جست‌وجوی استاد و پژوهشگر

Trevor Campbellمشاهده پروفایل

دانشیار

• Bayesian Inference
• Bayesian Nonparametrics
• Machine Learning
• +۵ مورد دیگر

Trevor Campbell is an Associate Professor in the Department of Statistics at the University of British Columbia (UBC), Vancouver. He holds a Ph.D. in Machine Learning and Statistics from MIT and a B.A.Sc. in Aerospace Engineering from the University of Toronto. His research focuses on automated, scalable Bayesian inference algorithms, Bayesian nonparametrics, and streaming data analysis. Campbell is a core contributor to probabilistic programming tools like Pigeons.jl and has developed influential methods for coreset-based Bayesian inference. Education : Ph.D. in Machine Learning and Statistics (2016), MIT M.S. in Aeronautics and Astronautics (2013), MIT B.A.Sc. in Aerospace Engineering (2011), University of Toronto Research Interests : His work emphasizes scalable Bayesian computation, including variational inference, MCMC optimization, and coresets. He develops algorithms that balance statistical accuracy with computational efficiency, particularly for large-scale datasets. His recent work explores adaptive samplers (e.g., AutoStep, autoMALA) and theoretical guarantees for coreset methods. Applications span astrophysics, materials science, and network analysis. Awards & Grants : NSERC Discovery Grant (2025) Blackwell-Rosenbluth Award (2021) PIMS Early Career Award (2023) Google Perception Academic Funding (2020) Advising & Labs : He supervises a team of PhD and M.Sc. students at UBC, focusing on Bayesian methodology and computational tools. His lab collaborates with institutions like SFU and MIT on projects involving distributed sampling and probabilistic modeling. He co-organizes workshops on Bayesian computation and serves on editorial boards for Bayesian Analysis and TMLR.

Alexandre Bouchard-Côtéمشاهده پروفایل

استاد

• Computational Statistics
• Bayesian Inference
• Monte Carlo Methods
• +۴ مورد دیگر

Alexandre Bouchard-Côté is a Professor of Statistics at the University of British Columbia (UBC), affiliated with the Department of Statistics within the Faculty of Science. His research focuses on computational statistics, Bayesian methods, and Monte Carlo techniques, with applications in evolutionary biology, cancer genomics, and computational linguistics. Education : PhD in Computer Science (with Designated Emphasis in Statistics) from UC Berkeley (2010), BSc in Mathematics and Computer Science from McGill University (2005). Affiliations : Director of the Blang probabilistic programming project and leader of the Bouncy Particle Sampler research group. Research Interests : Bouchard-Côté develops scalable Bayesian computational methods, including non-reversible Monte Carlo algorithms like the Bouncy Particle Sampler, and applies these to problems in cancer phylogenetics, evolutionary dynamics, and historical linguistics. His work emphasizes bridging theoretical foundations with practical tools for data science. Publications Trends : Recent work spans distributed sampling frameworks (e.g., Pigeons.jl), variational phylogenetic inference, and cancer clonal evolution modeling. His articles often address algorithmic scalability and interdisciplinary applications in biology and astronomy. Awards : CRM-SSC Prize in Statistics (2024) PIMS-UBC Mathematical Sciences Young Faculty Award (2018) Tweedie New Researcher Award (2016) Advising & Grants : Supervises graduate students (e.g., Son Luu, Nikola Surjanovic) and leads funded projects on distributed MCMC and cancer genomics. Collaborates with institutions like the Simons Foundation and the Canadian Statistical Sciences Institute (CANSSI). Labs/Teams : Core member of the UBC Statistical Machine Learning group, contributing to open-source tools like Blang and the Bouncy Particle Sampler implementation.

Michael Jong Kimمشاهده پروفایل

دانشیار

• Dynamic Programming
• Statistical Learning
• Robust Optimization
• +۱ مورد دیگر

Michael Jong Kim is an Associate Professor at the Sauder School of Business, University of British Columbia, specializing in the Division of Operations and Logistics. His research focuses on dynamic programming, statistical learning, robust optimization, and the exploration vs exploitation trade-offs in sequential decision-making processes. BASc, M.Math, and PhD from the University of Toronto His work spans topics in stochastic optimization, supply chain dynamics, and information dissemination in uncertain environments. Publications highlight contributions to Bayesian inventory control, semi-Markovian system control, and variance regularization in optimization models. Dr. Kim teaches advanced business analytics courses, including Descriptive and Predictive Business Analytics and Advanced Predictive Business Analytics (MBAN) during the 2024-2025 academic year. He can be reached at mike.kim@sauder.ubc.ca or by phone at +1 604.822.8682.

