Find Academic Supervisors

• Probability Theory
• Random Matrix Theory
• Stochastic Processes
• +5 more

Elliot Paquette is an Associate Professor in the Department of Mathematics and Statistics at McGill University, located in Burnside Hall. His research bridges probability theory, stochastic processes, and high-dimensional statistics, with a focus on applications in machine learning optimization and random matrix theory. He holds a prominent position within the Faculty of Science, contributing to both theoretical and applied mathematical research. While explicit educational details are not provided, his work suggests expertise in advanced mathematical disciplines. His research interests include stochastic gradient descent dynamics, random matrix ensembles, and the interplay between topology and data analysis. Notable contributions address high-dimensional optimization challenges, such as generalization properties of SGD and the behavior of neural scaling laws. His recent publications (2023–2025) explore topics like the extremal landscape of random ensembles, phase transitions in computational models, and the theoretical foundations of differential privacy in regression. These works often combine rigorous probabilistic methods with computational insights. No awards or grants are listed in the provided profile. His advising and team collaborations remain unspecified, though his research often intersects with machine learning and computational statistics communities. He is affiliated with McGill’s Mathematics and Statistics department and contributes to interdisciplinary research through his position in the Faculty of Science.

Leonid SigalView profile

Associate Professor

• Computer Vision
• Machine Learning
• Computer Graphics
• +8 more

Leonid Sigal is an Associate Professor in the Department of Computer Science at the University of British Columbia (UBC), holding the NSERC Canada Research Chair (CRC II) in Computer Vision and Machine Learning and a CIFAR AI Chair at the Vector Institute. He also serves as an Academic Advisor to Borealis AI. His research focuses on visual understanding, including object recognition, scene analysis, motion capture, and the intersection of computer vision with machine learning and graphics. Prior roles include Senior Research Scientist at Disney Research and Adjunct Faculty at Carnegie Mellon University. Education: Ph.D. (Computer Science, Brown University, 2008), M.Sc. (Computer Science, Brown University, 2003), B.Sc. (Computer Science & Mathematics, Boston University, 1999). Research interests span computer vision challenges such as human motion analysis, activity recognition, and generative models, alongside cross-disciplinary work in robotics and psychology. Notable achievements include Best Paper Awards at WACV 2015 and AMD 2012/2006, and leadership in organizing workshops like VisStory and tutorials on 'Looking at People.' Awards include the Killam Accelerator Fellowship, NSERC DAS award, and recognition as Area Chair for CVPR 2022, ICML 2022, and IJCAI 2022. His lab advises numerous graduate students and postdocs, with active collaborations spanning industry (e.g., Disney, Borealis) and academia (e.g., CMU, Vector Institute).

Daniel J. McDonaldView profile

Professor

• Epidemiology Modeling
• Statistical Methodology
• Machine Learning
• +3 more

Prof. Daniel J. McDonald is a Professor in the Department of Statistics at the University of British Columbia (UBC), Faculty of Science. His research focuses on statistical methodology, epidemiology modeling, machine learning, and time-series analysis. He has contributed to the development of R packages for statistical computing and has advised students like Wakeel Adekunle Kasali and Jiaping(Olivia) Liu. His work spans applications in public health, neuroscience, and econometrics. Education details are not explicitly provided, but his career trajectory suggests advanced training in statistics. Research interests emphasize statistical methods for complex data, including fMRI analysis, epidemic forecasting, and structural brain connectomics. He has published extensively on topics such as time-varying reproduction number estimation, sparse group lasso algorithms, and trend filtering techniques. His articles reflect a blend of methodological innovation and applied problem-solving in public health crises (e.g., COVID-19), neuroscience, and macroeconomic modeling. Collaborations likely involve interdisciplinary teams given the scope of his work in health informatics and computational neuroscience.

