جست‌وجوی استاد و پژوهشگر

Fatih Nayebiمشاهده پروفایل

مدرس

• Information Systems
• Data Science
• Machine Learning
• +۵ مورد دیگر

Dr. Fatih Nayebi is a Faculty Lecturer in Information Systems at McGill University while also serving as Vice President of Data & AI at the ALDO Group. He bridges academic research with enterprise innovation, focusing on data science, machine learning, and AI systems. Academic Background: Ph.D. in Computer Engineering from École de technologie supérieure M.Sc. in Software Engineering from Boğaziçi University B.Sc. in Computer Engineering from Boğaziçi University Dr. Nayebi's research interests include: Information Systems Data Science Machine Learning Engineering & MLOps Deep Learning Agentic AI Human-Computer Interaction AI in Retail His recent publications focus on AI for retail, mathematical foundations of AI, information integrity in democratic systems, and best practices for technical documentation. Dr. Nayebi also teaches graduate courses at McGill University including: Enterprise Data Science Machine Learning in Production Introduction to AI and Deep Learning Applications and Architectures of Deep Learning Designing and Developing Agentic AI Systems As an active speaker and thought leader, Dr. Nayebi has participated in events such as: World Summit AI Americas RETHINK Retail NRF Nexus 2025 Supply Chain Research Forum JOPT2025 - Annual Conference of Optimization Days He is also the founder of Gradient Divergence, an advisory studio focused on advanced AI solutions for retail and consumer brands.

Eric Liberdaمشاهده پروفایل

استاد

• Toxicology
• Pandemic Planning
• Inhalation Exposures
• +۳ مورد دیگر

Eric Liberda is an Interim Co-Director and Professor at the School of Occupational and Public Health, part of the Faculty of Community Services at Toronto Metropolitan University. He holds a doctorate in Environmental Medicine from New York University School of Medicine. His expertise spans Toxicology, Pandemic Planning, Inhalation Exposures, and Risk Assessment. Dr. Liberda’s research focuses on protecting Indigenous communities in Canada and globally from environmental health threats, particularly through community-driven initiatives like land-based programs and food security projects. He actively collaborates with organizations such as the Society of Toxicology and the Society of Environmental Toxicology and Chemistry. His work emphasizes innovative solutions to environmental contamination, pandemic preparedness, and the integration of traditional knowledge with modern science. Notable projects include assessing methylmercury risks in Indigenous diets, developing opioid treatment programs, and addressing water policy gaps in Indigenous regions. Dr. Liberda is committed to advancing public health equity through interdisciplinary approaches, leveraging technologies like machine learning and AR/VR to enhance risk communication and community engagement.

Sivan Sabatoمشاهده پروفایل

دانشیار

• Interactive machine learning
• Machine learning theory
• Fairness in machine learning
• +۳ مورد دیگر

Sivan Sabato is an Associate Professor at McMaster University's Department of Computing and Software , a Canada CIFAR AI Chair, and faculty member at the Vector Institute of Artificial Intelligence . She holds a joint appointment at Ben-Gurion University's Department of Computer Science while on leave. Her research focuses on machine learning theory, active learning algorithms , and fairness in machine learning . Education: PhD in Computer Science, Hebrew University of Jerusalem Postdoctoral Fellowship, Microsoft Research New England Her theoretical work develops interactive learning frameworks that optimize information costs through algorithmic interaction patterns. Recent publications emphasize differential privacy and discriminative feature analysis with applications to healthcare and social data. She serves as Action Editor for Journal of Machine Learning Research and organizes conference tracks including ICML 2022-2023 and ALT 2021 . Awards include the Alon Scholarship and Google Anita Borg Memorial Scholarship . Advising: Actively supervises Computer Science PhD and MSc students through McMaster's Faculty of Engineering. Research interns can apply via the Vector Institute program with Summer 2026 opportunities.

