جست‌وجوی استاد و پژوهشگر

Sarah Dickson-Andersonمشاهده پروفایل

استاد

• Hydrogeology
• Fractured rock systems
• Water security
• +۵ مورد دیگر

Sarah Dickson-Anderson serves as Associate Dean of the Faculty of Engineering and faculty member in the Department of Civil Engineering at McMaster University. Her research expertise centers on hydrogeology with a focus on fractured rock systems and water security challenges in vulnerable communities. Her educational background includes a Ph.D. and B.A.Sc. from the University of Waterloo (1990-2001). Her research program spans multiple dimensions of water security: Characterization of fractured rock systems and contaminant transport Water security in rural, remote, and Indigenous communities Drinking water treatment resilience, particularly after environmental disturbances Gender dimensions of water access in developing regions Interdisciplinary approaches integrating physical, social, cultural and economic elements Dr. Dickson-Anderson's scholarly work demonstrates a clear evolution from technical hydrogeological studies toward increasingly integrated, interdisciplinary research addressing complex water challenges. Her recent publications examine wildfire impacts on water treatment systems, gender dynamics of water collection in Ghana, Indigenous water security in Canada, and innovative modeling approaches for fractured rock systems. Her research impact is evident through: References in patents and policy documents Citations across multiple academic platforms including Mendeley Media coverage in news outlets References in Wikipedia pages Discussion on social media platforms including X (Twitter) and Bluesky As an educator, she teaches Engineering: Communications and Social Impact (ENGINEER 2PX3) and Advanced Groundwater Flow and Contaminant Transport (CIVENG 770/EARTHSC 750), mentoring graduate students in hydrogeology and water security research. Her administrative role as Associate Dean reflects her leadership within the Faculty of Engineering. Her laboratory and research team employ experimental and computational methods to understand water flow and contaminant transport, with applications to real-world water security challenges in diverse community contexts.

Martin Trépanierمشاهده پروفایل

استاد

• Transportation Engineering
• Logistics
• Operational Research
• +۱۶ مورد دیگر

Martin Trépanier is a Full Professor in the Department of Mathematics and Industrial Engineering at Polytechnique Montréal . He directs the Interuniversity Research Center on Enterprise Networks, Logistics and Transport (CIRRELT) and co-holds the Chair in Transport Transformation . His expertise spans transportation engineering, logistics, operational research, and information systems design. Education: B.Eng., M.Sc.A., Ph.D. from Polytechnique Montréal Research Interests focus on Data Valorization , Artificial Intelligence , Geographic Information Systems , and Vehicle Routing in urban transport contexts. He leverages GPS data, RFID, and smart cards for applications in public transit, bike sharing, and freight transport. Recent Articles highlight trends in urban mobility prediction , gender transport disparities , and smart city technologies . His work emphasizes integrating data analytics with sustainable transport planning. Scientific Awards Quebec Research Fund grant (2022) Association québécoise des transports award (2017) Supervision includes 9 ongoing and 75+ completed graduate students across topics like subway disruption modeling , carsharing demand , and smart city logistics . Labs & Affiliations include the Data Intelligence Laboratory , Poly-Industries 4.0 Laboratory , and Institute for Data Valorization (IVADO) .

Charles L. Bérubéمشاهده پروفایل

استادیار

• Applied geophysics
• Mineral physics
• Mathematical modeling
• +۷ مورد دیگر

Charles L. Bérubé is an Assistant Professor in the Department of Civil, Geological and Mining Engineering at Polytechnique Montréal since 2021. His research couples applied geophysics, mineral physics and machine learning to advance mineral exploration and resource characterisation. Education: B.Sc. in Physics, Université de Montréal Ph.D. in Mineral Engineering, Polytechnique Montréal (2018) Research Interests: Dr. Bérubé focuses on electromagnetic geophysics , induced polarization and machine-learning-assisted inversion . Key themes include: Spectral Induced Polarization (SIP) tomography and modelling Neural-network-based rock-physics inversion Mineral prospectivity mapping with variational autoencoders Quantitative mineralogy and computer vision for outcrop analysis Uncertainty quantification in geophysical imaging Publications Overview: His recent work (2022-2024) demonstrates a clear pivot toward data-driven and AI-enhanced geoscience , integrating Bayesian inversion, generative models and deep learning with traditional exploration geophysics. Earlier studies (2017-2020) concentrated on petrophysical characterisation of the world-class Canadian Malartic gold deposit and non-destructive evaluation of concrete degradation. Graduate Supervision & Funding: Dr. Bérubé has supervised one completed M.Sc. thesis (Benzetta, 2024) on graphite geoelectrics. In May 2023 he received a research grant from the Fonds de recherche du Québec as part of a campus-wide allocation exceeding CAD 1.57 million. Contact: Local B-632, Department of Civil, Geological and Mining Engineering Tel: +1 (514) 340-4711 ext. 4991 | Email: charles.berube@polymtl.ca