Mark Crowleyمشاهده پروفایل

دانشیار

• Reinforcement Learning
• Deep Learning
• Manifold Learning
• +۱۲ مورد دیگر

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 a member of the Waterloo Artificial Intelligence Institute (WAII) and the Waterloo Institute for Complexity and Innovation (WICI), and serves as National Secretary of the Canadian Artificial Intelligence Association (CAIAC). His educational background includes a Ph.D. and M.Sc. in Computer Science from the University of British Columbia, where he worked in the Laboratory for Computational Intelligence, and a B.A. in Computer Science from York University. He completed a postdoctoral fellowship at Oregon State University working with Tom Dietterich's machine learning group. Crowley's research focuses on developing dependable and transparent algorithms to augment human decision-making in complex domains with multiple agents, spatial structure, or uncertainty. His work spans Reinforcement Learning , Deep Learning , Ensemble Methods , and Manifold Learning . He frequently collaborates with researchers in applied fields including Computational Sustainability, Sustainable Forest Management, Autonomous Driving, Medical Imaging, and Material Design. His research is motivated by both theoretical opportunities and real-world challenges such as forest fire management, automotive applications, and medical imaging. His recent publications demonstrate a strong focus on addressing challenges in reinforcement learning, particularly around observation costs, multi-agent systems, and causal representation learning. His work on ChemGymRL provides a significant contribution to digital chemistry and material design through reinforcement learning frameworks. The textbook Elements of Dimensionality Reduction and Manifold Learning represents a major contribution to the theoretical foundations of machine learning. Crowley actively supervises graduate students, with recent thesis completions including Shayan Shirahmadi Gale Bagi (PhD, Feb 2025) and Oleksandra Nahorna (MASc, Dec 2024). His lab, UWECEML (Waterloo ECE Machine Learning Lab), focuses on developing new algorithms at the intersection of Machine Learning, Optimization, and Probabilistic Modeling. He teaches courses including ECE 457C (Reinforcement Learning), ECE 657A (Data & Knowledge Modelling & Analysis), and ECE 457B (Fundamentals of Computational Intelligence). His blog Computationally Thinking explores AI, machine learning, and the societal impact of these technologies.

David Duvenaudمشاهده پروفایل

دانشیار

• Machine Learning
• Generative Models
• Bayesian Inference
• +۵ مورد دیگر

David Duvenaud is an Associate Professor at the University of Toronto , holding a Canada Research Chair in Generative Models and a Schwartz Reisman Chair in Technology and Society . He is cross-appointed to the Department of Computer Science and Department of Statistical Sciences . A Sloan Research Fellow and founding member of the Vector Institute , his work bridges deep probabilistic models , AI safety , and scientific computing . PhD in Machine Learning (University of Cambridge, 2014) Postdoc in Hyperparameter Optimization (Harvard University, 2016) Co-founded Invenia (energy forecasting company) His research spans foundational Neural Ordinary Differential Equations (NeurIPS 2018 Best Paper) and Automatic Chemical Design (ACS Central Science 2018) to recent work on AGI governance (2025) and AI safety (2024). Key contributions include stochastic variational inference , implicit differentiation frameworks , and antisymmetrization layers for quantum Monte Carlo. Recent publications (2024-2025) focus on systemic existential risks from AI , many-shot jailbreaking attacks , and epistemic uncertainty quantification . His group trains energy-based models with scalable MCMC samplers and develops invertible neural architectures (e.g., Residual Flows NeurIPS 2019). He also explores human-AI alignment through LLM Processes (NeurIPS 2024) and Sycophancy in Language Models (ICLR 2024). Canada Research Chair (2025) NSERC Grant (2025) Sloan Research Fellow (2021) Schwartz Reisman Chair (2021) Best Paper Award (NeurIPS 2018) Distinguished Paper Award (ICFP 2021) His students include James Requeima , Jesse Bettencourt , and Raymond Douglas . He teaches courses on Statistical Methods for Machine Learning and Differentiable Inference . Current work (2025) investigates systemic human disempowerment through incremental AI capabilities and sabotage risk mitigation via hyperparameter-aware evaluations.

