciences. Fields of Interest : Spatial Statistics, Cancer Statistics, Statistical Software Education : PhD from University of Lancaster His methodological work encompasses Bayesian inference for non-Gaussian spatial data, Gaussian Markov random fields, and computational techniques like INLA and MRA. Applied research themes include disease mapping, environmental risk assessment, and public health surveillance using real-world data sources such as electronic health records and wastewater monitoring . He has developed key R packages (mapmisc, geostatsp, diseasemapping) supporting spatial statistical applications. Current collaborative projects span diverse fields: Ultra-diffuse galaxy detection with astrophysical applications Multi-pollutant mortality studies in Canadian cities SARS-CoV-2 seropositivity tracking Homelessness population estimation using EHR Geospatial cancer risk tools for Nova Scotia His work bridges statistical innovation with global health challenges , emphasizing computationally efficient solutions for large-scale spatiotemporal datasets.

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Richard Zemelمشاهده پروفایل

استاد

University of TorontoCanada

Richard Zemel is a Professor in the Department of Computer Science at the University of Toronto, where he has been since 2000. He holds an Industrial Research Chair in Machine Learning and co-founded the Vector Institute for Artificial Intelligence. His research focuses on machine learning, including unsupervised learning, deep learning, and ethical AI, with contributions to probabilistic models, fairness, and representation learning. Zemel has developed influential systems like the Toronto Paper Matching System and holds awards such as the NVIDIA Pioneers of AI Award and multiple NSERC grants. Education: B.Sc. in History & Science from Harvard University (1984), Ph.D. in Computer Science from the University of Toronto (1993). Postdoctoral work at the Salk Institute and Carnegie Mellon University. Research Interests: Machine Learning (unsupervised/deep learning), probabilistic models, fairness in algorithms, computer vision, natural language processing. He emphasizes ethical AI and practical applications like recommendation systems and causal inference. Awards & Affiliations: Fellow of CIFAR, member of the Neural Information Processing Society (NIPS) Executive Board, and advisor to the Creative Destruction Lab. His work is funded by NSERC, CIFAR, Google, Microsoft, and DARPA. Grants & Labs: Active in grants supporting machine learning research, including projects on fairness and invariant learning. Collaborates with industry partners and leads teams at the University of Toronto and Vector Institute.

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Radu Craiuمشاهده پروفایل

استاد

University of TorontoCanada

Radu Craiu is a Professor in the Department of Statistical Sciences at the University of Toronto. Born in Bucharest, Romania, he earned his PhD in Statistics from the University of Chicago in 2001 under the supervision of Xiao-Li Meng. He joined the University of Toronto as an Assistant Professor, was promoted to Associate Professor in 2006, and to Full Professor in 2013. He served as Department Chair from 2018–2022 and 2023–2024. Education: PhD in Statistics (2001), University of Chicago MS in Mathematics (1996), University of Bucharest BS in Mathematics (1995), University of Bucharest His research focuses on computational methods in statistics, particularly Markov chain Monte Carlo (MCMC) algorithms, Bayesian inference, copula models, model selection, and statistical genetics. He has collaborated on interdisciplinary projects in genetics, ecology, astronomy, and demography, emphasizing flexible statistical modeling and efficient computational techniques. Radu Craiu has received prestigious awards, including the CRM-SSC Prize (2016), CJS Best Paper Award (2019), and fellowships from the American Statistical Association (2022), Institute of Mathematical Statistics (2020), and International Statistical Institute (2015). He actively contributes to editorial roles for journals like the IMS Bulletin and Harvard Data Science Review .

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Christoph Freiمشاهده پروفایل

استاد

University of AlbertaCanada

Christoph Frei is a Professor and Chair of the Department of Mathematical and Statistical Sciences at the University of Alberta. He holds a PhD in mathematical finance from ETH Zurich and previously worked as a researcher at École Polytechnique in Paris. His research focuses on quantitative finance, risk management, and mathematical economics, with applications to algorithmic trading, credit risk, and digital currencies. Education: PhD in Mathematical Finance, ETH Zurich Postdoctoral Researcher, École Polytechnique (Paris) Bachelor/Master in Mathematics, ETH Zurich His work bridges academia and industry through collaborations with institutions like ATB Financial, Canadian Western Bank, and the Federal Reserve System. Key research interests include over-the-counter markets, financial regulation, and machine learning applications in risk prediction. Frei has received notable recognition, including the Best Paper in Asset Pricing Award (2019). Current industry partnerships include AI-driven customer risk prediction projects with ATB Financial and credit risk analysis with Canadian Western Bank. He actively contributes to professional organizations like PRMIA Edmonton and advises on financial technology innovation. Research grants come from NSERC, SSHRC, and Mitacs. His consulting roles have spanned risk modeling at UBS and Credit Suisse, emphasizing practical applications of theoretical frameworks.