• Smart Grids
• Renewable Energy Systems
• Power Systems Optimization
• +3 more

Paul François is an Adjunct Professor in the Department of Physics at McGill University and a primary affiliate at Université de Montréal, associated with MILA Québec. His research focuses on smart grid architecture, renewable energy integration, and power systems optimization. He leads a research group advising graduate students in energy-related disciplines. Key projects include flexibility management in sustainable power systems, data-driven optimization techniques, and machine learning applications in energy systems. Recent work highlights include contributions to cold load pick-up estimation, microgrid control, and reinforcement learning-based energy management strategies. Affiliations: Université de Montréal (primary), McGill University (Adjunct), MILA Québec Research Interests: Smart Grids, Renewable Energy Systems, Power Systems Optimization, Machine Learning in Energy His group has developed innovative frameworks for constraint screening in unit commitment, energy storage control, and distributed energy resource coordination. Advised students include Jimmy Hsu (PhD 2021), Thomas Rademaker (PhD 2020), and Nacer Boukacem (M.Sc. 2021). Current research explores spatio-temporal flexibility management and autonomous building energy systems using deep reinforcement learning. Publications span foundational topics like microgrid control architectures and advanced methods such as polynomial chaos expansion for uncertainty analysis.

Pierre PayeurView profile

Professor

• Machine Vision
• 3D Sensing and Modeling
• Human Motion Capture
• +4 more

Pierre Payeur is a Professor in the School of Electrical Engineering and Computer Science at the University of Ottawa. He holds a PhD from Université Laval (1999) and has been a faculty member since then. He leads the SMART Research Group and co-founded the VIVA Research Laboratory. His roles include Graduate Programs Coordinator for the Ottawa-Carleton Institute for Electrical and Computer Engineering and faculty advisor for the ARISE student robotics club. Education: Ph.D. in Electrical Engineering from Université Laval (1999) Research interests focus on machine vision, 3D sensing, human motion capture, tactile sensing, robotic navigation, and computational intelligence. His work integrates industry collaboration and has been funded by major granting agencies. Recent articles explore domain adaptation in human action recognition, multi-agent robotics, and soil temperature prediction using AI. His contributions include over 150 technical papers and supervision of 70+ researchers. Awards: Best Paper Awards at IROS 2020 and ISVC 2021 Advising and grants: Over 70 supervised researchers, grants from major agencies. Active in professional roles like co-chair of IEEE TC-28 and organizer of the ROSE conference series. Labs: SMART Group and VIVA Lab, focusing on robotics, sensing, and autonomous systems.

Vincent BouchardView profile

Professor

• String Theory
• Mathematical Physics
• Geometry and Topology
• +3 more

Professor Vincent Bouchard is a faculty member in the Department of Mathematical & Statistical Sciences at the University of Alberta. His research bridges mathematics and physics, focusing on string theory, geometry, topology, number theory, quantum field theory, and gauge theory. He is passionate about teaching and has developed calculus courses and lecture notes emphasizing active learning environments. His work includes contributions to both theoretical physics and applied computing domains such as robotics and medical imaging. Outside academia, Bouchard is an avid runner and outdoor enthusiast, competing in ultra-marathons and backcountry activities in the Rockies. He also maintains interests in music, particularly piano and jazz, and skiing. His research publications span topics from salient object detection in computer vision to control systems in robotics, reflecting his interdisciplinary approach.

KashlakView profile

Associate Professor

• Nonasymptotic Statistics
• Concentration inequalities
• Permutation and Randomization tests
• +7 more

Dr. Kashlak is an Associate Professor in the Department of Mathematical & Statistical Sciences at the University of Alberta. He holds a PhD from the University of Cambridge (2017), MSc from Johns Hopkins University (2011), and BSc from McGill University (2008). His research focuses on nonasymptotic statistics, concentration inequalities, functional data analysis, and probabilistic methods in Banach spaces. He is particularly known for developing permutation and randomization tests, integrating group theory into statistical frameworks, and applying statistical methods to diverse datasets ranging from sleep apnea diagnosis to electoral analysis. **Research Contributions**: His work bridges theoretical statistics and applied problems, with key contributions in covariance operator inference, topological hidden Markov models, and sparse precision matrix estimation. Notable publications include advancements in bootstrap methods for generalized linear models and analytic permutation testing for functional ANOVA. He has collaborated with institutions globally, including the University of Cambridge and Baylor University. **Teaching & Outreach**: He teaches advanced courses like Applied Regression Analysis (Stat 378) and Probability and Measure (Stat 571). His YouTube channels, 'Cache Lack Stats' and 'Cache Lack Math & Stats Lectures', provide accessible explanations of statistical concepts and dataset analyses. He has developed R packages like sparseMatEst for sparse covariance estimation and fdcov for functional data analysis. **Awards & Service**: Kashlak has received an NSERC Discovery Grant ($161,000), SSC New Investigator Award, and multiple grants from the University of Alberta. He organized workshops like the Alberta Math Dialogue (2019) and served as Session Chair at EcoSta 2019 and WNAR 2018. His work has been featured in Significance Magazine and presented at global conferences such as the IMS-Bernoulli Symposium and the RSS Conference. **Lab & Collaborations**: His research group focuses on statistical methodology with applications in healthcare, spatial analysis, and high-dimensional data. He actively mentors students, including Xinyu Zhang, and collaborates with researchers in machine learning and biomedical fields.