Colin G. Farquharsonمشاهده پروفایل

استاد

• Geophysics
• Electromagnetic Modeling
• Geophysical Inversion
• +۷ مورد دیگر

Colin G. Farquharson is a Professor in the Department of Earth Sciences at Memorial University of Newfoundland. His career spans roles including Assistant Professor (2008-2018), Associate Professor (2014-2018), and full Professor since 2018. He holds a Ph.D. in Geophysics from the University of British Columbia (1995) and a B.Sc. (Honours) in Geophysics from the University of Edinburgh (1990). His research focuses on forward modeling and inversion of geophysical electromagnetic data, particularly surface geometry inversion (SGI), meshfree methods for unstructured grids, and joint inversion techniques. Key projects include SGI for hydrothermal vent systems, 3D EM modeling for mineral exploration, and computational methods on unstructured tetrahedral meshes. His work addresses challenges in uranium exploration, subsurface imaging, and geological interface reconstruction. Publications emphasize advancements in electromagnetic theory, inversion algorithms, and applications in mineral and hydrocarbon exploration. Notable collaborations include work with Peter Lelièvre on surface-based inversion and stochastic optimization. Research groups include the CAG Group and FacetModeller team, focusing on integrating geological models with geophysical data. Awards and recognitions are not explicitly listed, but his extensive contributions to geophysical methodology and exploration geophysics are well-documented. He advises numerous graduate students and leads projects funded by NSERC, industry partnerships, and international collaborations.

Ayşe Başarمشاهده پروفایل

استاد

• Machine Learning
• Bayesian Inference
• Decision Making under Uncertainty
• +۷ مورد دیگر

Ayşe Başar is a Professor in the Department of Mechanical, Industrial, and Mechatronics Engineering at Toronto Metropolitan University. She serves as Director of the Data Science Lab and Program Director of the Data Science Masters Program. Her research integrates machine learning and Bayesian methods to build predictive models and recommender systems, with applications in software engineering and healthcare. BSc, Boğaziçi University MASc, University of Alabama in Huntsville PhD, London School of Economics Her research focuses on advancing machine learning through Bayesian inference, deep reinforcement learning, and explainability. She develops models that simulate expert decision-making, particularly in complex environments like emergency rooms and software development. Her work emphasizes uncertainty quantification , social learning in networks , and evidence-based decision support systems . The selected publications reflect a strong trend in applying machine learning to real-world problems—particularly in software engineering defect prediction, healthcare team dynamics, and social network analysis. Her work consistently uses probabilistic modeling and ensemble methods to improve prediction accuracy and interpretability. Paper among top 3 most cited in Empirical Software Engineering Journal (2009–2014) Best Paper Award, AAAI (2010) Best Paper Award, IFIP/IEEE IM Symposium (2019) Best Paper Award, ESEM (2013) Dr. Başar actively supervises students and leads impactful research collaborations with institutions like St. Michael’s Hospital and IBM. She has held leadership roles including Director of Big Data for the Provost’s Office and Program Director for Data Analytics Certificates. She is an affiliated scientist at St. Michael’s Hospital and a research fellow at IBM CAS. She leads the Data Science Lab , which focuses on developing advanced machine learning models for complex decision-making. The lab works on projects involving large-scale data integration, predictive analytics, and human-in-the-loop AI systems.

Joy Morrisمشاهده پروفایل

استاد

• Combinatorics
• Algebraic Graph Theory
• Group Theory
• +۴ مورد دیگر

Joy Morris is a Professor at the Department of Mathematics & Computer Science at the University of Lethbridge. She earned her BSc from Trent University in 1992 and her PhD from Simon Fraser University in 2000 under Brian Alspach. Her academic journey includes tenure in 2005, promotion to Associate Professor, and full Professor status in 2015. Research Focus: Interactions between group theory and graph theory, with emphasis on Cayley graphs and automorphisms Key Contributions: Solving the distinguishing number problem for various graph families, advancing DCI/CI group theory Research Overview Her work bridges abstract algebra with graph theory through Cayley graphs. She investigates automorphism groups, Hamilton cycles, and graph symmetry properties. Notable contributions include resolving the DCI property for specific groups, analyzing Praeger-Xu graphs, and studying color-preserving automorphisms. Academic Leadership As a co-author of two influential open-access textbooks ( Proofs and Concepts with Dave Morris and her own Combinatorics text), she has shaped curriculum development and mathematical education outreach programs for parents in Alberta. Her teaching portfolio includes foundational courses like Math 2000 and advanced combinatorial theory instruction. Collaborative Impact With over 30 publications since 1996, her collaborations span international researchers including C. Praeger, P. Spiga, and E. Dobson. Current projects involve hypercube distinguishing costs and automorphism group analysis of vertex-transitive digraphs. She maintains active research into graphical regular representations and graph isomorphism problems.