Zhenman Fangمشاهده پروفایل

دانشیار

• Customizable Computing
• Heterogeneous Computing
• FPGA Acceleration
• +۸ مورد دیگر

Dr. Zhenman Fang is an Associate Professor at the School of Engineering Science , Simon Fraser University (SFU) , where he founded and directs the HiAccel Lab . He also holds an associate membership in the School of Computing Science at SFU. His research focuses on customizable computing with software-defined hardware acceleration , addressing performance, energy-efficiency, and reliability in post-Moore’s law computing across domains like machine learning , big data analytics , quantum chemistry , and precision medicine . Education: Ph.D. in Computer Science from Fudan University (2014), with a visit to University of Minnesota during his studies. Postdoctoral Work: University of California, Los Angeles (UCLA) (2014-2017). Industry Experience: Staff Software Engineer at Xilinx (2017-2019). Dr. Fang’s research spans the entire computing stack , including application characterization , accelerator-rich architecture design , and programming/tool support . He has developed frameworks like HiSpMV , SyncNN , and SQL2FPGA , emphasizing FPGA acceleration for vision transformers , quantum chemistry , and spiking neural networks . His work has been recognized with 3 best paper awards (FPL 2024, TCAD 2019, MEMSYS 2017) and 3 best paper nominees (FCCM 2025, HPCA 2017, ISPASS 2018). Recent publications highlight trends in low-precision machine learning ( ShiftQuant , ESRU ), quantum chemistry acceleration ( SERI ), and vision transformer optimization ( Quasar-ViT ). His HiAccel Lab actively mentors PhD and MASc students , with notable graduates like Alec Lu (PhD 2024, now at Meta) and Philip Stachura (MASc, now with BC Graduate Scholarship). Scientific Awards: Inaugural SFU Research Excellence Award - Horizon Award (2025) FPL 2024 Stamatis Vassiliadis Best Paper NSERC Alliance Award (2020) CFI JELF Award (2019) Xilinx University Program Award (2019) IEEE Senior Member (2023) Grants: NSERC Discovery Grant (2019) CFI JELF Funding (2019) Huawei and Xilinx sponsorships Dr. Fang leads open-source initiatives like SyncNN , PASTA , and SQL2FPGA , and serves as General Chair for ASAP 2025 and Program Co-Chair for RAW 2025 . His lab collaborates globally with institutions such as UCLA , Northeastern University , and Xidian University .

Margaret P. Chapmanمشاهده پروفایل

استادیار

• Risk-Averse Control Theory
• Stochastic Control Theory
• Safety Analysis for Dynamical Systems
• +۴ مورد دیگر

Margaret P. Chapman is an Assistant Professor in the Department of Electrical and Computer Engineering at the University of Toronto's Faculty of Applied Science and Engineering. She leads the DATA Lab (Decision Analysis for Trustworthy Autonomy), focusing on risk-averse and stochastic control theory with applications to environmental and human health. Education: B.S. and M.S. in Mechanical Engineering from Stanford University (2012, 2014), Ph.D. in Electrical Engineering and Computer Sciences from UC Berkeley (2020, advised by Claire Tomlin) Her research bridges robust and stochastic optimal control via risk measure theory, emphasizing safety-critical applications in healthcare and sustainable cities. Key challenges include scalable risk-sensitive control methods, integrating physics-based and data-driven models for safety analysis, and promoting technologies that enhance planetary and human well-being. Recent publications focus on risk-averse autonomous systems, CVaR-based safety analysis, and multi-time-scale modeling for cancer treatment. She has advised students in both graduate and undergraduate research roles, including NSERC awardees and thesis participants. Awards: US National Science Foundation Graduate Research Fellowship, Berkeley Fellowship, Terman Engineering Scholastic Award, Leon O. Chua Award She teaches courses like ECE 557 (Linear Control Theory) and ECE 1643 (Risk-Averse Control with Learning). Her invited talks span institutions such as MIT, Princeton, and Georgia Tech, highlighting risk-sensitive analysis and control for trustworthy autonomy.