Alexander LitvakView profile

Professor

• Convex Geometry
• Random Matrix Theory
• Probability Theory
• +3 more

Alexander Litvak is a Professor in the Department of Mathematical and Statistical Sciences at the University of Alberta. His research focuses on convex geometry, random matrix theory, and probability theory with applications to high-dimensional geometry and stochastic processes. Education: He holds a Ph.D. from Tel Aviv University and an M.Sc. from Saint Petersburg State University. Research Interests: Litvak explores geometric probability, random structures, and their applications in optimization and functional analysis. His work often addresses questions related to convex bodies, matrix singularity, and the behavior of high-dimensional random systems. Key topics include minimal dispersion, random polytopes, and spectral properties of random graphs. Publications: His recent work (2020-2024) emphasizes probabilistic methods in convex geometry and random matrix theory, with notable contributions to linear bandits, sparse matrix analysis, and the circular law for digraphs. These studies reveal trends in understanding high-dimensional phenomena and probabilistic structures. Grants and Awards: No specific grants or awards listed in the provided materials. Advising and Labs: No student advisees or lab affiliations explicitly mentioned in the text.

Luc DevroyeView profile

Professor

• Computer Science
• Probability Theory
• Algorithms
• +4 more

Luc Devroye is a Professor in the School of Computer Science at McGill University, Montreal. He joined McGill in 1977 after completing his PhD at the University of Texas in 1976. His research spans probabilistic analysis of algorithms, random structures, and typography. Notable contributions include work on random trees, data structures, and simulation methods. Affiliations : McGill University since 1977, School of Computer Science. Education : PhD in Computer Science from the University of Texas (1976), studies at the University of Leuven and Osaka University. Research Interests : Algorithms, probability theory, random graphs, data structures, and typography. Focus on average-case analysis, random tree structures, and simulation techniques. His work emphasizes theoretical foundations with practical applications, including contributions to random number generation and algorithmic efficiency. He advises students in theoretical computer science and maintains an active research agenda with over 500 publications. Teaching : Courses like COMP 252 (Algorithms), COMP 690 (Probabilistic Analysis of Algorithms). Students : Mentored numerous PhD and MSc students, many now in academia and industry.

Gabriel SpadonView profile

Assistant Professor

• Spatiotemporal Analytics
• Time-Series Forecasting
• Complex Network Mining
• +13 more