Hyukjun Gweonمشاهده پروفایل

استادیار

• Sparse-labeled Data Analysis
• Predictive Model Assessment
• Predictive Analytics in Actuarial Science
• +۱ مورد دیگر

Hyukjun Gweon is an Assistant Professor in the Department of Statistical and Actuarial Sciences at Western University . His research focuses on Sparse-labeled Data Analysis , Predictive Model Assessment , and Predictive Analytics in Actuarial Science . Research Areas: Sparse-labeled Data Analysis Predictive Model Assessment Predictive Analytics in Actuarial Science Automated coding for text/survey data Publications highlight methodological advancements in active learning, bias correction, multi-label classification, and reliability assessment for probabilistic models. Applications span insurance valuation, time series analysis, and survey methodology.

• Differential-Algebraic Equations (DAEs)
• Interval Methods
• Structural Analysis
• +۷ مورد دیگر

Nedialko Nedialkov is a Professor in the Department of Computing and Software at McMaster University, Faculty of Engineering. His research centers on differential-algebraic equations (DAEs) , interval methods , and computational science , with a focus on rigorous numerical solutions and structural analysis. Contact: nedialk@mcmaster.ca Teaching: Courses include Scientific Computing, Advanced Topics in Computing and Software, and Principles of Programming. Research Highlights: Nedialkov has developed symbolic-numeric methods for DAEs, improved verified ODE integration, and created tools like DAETS for structural analysis. His work spans applications in hybrid systems , control theory , and biomedical computing . Publications Trends: Over the past 15 years, his scholarship emphasizes DAEs, validated numerical methods, and computational modeling. Key subfields include Port-Hamiltonian systems , block triangularization , interval constraints , and automatic differentiation . Labs & Teams: Collaborates with McMaster Experts and contributes to open-source projects for validated numerical computing.

• Computer Vision
• Machine Learning
• Statistics
• +۶ مورد دیگر

Marcus A. Brubaker is an Associate Professor of Computer Science at York University, founder and director of the Computational Vision and Imaging Lab (CVIL), Faculty Affiliate at the Vector Institute, and Adjunct Professor at the University of Toronto Department of Computer Science. He holds additional roles as a Visiting Professor at Samsung AI Center - Toronto, co-founder and advisor of Structura Biotechnology (creators of cryoSPARC), and Academic Advisor at Borealis AI. His research focuses on building rich models capturing fundamental relationships between the world and observations, with recent emphasis on generative models (particularly normalizing flows) and Cryo-EM structure estimation of biological molecules. His broader interests span vehicle localization, physically realistic human motion modeling, probabilistic programming languages, Bayesian methods, MCMC, and forensic ballistics. His publication trends reveal strong specialization in computer vision and generative modeling, with recent ICCV 2023 papers advancing neural radiance field inpainting and photometrically consistent view synthesis using diffusion models. These works demonstrate interdisciplinary integration of computer graphics, deep learning, and novel view generation techniques. Brubaker has served as Associate Editor for IET Computer Vision and Area Chair/Senior Area Chair for major conferences including CVPR, ECCV, NeurIPS, and AAAI. Previously, he was Research Director at Borealis AI (2018-2020), Research Associate at Cadre Research Labs (creators of TopMatch-GS), and an original contributor to the Stan probabilistic programming framework. He directs the Computational Vision and Imaging Lab (CVIL) at York University alongside colleagues Kosta Derpanis and Michael Brown, focusing on cutting-edge computer vision research with applications in biotechnology and AI. As of August 2024, he has commenced a leave of absence from York University to join Google DeepMind as a Research Scientist in Toronto.