Roie Levinمشاهده پروفایل

استادیار

• Approximation Algorithms
• Online Algorithms
• Submodular Functions
• +۵ مورد دیگر

Roie Levin is an Assistant Professor at Rutgers University's Department of Computer Science. He received his PhD in Algorithms, Combinatorics and Optimization from Carnegie Mellon University in 2022, advised by Anupam Gupta. Prior to that, he worked at the Allen Institute for Artificial Intelligence (2015-2017) and earned dual BSc degrees in Computer Science/Applied Mathematics and Mathematics from Brown University (2015). Before joining Rutgers, he was a Fulbright Postdoctoral Fellow at Tel Aviv University under Niv Buchbinder. Current Role: Assistant Professor in Computer Science Academic Training: PhD (2022) CMU, BSc (2015) Brown University Postdoctoral: Fulbright Fellow at Tel Aviv University Levin's research focuses on approximation algorithms for uncertain environments (online/dynamic/streaming models) and submodular function optimization. His work spans theoretical foundations and practical implementations across distributed systems, geometric constraints, and reinforcement learning paradigms. Teaching includes graduate and undergraduate algorithms courses (CS 344, CS 513) with emphasis on problem-solving techniques, computational complexity, and modern algorithmic trends. His publications showcase expertise in online algorithms, submodular optimization, and approximation theory with applications in clustering, caching, and machine learning. The 2025 articles demonstrate continued exploration of online consistency and contention resolution, while 2023-2024 works focus on submodular optimization under uncertainty and dynamic environments. Earlier publications (2015-2017) cover semantic parsing, geometric approximation, and planar graph optimization. Fulbright Postdoctoral Fellow Levin's research connects theoretical guarantees with practical implementations, bridging classical algorithm design with modern machine learning applications. His recent work explores primal-dual methods in online settings and robust subspace approximation techniques for streaming data environments.

Archer Yangمشاهده پروفایل

دانشیار

• Statistical machine learning
• High-dimensional statistics
• Computational statistics
• +۸ مورد دیگر

Archer Yang is an Associate Professor in the Department of Mathematics and Statistics at McGill University, with additional affiliations as an Associate Academic Member of Mila - Quebec AI Institute, Associate Member of the School of Computer Science, and Member of the Quantitative Life Science Program. His academic journey began with a PhD from the University of Minnesota under the supervision of Hui Zou, establishing his foundation in statistical methodology and machine learning. Dr. Yang's research spans three interconnected themes: statistical machine learning, applications in drug discovery, and computational genomics and healthcare. In statistical machine learning, he focuses on developing dimensionality reduction, probabilistic models, and causality-inspired methods to address complex high-dimensional data challenges. His work in drug discovery involves creating machine learning models to accelerate drug candidate identification and enhance understanding of drug efficacy and safety. In computational genomics and healthcare, he develops techniques to analyze genomic data, identify biomarkers, and explore the genetic basis of diseases, with the goal of improving precision medicine and predicting patient outcomes. His overarching objective is to bridge advanced data-driven methodologies with impactful applications in pharmacology, genomics, and healthcare. His recent publications reveal a strong trend toward applying machine learning to healthcare challenges, particularly in congenital heart disease analysis, mortality prediction, and drug discovery. His work demonstrates expertise in developing interpretable models that can handle complex, high-dimensional biomedical data while maintaining statistical rigor. The integration of causal inference methods with machine learning appears to be a growing focus in his research trajectory. ICML Spotlight Paper (top 2.6%, 313/12,107) Dr. Yang actively supervises a large research group including multiple postdoctoral fellows, PhD students, and Master's students. His lab has developed several notable software tools, including ml-mr for machine learning in Mendelian randomization. His supervision extends across statistics, computer science, and biomedical applications, reflecting the interdisciplinary nature of his work. He appears to maintain strong collaborative relationships with researchers in healthcare and genomics fields. His laboratory, the Archer Yang Lab, maintains active GitHub repositories focused on machine learning applications in healthcare and drug discovery, with particular emphasis on interpretable models and statistical methodology development. The lab appears to work at the intersection of theoretical statistics and practical biomedical applications, with projects spanning from algorithm development to clinical implementation.