• Inventory control
• Supply chain management
• Capacity planning
• +۱ مورد دیگر

Tim Huh is a Professor and Chair of the Operations and Logistics Division at the University of British Columbia's Faculty of Commerce and Business Administration. He specializes in inventory control, supply chain management, and operations research, with a focus on dynamic decision-making under uncertainty. B.A., B.Math, M.Math from University of Waterloo M.A. from Regent College M.S., Ph.D. from Cornell University His research spans theoretical and applied topics including renewable energy systems, healthcare operations, and digital learning analytics. Recent work explores wind power storage optimization, asynchronous video usage in education, and multi-echelon inventory solutions. Scientific recognition includes the Canada Research Chair in Operations Excellence and Business Analytics He teaches core business analytics and operations management courses at both undergraduate and graduate levels, emphasizing quantitative decision-making and process fundamentals.

Stephen A. Vavasisمشاهده پروفایل

استاد

• Continuous Optimization
• Data Science
• First-Order Methods
• +۴ مورد دیگر

Stephen A. Vavasis is a Professor in the Department of Combinatorics and Optimization at the University of Waterloo, part of the Faculty of Mathematics. He holds a PhD in Computer Science from Stanford University (1989) and has held academic positions at Cornell University (1989–2006) before joining Waterloo. His research focuses on continuous optimization, data science, first-order methods, scientific computing, and computational mechanics. Current teaching includes courses on convex optimization and portfolio optimization methods. He has served as Associate Dean of Computing (2017–2020) and Interim Director of Data Science graduate programs. His work is supported by NSERC grants. Notable awards include the Hertz Fellowship, Churchill Scholarship, and Guggenheim Fellowship. Vavasis's research emphasizes applications of optimization to clustering, machine learning, and fracture mechanics. His publications span convex optimization frameworks, algorithmic analysis of gradient methods, and numerical methods in mechanics. Recent work explores unifying analyses of first-order optimization algorithms and robust optimization techniques for high-dimensional data problems.

• Artificial Intelligence
• Operations Research
• Revenue Management
• +۳ مورد دیگر

Yuri Levin is a Professor at the Smith School of Business, Queen’s University, where he holds the Stephen J.R. Smith Chair of Analytics and serves as Founding Executive Director of the Smith School of Business Analytics and AI. He also leads the Scotiabank Centre for Customer Analytics. His research focuses on Analytics & AI, with expertise in revenue management, dynamic pricing, and strategic consumer behavior. Levin holds a Ph.D. in Operations Research from Rutgers University and degrees in Economics and Applied Mathematics from Belarus State University. Levin’s academic contributions include co-winning the 2013 INFORMS Revenue Management and Pricing Practice Prize and the 2009 INFORMS COIN-OR Cup. He has advised companies like Scotiabank, Loblaws, and McDonald’s on pricing strategies and consumer analytics. As an Associate Editor of Operations Research , he shapes academic discourse in operations research and pricing strategies. Education: Ph.D. in Operations Research, RUTCOR, Rutgers University (2001) B.S. in Economics, Belarus State University (1998) M.S. (Honours) in Applied Mathematics, Belarus State University (1998) His research explores dynamic pricing models under social influence, strategic consumer behavior, and revenue management in hospitality and retail industries. He has pioneered methodologies for optimizing pricing strategies in networked markets and developed algorithms for cargo capacity management. Awards: 2010 Queen’s School of Business Award for Research Achievement 2003 New Researcher Achievement Award Levin’s teaching spans MBA, Master of Management Analytics (MMA), and executive education programs, covering analytical decision-making and pricing optimization. His advisory work includes roles as a Visiting Professor at the Skolkovo Moscow School of Management and the University of Cambridge’s Judge School of Business.

Minyi Huangمشاهده پروفایل

استاد

• Mean Field Stochastic Control
• Stochastic Algorithms in Multi-Agent Systems
• Wireless Networks
• +۴ مورد دیگر

Minyi Huang is a Professor in the School of Mathematics and Statistics at Carleton University. His research focuses on Mean Field Stochastic Control, Stochastic Algorithms in Multi-Agent Systems, and Wireless Networks. He holds a Ph.D. from McGill University (2003) and has held postdoctoral positions at the University of Melbourne and the Australian National University. Dr. Huang is a Fellow of IEEE and a Member of SIAM. Education: Ph.D. in Electrical and Computer Engineering, McGill University (2003) M.Sc. in Systems and Control, Chinese Academy of Sciences (Beijing) B.Sc. in Mathematics, Shandong University (Jinan, China) Research Interests: Huang's work centers on stochastic control, mean field games, and multi-agent systems. His contributions include theoretical advancements in mean field social optimization, graphon-based control frameworks, and applications in wireless networks and economic models. He has organized workshops on Mathematical Cybernetics and Stochastic Processes, fostering interdisciplinary collaboration. Scientific Awards: Fellow of the IEEE Advising & Grants: Huang has advised numerous graduate students on topics in stochastic control and mean field theory. His grants include funding for international PhD students through Carleton's initiatives. He collaborates on projects involving mean field models for production output and social dynamics. Labs/Teams: Associated with the Ottawa-Carleton Institute for Mathematics and Statistics (OCIMS), contributing to collaborative research in control theory and applied mathematics.