Gabriel Spadon is an Assistant Professor in the Faculty of Computer Science at Dalhousie University, Halifax, Canada. He leads the MAPS Lab (Modeling and Analytics on Predictive Systems), focusing on spatiotemporal analytics, network science, and machine learning. His work is part of the Big Data Analytics, AI & Machine Learning research cluster. Education: PhD in Computer Science and Computational Mathematics, University of São Paulo (USP), 2021 MSc in Computer Science and Computational Mathematics, University of São Paulo (USP), 2017 BSc in Computer Science, São Paulo State University (UNESP), 2015 Research Interests: Gabriel Spadon’s research lies at the intersection of data science, machine learning, and geoinformatics . He specializes in spatiotemporal forecasting , complex network mining , and trajectory modeling , particularly in maritime and urban environments. His work integrates physics-informed AI, graph-based learning, and environmental data to improve predictive accuracy and decision support. Key domains include vessel movement prediction, environmental monitoring, and urban intelligence. Recent Publication Trends: His latest publications (2024–2025) focus on maritime mobility , using AIS data and physics-informed neural networks (PINNs) for vessel trajectory forecasting. He applies deep learning, clustering, and probabilistic fusion to model complex, multi-modal movement patterns. Other themes include fishing activity detection, port network analysis, and AI for public health and climate matching. Scientific Awards: Advising and Grants: Dr. Spadon supervises multiple graduate students and research assistants in the MAPS Lab, working on mobility data mining, physics-informed AI, and geospatial analytics. His research is supported by major grants from the Ocean Frontier Institute (OFI) , Canadian Space Agency (CSA) , Mitacs , and IMT Atlantique . He leads projects such as smartWhales , AISViz , and Physics-Informed Mobility Forecasting , involving international collaborations with institutions in Brazil and France. Labs and Teams: He is the Director of the MAPS Lab , which develops cutting-edge computational methods for predictive systems. The lab collaborates with the smartWhales initiative (DHI, WSP, Fisheries and Oceans Canada), MERIDIAN , and the Institute for Big Data Analytics . His team includes PhD and MCS students working on AI, data mining, and signal processing for real-world ocean and urban challenges.

Josh TenenbaumView profile

Professor

• Computational Cognitive Science
• Artificial Intelligence
• Bayesian Inference
• +8 more

Josh Tenenbaum is a Professor of Computational Cognitive Science at the Massachusetts Institute of Technology , affiliated with the Department of Brain and Cognitive Sciences , CSAIL , and CBMM . His research focuses on computational models of human cognition, integrating Bayesian inference, probabilistic programming, and machine learning with behavioral experiments in adults and children. Research spans concept learning, causal reasoning, word meaning acquisition, and theory-of-mind modeling. Recent publications emphasize neuro-symbolic integration, physical reasoning, and adaptive world modeling. Awards include multiple Cognitive Science Computational Modeling Prizes and IEEE CVPR/NIPS recognitions.

• Cardiology
• Electrophysiology
• Cardiac Arrhythmias
• +3 more

Javad Hashemi serves as an Adjunct Assistant Professor at Queen's University within the School of Computing (Faculty of Arts and Science), with cross-appointments at the Translational Institute of Medicine (TIME). His primary academic affiliation combines computational expertise with clinical cardiology applications. His research focuses on cardiovascular electrophysiology , specializing in atrial fibrillation mechanisms, cardiac arrhythmia detection, and ablation therapy optimization. Key methodologies include intracardiac electrogram analysis, dominant frequency mapping, and machine learning applications for ECG/IEGM signal processing. Current work emphasizes Real-time arrhythmia classification systems Uncertainty-aware diagnostic models Novel ablation guidance techniques using electrogram feature analysis Continual learning frameworks for medical monitoring His publication portfolio demonstrates consistent innovation in translational cardiovascular engineering , with recent work (2022-2025) advancing AI-driven ECG analysis while maintaining continuity with earlier electrophysiology research (2014-2019) on atrial fibrillation mapping and ventricular tachycardia ablation strategies. No scientific awards were documented in the source materials. Dr. Hashemi maintains active research collaborations across Queen's medical and engineering departments, particularly through TIME. His work integrates clinical electrophysiology data with advanced computational modeling to develop next-generation cardiac diagnostic and therapeutic tools. Current projects focus on uncertainty quantification in arrhythmia classification and continual learning systems for longitudinal patient monitoring.

• Spatiotemporal Data Management
• Spatial Crowdsourcing
• Routing Algorithms
• +3 more

Mario Nascimento is a Professor at the University of Alberta 's Department of Computing Science within the Faculty of Science. He previously served as department Chair from 2014-2021 (with a 2019-2020 leave) and holds affiliations with institutions including the National University of Singapore , Aalborg University , and others. His current leave-of-absence status allows focusing on specific research initiatives.