• Reinforcement Learning
• Robotics
• Deep Learning
• +۳ مورد دیگر

Glen Berseth is an Associate Professor in the Department of Computer Science and Operations Research at the University of Montreal and a Senior Academic Fellow at Mila – Quebec Institute for Artificial Intelligence. He is also a Canada CIFAR Chair in AI and Co-Director of the Montreal Robotics and Integrative AI Laboratory (REAL). His work focuses on reinforcement learning, robotics, and deep learning applied to autonomous systems. He holds a postdoctoral background from Berkeley Artificial Intelligence Research (BAIR), working under Sergey Levine. His research emphasizes real-world applications, including human-robot collaboration, continual learning, and multi-agent systems. He teaches courses on robot learning at the University of Montreal and Mila, covering cutting-edge techniques for general-purpose robots. Key research interests include reinforcement learning for robotics, adaptive interfaces, and sim-to-real transfer. His recent work addresses challenges in autonomous learning systems, such as robust locomotion control and efficient exploration strategies. Notable awards include the Canada CIFAR AI Chair. He has supervised numerous students, including PhD candidates Ozgur Aslan and Siddarth Venkatraman, and Master’s students like Roger Creus-Castanyer and Léa Demeule, focusing on topics like reinforcement learning and robotic control. Berseth leads research projects funded by organizations like the CRSNG, FCI, and MITACS, addressing topics such as modular lifelong learning and generalization in robotics. His lab, REAL, explores embodied AI and robotics integration.

Yassine Yaakoubiمشاهده پروفایل

استادیار

• Machine Learning-Augmented Optimization
• Probabilistic Machine Learning
• Large-Scale Combinatorial Optimization
• +۶ مورد دیگر

Dr. Yassine Yaakoubi is an Assistant Professor in the Department of Mechanical, Industrial and Aerospace Engineering at Concordia University. He specializes in research areas including Machine Learning-Augmented Optimization, Probabilistic Machine Learning, and Large-Scale Combinatorial Optimization. His work addresses challenges in stochastic programming, uncertainty quantification, and decision support systems for climate change adaptation. He supervises graduate students in Industrial Engineering (MASc and PhD programs) and focuses on applications in mining, aviation, intelligent transportation systems, and supply chain optimization. His research integrates machine learning with optimization techniques to solve complex real-world problems.

Frank Woodمشاهده پروفایل

دانشیار

• Probabilistic Programming
• Deep Generative Models
• Reinforcement Learning
• +۴ مورد دیگر

Frank Wood is an Associate Professor of Computer Science at the University of British Columbia and a Canada CIFAR AI Chair at AMII. He directs the Pacific Laboratory for Artificial Intelligence (PLAI) research group and co-founded Inverted AI, a spin-out focused on advanced simulation for autonomous vehicles. His research focuses on probabilistic programming, deep generative models, and reinforcement learning, with applications in autonomous driving, robotics, and vision. Wood teaches courses such as Machine Learning and Data Mining (CPSC 340) and Topics in Artificial Intelligence (CPSC 532W). He has been on academic leave from January to December 2025. His work integrates theoretical advancements with practical applications, including projects like PLAICraft, a large-scale embodied AI dataset. Key awards include the ICML Best Paper Honourable Mention and contributions to probabilistic programming systems like Anglican. His research group collaborates on tools such as TorchDriveEnv for autonomous driving benchmarking and explores cutting-edge methods in diffusion models and continual learning.