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Jonathan Cannonمشاهده پروفایل

استادیار

McMaster UniversityCanada

Jonathan Cannon is an Assistant Professor in the Department of Psychology, Neuroscience & Behaviour at McMaster University's Faculty of Science. His research focuses on timing and rhythm in perception and action, with particular interest in timing-related neural dynamics in the basal ganglia, cerebellum, and supplementary motor area. His work combines mathematical modeling with experimental approaches to understand the neural basis of rhythm perception and production. Dr. Cannon's research interests span timing and rhythm perception , neural dynamics , dynamical systems theory , Bayesian cognition , neural oscillations , and autism research . His approach centers on formulating and simulating neurophysiological and cognitive models, drawing on dynamical systems theory and Bayesian cognitive frameworks. His work incorporates psychophysics, EEG experiments, and collaborations with experimentalists to investigate how the brain processes rhythmic information. Analysis of his recent publications reveals a strong focus on the intersection of rhythm perception, motor control, and autism spectrum disorder. His work demonstrates how beat perception co-opts motor neurophysiology, with particular attention to predictive processes in rhythmic cognition. His research shows reduced precision of motor and perceptual rhythmic timing in autistic adults, while also finding intact sequence learning abilities in certain contexts. Dr. Cannon teaches advanced courses including Machine Learning Methods for Brain Modelling and Neural Data Analysis (PSYCH 734), Computational Models in Neuroscience (NEUROSCI 3MN3), and Neuroscience Seminars. His teaching reflects his interdisciplinary approach that bridges mathematics, neuroscience, and cognitive science. Beyond his academic work, Dr. Cannon is an active musician who performs on violin and guitar, particularly in klezmer and folk music contexts. He has also demonstrated entrepreneurial spirit through founding Flying Leap Games and developing the storytelling game 'Wing It,' which successfully crowdfunded and reached numerous retailers.

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Franz Huberمشاهده پروفایل

دانشیار

University of TorontoCanada

Franz Huber is an Associate Professor in the Department of Philosophy at the University of Toronto (St. George campus). He holds an MA from the University of Salzburg, Austria, and a PhD from the University of Erfurt, Germany. His primary research areas include Formal Epistemology, Philosophy of Science, Philosophical Logic, and Metaphysics. Huber’s work focuses on belief revision, counterfactual reasoning, and means-end rationality, often employing formal methods to address philosophical problems. His publications include influential books like A Logical Introduction to Probability and Induction (2018) and Belief and Counterfactuals (2021), as well as numerous peer-reviewed articles in journals such as Synthese and Journal of Philosophical Logic . His research explores the foundations of epistemology and metaphysics, particularly through ranking theory and its applications to belief change, counterfactuals, and causal reasoning. Huber has also contributed to the philosophy of science, examining confirmation theory and the logic of scientific reasoning. His recent work emphasizes a means-end approach to philosophical problems, integrating instrumental rationality with normative theory. Huber’s publications demonstrate a commitment to bridging formal methods with traditional philosophical inquiry. His academic contributions include editing volumes on belief revision and co-authoring works on ranking theory. He maintains an active research agenda, reflected in his latest book Causality, Counterfactuals, and Belief (2025). His office is located in the Jackman Humanities Building, and he is affiliated with the University of Toronto’s philosophy faculty and research initiatives such as the Balzan Research Project on Styles of Reasoning.

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Alex Shestopaloffمشاهده پروفایل

مدرس

University of TorontoCanada

Alex Shestopaloff is a Lecturer in Statistics at Queen Mary University of London (QMUL), affiliated with the School of Mathematical Sciences. Previously, he was a Research Fellow at the Alan Turing Institute (2017–2020) and a Junior Research Fellow at Campion Hall, Oxford. He holds a PhD in Statistics from the University of Toronto (2016), supervised by Radford M. Neal. His research focuses on developing efficient MCMC methods, high-dimensional time series analysis, network science, and applications in financial market microstructure. Education: PhD in Statistics, University of Toronto (2016) Supervisor: Radford M. Neal Research Interests: Bayesian online learning in non-stationary environments Limit order book modeling and trading strategies Graph clustering and network analysis Statistical methods for high-dimensional data Algorithmic trading and cryptocurrency markets His recent work spans financial engineering, machine learning, and statistical methodologies. Notable contributions include cluster-based trading strategies (ClusterLOB), generalized Bayesian filtering frameworks, and scalable graph analysis techniques. Collaborations with industry partners (e.g., Wise Plc) highlight applied research in financial systems. Advising & Alumni: Current advisees include Yichi Zhang (Oxford), Maria Fernanda Pintado (QMUL), and Dave Lui (Oxford) Alumni: Gerardo Duran-Martin (Postdoc at Oxford-Man Institute), Claudio Bellani (Citadel Securities) Labs/Teams: Leads interdisciplinary projects at QMUL and collaborates with the Alan Turing Institute on financial and network science initiatives.

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Daniel M. Royمشاهده پروفایل

استاد

University of TorontoCanada

Daniel M. Roy is a Professor at the University of Toronto with cross-appointments in the Departments of Computer Science and Electrical and Computer Engineering. He serves as Associate Chair, Statistics, and is a Research Director at the Vector Institute and a CIFAR Canada AI Chair. His research focuses on foundational principles of prediction, inference, and decision-making under uncertainty, spanning machine learning, statistics, mathematical logic, applied probability, and computer science. He has contributed to learning theory, statistical network analysis, probabilistic programming, and Bayesian nonparametric statistics. Education: Ph.D. in Computer Science from MIT (2011), advised by Leslie Kaelbling. Postdoctoral fellowships at the University of Cambridge (Newton International Fellow and Research Fellow). His research explores information theories of learning , online learning , and nonstandard foundations for decision theory . Recent work includes best paper awards at ICML 2024 and advancements in probabilistic programming systems like Church. His publications address problems in generalization bounds, causal bandits, neural network theory, and exchangeable random structures. Scientific Awards include the MIT/EECS George M. Sprowls Doctoral Dissertation Award and the ICML 2024 Best Paper Award. He advises students and postdocs across statistics, computer science, and machine learning, with alumni now holding positions at institutions like Princeton, Imperial College London, and the University of Chicago.

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