Bao Michael UyenView profile

Adjunct Professor

• Complex Systems
• Robotics
• Artificial Intelligence
• +7 more

Bao Michael Uyen is an Adjunct Professor at the University of Ottawa Faculty of Engineering (Department of Mechanical Engineering) and a Senior Scientist at Defence Research and Development Canada (DRDC) , a role he has held since 1993. He is also co-chair of NATO Modelling Simulation Group 186, leading research on multidimensional data farming. His affiliations span NORAD, US Space Command, NATO Centre for Maritime Research & Experimentation, and DRDC Atlantic. Education: PhD in Theoretical Particle Physics, McGill University (1993) BSc in Physics (summa cum laude), University of Ottawa (1988) BSc in Mathematics (summa cum laude), University of Ottawa (1988) Undergraduate studies at Caltech (2 years) Research Interests: Dr. Uyen's research lies at the intersection of complex systems, autonomous agents, and defense applications. He investigates how autonomous vehicles, human operators, and AI systems interact in high-stakes environments. His work spans robotics, AI, cybersecurity, machine learning, combinatorial optimization, stochastic processes, and discrete mathematics. These disciplines are applied to mine countermeasures, ballistic missile defense, multi-domain operations, and strategic deterrence. Research Trends: His recent publications reflect a strong focus on multi-domain defense modeling , AI-enhanced simulation , and autonomous systems . Themes include quantum game theory in deterrence, probabilistic modeling for mine detection, and data farming for strategic decision support. These works are often collaborative and NATO-linked, emphasizing real-world defense applications. Scientific Awards & Honors: Koopman Prize 2014 – Outstanding Military Operations Research (INFORMS) Third Prize, CORS Practice Competition 2006 – AUV Mine Countermeasures Deputy Minister Commendation Award – NORAD Sustainability Special Merit, CORS Practice Competition 2000 – Search & Rescue Modeling Gold Medal, University of Ottawa 1988 NSERC Postdoctoral Fellowship 1993–1995 Multiple NSERC and Ontario Graduate Scholarships Advising & Grants: Dr. Uyen has mentored numerous graduate students who now hold positions at institutions like Yale, IBM, and the University of Manitoba. He has secured over $6.7 million CAD in defense research funding, including a $5.6M collaborative project with uOttawa, UNB, and NRC focused on autonomous agent coordination. Labs & Teams: He leads research under the NATO Modelling Simulation Group 186 and collaborates with DRDC teams across Canada and internationally. His work integrates defense simulation, robotics, and AI, often involving interdisciplinary teams from academia, military, and government agencies.

Huang FangView profile

Researcher

• Optimization
• Learning theory
• Data mining
• +3 more

Huang Fang is a Research Scientist at Baidu Research's Cognitive Computing Lab, specializing in optimization theory, machine learning, and data mining. With a strong academic background from top institutions, Huang has established themselves as a prolific researcher in the field of machine learning, particularly in federated learning, privacy-preserving algorithms, and optimization methods. Ph.D. in Computer Science, 2021, University of British Columbia M.S. in Statistics and Computer Science, 2017, University of California, Davis B.Sc. in Financial Mathematics, 2015, Central University of Finance and Economics (China) Huang Fang's research spans multiple critical areas in machine learning, with a particular focus on optimization theory and its applications. Their work bridges theoretical foundations with practical implementations, especially in federated learning systems where privacy and efficiency are paramount. The research portfolio demonstrates expertise in developing novel algorithms for large-scale learning problems, with particular emphasis on convergence analysis, sparse optimization, and scalable methods for knowledge representation. Huang has made significant contributions to understanding the theoretical properties of optimization algorithms under various constraints and settings. The publication record reveals a strong trajectory in advancing machine learning methodologies, with recent work focusing on differentiable neuro-symbolic reasoning, efficient Markov logic networks, and improved convergence guarantees for privacy-preserving stochastic optimization. The research shows a consistent pattern of addressing fundamental challenges in distributed and large-scale machine learning, particularly in the areas of federated learning fairness, efficient coordinate descent methods, and matrix completion techniques. Huang Fang has demonstrated exceptional productivity in high-impact venues including NeurIPS, ICML, ICLR, WWW, and other top-tier machine learning conferences. The work consistently combines theoretical rigor with practical applicability, making significant contributions to both the foundational understanding of optimization methods and their implementation in real-world systems.