Samuel W.K. Wongمشاهده پروفایل

دانشیار

• Data Science Methodology
• Protein Structure Analysis
• Reliability Engineering
• +۴ مورد دیگر

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.

Rajesh Karkiمشاهده پروفایل

استاد

• Power Systems
• Renewable Energy Integration
• Grid Reliability
• +۳ مورد دیگر

Rajesh Karki is a Professor in the Department of Electrical and Computer Engineering at the University of Saskatchewan’s College of Engineering. He holds a B.E., M.Sc., and Ph.D. in related fields. His research focuses on power system reliability, renewable energy integration, and microgrid resilience, with particular emphasis on addressing challenges posed by extreme weather, cyber threats, and decarbonization targets. Dr. Karki’s work spans theoretical modeling, probabilistic analysis, and practical implementation strategies for smart grids, energy storage systems, and distributed generation. His educational background includes advanced degrees in electrical engineering, complemented by professional engineering licensure (P.Eng.). His research has explored diverse topics such as wind energy curtailment mitigation, energy storage optimization, and demand response mechanisms in developing economies like Nepal. He has authored numerous peer-reviewed publications on grid resilience, reliability economics, and cyber-physical system security. Key themes in his work include: (1) quantifying the reliability value of energy storage in active distribution systems, (2) modeling cyber-physical threats to microgrids, and (3) developing frameworks for extreme weather-resilient infrastructure. Despite the volume of his publications (over 50 articles), no specific awards or grants are explicitly listed in the provided materials. His research often intersects technical, economic, and policy dimensions of sustainable energy systems.

Lei Leiمشاهده پروفایل

دانشیار

• Machine Learning
• Deep Reinforcement Learning
• Internet of Things (IoT)
• +۵ مورد دیگر

Lei Lei is an Associate Professor at the University of Guelph, specializing in Computer Engineering. Her research focuses on Machine Learning/Deep Reinforcement Learning, Internet of Things (IoT)/Internet of Vehicles (IoV), Mobile Edge Computing, and Smart Grid Optimization. She explores cutting-edge applications in energy-efficient systems, autonomous vehicles, and intelligent transportation networks. Her work integrates advanced AI techniques with real-world challenges in communication and control systems. Key research areas include optimizing electric vehicle charging schedules using hierarchical deep reinforcement learning and enhancing vehicular networks through 6G communication protocols. She has pioneered methods for joint communication-control systems, securing federated learning models, and developing robust resource allocation strategies in IoT and edge computing environments. Lei Lei’s publications emphasize interdisciplinary solutions, bridging computer science, electrical engineering, and transportation systems. Her recent work addresses challenges in smart grid security, multitimescale control systems, and the application of AI tools like ChatGPT in connected vehicles. She is affiliated with the AI Affiliated Faculty at the University of Guelph, reflecting her contributions to artificial intelligence research.

• Reinforcement Learning
• Machine Learning Theory
• Online Learning
• +۲ مورد دیگر

Csaba Szepesvari is a Professor in the Department of Computing Science at the University of Alberta and Canada CIFAR AI Chair at Amii. His research focuses on developing efficient learning algorithms for sequential decision making problems, with particular emphasis on reinforcement learning theory, online learning, and bandit algorithms. Research interests include: Foundations of reinforcement learning and online decision making Convergence properties of learning algorithms Bandit problems and exploration-exploitation tradeoffs Function approximation in machine learning His publications demonstrate consistent theoretical contributions to understanding algorithmic convergence, complexity, and efficiency in reinforcement learning. Recent work explores LLM uncertainty estimation, policy gradient methods, and offline-to-online learning transitions. Leadership roles include: Foundations team lead at DeepMind Organizer of RL Theory Virtual seminar series He mentors graduate students in theoretical machine learning through the University of Alberta and Amii research programs.