Ilham Akhundovمشاهده پروفایل

دانشیار

• Statistics
• Probability Theory
• Distribution Theory
• +۵ مورد دیگر

Ilham Akhundov is an Associate Professor (Teaching Stream) and Director of Mathematics, Business, and Accounting Programs at the University of Waterloo. His primary affiliation is with the University of Waterloo, where he holds a leadership role in academic program development. He is also a member of the Steering Committee and Affiliated Faculty Members groups. His contact information includes the email ilham.akhundov@uwaterloo.ca and phone extension 43113. Research interests focus on statistical methodologies, particularly distribution theory, probability, and regression analysis. Key areas include characterization of distributions (e.g., Student's t-distribution), record values, and stochastic processes. His work often explores regression properties and conditional distributions in probabilistic contexts. A notable emphasis is placed on theoretical advancements in statistical modeling and their applications. His publications from 2002 to 2013 highlight recurring themes in distributional analysis, regression frameworks, and probabilistic systems. No scientific awards or grants are explicitly listed, though his contributions to academic leadership and program direction are central to his profile. Advising roles or student collaborations are not detailed in the provided materials.

Quentin Cappartمشاهده پروفایل

دانشیار

• Artificial Intelligence
• Combinatorial Optimization
• Constraint Programming
• +۵ مورد دیگر

Quentin Cappart is an Associate Professor at Polytechnique Montréal since 2020, specializing in artificial intelligence , combinatorial optimization , and constraint programming . His work integrates machine learning and operations research to solve complex industrial problems, particularly in transportation , logistics , and healthcare . Affiliations: Co-founder and Head of the Combinatorial Optimization and Reasoning in Artificial Intelligence Laboratory (CORAIL) Interuniversity Research Centre on Enterprise Networks, Logistics and Transportation (CIRRELT) IVADO researcher Affiliate member of MILA Researcher at IMC2 institute Research focus: Hybrid algorithms combining logical reasoning , machine learning , and operations research to enhance decision-making tools. His group emphasizes accessibility for non-experts and small companies. Scientific Awards: 2025: Early Career Engineer Award (Ordre des ingénieurs du Québec) 2025: Early Career Professor Award (Polytechnique Montréal) 2025: Prix Méritas (Polytechnique Montréal) 2024: Early Career Researcher Award (ACP) 2024: Best Paper Award (ACP Machine Learning track) 2023: Winning approach for Citylearn challenge (NeurIPS) Teaching: Courses include INF8175: Artificial intelligence - methods and algorithms and INF6102: Local search and metaheuristics . He also teaches at ACP Summer/Winter Schools.

Lap Chi Lauمشاهده پروفایل

استاد

• Algorithms
• Optimization
• Spectral Graph Theory

Lap Chi Lau is a Professor at the University of Waterloo's Cheriton School of Computer Science. His primary research explores algorithms, optimization, and spectral graph theory. His recent publications focus on graph algorithms, spectral methods, and combinatorial optimization, with consistent applications to network design and experimental methodologies. Publications demonstrate advanced techniques in graph sparsification, eigenvalue methods, and efficient algorithm design for complex computational problems. His work frequently intersects theoretical computer science and applied mathematics, contributing to foundational improvements in graph partitioning, spectral clustering, and randomized algorithms.

Chengnian Sunمشاهده پروفایل

دانشیار

• Software Engineering
• Software Reliability
• Software Security
• +۵ مورد دیگر

Chengnian Sun is an Associate Professor at the University of Waterloo's David R. Cheriton School of Computer Science. He holds a Ph.D. from the National University of Singapore (2013). His research focuses on software engineering, emphasizing software reliability, security, and developer productivity. His work spans compiler testing, program analysis, cybersecurity, and programming language tools. Key research trends include leveraging large language models (LLMs) for compiler testing and program reduction, probabilistic debugging techniques, and enhancing software security against ransomware and fuzzing attacks. His contributions address challenges in program simplification, fault localization, and vulnerability detection. Notable projects include frameworks like Perses (syntax-guided program reduction), T-Rec (language-agnostic program reduction), and tools like AddressWatcher for memory leak detection. His work bridges theoretical advancements with practical software engineering solutions. Chengnian advises on compiler reliability, cybersecurity, and developer productivity. His research has led to collaborations with industry on testing tools and security frameworks. He maintains an active lab focused on advancing software systems through rigorous analysis and innovation.