Artur Izmaylovمشاهده پروفایل

استاد

• Quantum Dynamics
• Theoretical Chemistry
• Quantum Computing
• +۵ مورد دیگر

Artur Izmaylov is a Professor of Theoretical Chemistry at the University of Toronto with dual departmental appointments: the Department of Chemistry at the St. George campus and the Department of Physical and Environmental Sciences at the University of Toronto Scarborough (UTSC). He leads the Izmaylov Research Group and is affiliated with the Center for Quantum Information and Quantum Control. His offices are located at EV356 (UTSC) and LM420C (St. George), and he can be contacted at artur.izmaylov@utoronto.ca or via phone at 416-208-2951 (UTSC) / 416-946-8405 (St. George). Professor Izmaylov's research develops novel theoretical and computational approaches to quantum dynamics in complex systems. Key focus areas include: Quantum processes in organic photovoltaics, biomolecules, and catalytic surfaces Hybrid quantum-classical methodologies for subsystem-environment interactions Renormalization techniques for efficient quantum dynamics simulations Quantum computing applications for chemical problems and electronic structure Nonadiabatic dynamics near conical intersections and spin-charge transfer His recent publications (2023-2025) demonstrate strong emphasis on quantum algorithm development for chemical applications, particularly: Advancements in variational quantum eigensolver (VQE) methodologies Quantum resource optimization and error mitigation strategies Novel Hamiltonian decomposition techniques for efficient simulation Applications in molecular vibrations, electronic structure, and materials science Hybrid quantum-classical approaches for scalable computations The Izmaylov Research Group actively recruits graduate students and postdoctoral researchers, with opportunities through NSERC USRA, CQIQC, CHMD90/91, CHM499Y/PHY479Y courses, and Mitacs Globalink programs. Current research directions emphasize quantum computing implementations for chemical dynamics and surface interactions.

Niv Dayanمشاهده پروفایل

استادیار

• Database Systems
• Data Structures
• Algorithms
• +۶ مورد دیگر

Niv Dayan is an Assistant Professor in the Department of Computer Science at the University of Toronto, holding this position since 2023. His research focuses on designing efficient data structures and algorithms for modern hardware, with applications in databases, cryptography, machine learning, and virtual reality. He collaborates closely with industry partners to bridge theoretical advancements to real-world systems. Education: PhD in Computer Science (IT University of Copenhagen, 2015), MSc in Software Development (IT University of Copenhagen, 2012), BSc in Computing and Economics (University of Dundee, 2010). Prior to his faculty position, he held roles as a Research Scientist at Pliops (2019–2022) and postdoctoral researcher positions at Harvard (2015–2019) and Copenhagen University (2022). Research Interests: Data structures for modern hardware, LSM-trees, key-value stores, probabilistic filters (e.g., Bloom, Cuckoo), and adaptive storage systems. His work emphasizes practical implementations and rigorous experimentation, addressing challenges in scalability, efficiency, and robustness. Teaching: Instructors for courses CSC443/CSC2234 (Database System Technology) and CSC2525 (Research Topics in Database Management), emphasizing hands-on projects, paper presentations, and research-oriented learning. Courses require strong programming skills in C/C++, Rust, or Java. Awards: Received the Best of SIGMOD 2017 award for the paper Monkey: Optimal Navigable Key-Value Store . His work has been cited as one of the best four papers at conferences, reflecting its impact on database systems research. Lab and Collaborations: Leads a research lab focused on advancing database system fundamentals. Collaborates with industry on projects like Pliops' storage solutions and startup partnerships to ensure practical relevance of his research.

David Kristjanson Duvenaudمشاهده پروفایل

دانشیار

• Artificial Intelligence
• Machine Learning
• AGI Governance
• +۳ مورد دیگر

David Duvenaud is an Associate Professor at the University of Toronto, affiliated with the Faculty of Arts and Science. He is a founding member of the Vector Institute and holds a Schwartz Reisman Chair in Technology and Society. His research focuses on AGI governance, existential risk mitigation, and probabilistic machine learning. Previously, he conducted postdoctoral research at Harvard and earned his Ph.D. from the University of Cambridge under advisors Carl Rasmussen and Zoubin Ghahramani. Education: Ph.D. (University of Cambridge), M.Sc. (University of British Columbia) Research Interests: Neural ODEs, AI ethics, probabilistic modeling, and scalable generative systems. His work bridges foundational machine learning theory with real-world applications, including chemical design and alignment science. Key contributions include LLM Processes, sabotage evaluations for frontier models, and neural ODE frameworks. He teaches advanced machine learning courses (e.g., STA414, CSC2547) and advises students like James Requeima and Raymond Douglas. His awards include a Sloan Fellowship and distinguished paper recognitions. Current projects address systemic risks posed by incremental AI progress, emphasizing long-term societal impacts and governance mechanisms.

Ricardo Baptistaمشاهده پروفایل

استادیار

• Probability
• Statistical Computing
• Measure Transport
• +۵ مورد دیگر

Ricardo Baptista is an Assistant Professor at the University of Toronto's Department of Statistical Sciences and a Faculty Affiliate at the Vector Institute. He joined the University of Toronto in Fall 2025 after serving as an instructor at Caltech in Computing and Mathematical Sciences since 2022. Education: PhD in Computational Science and Engineering from MIT (advised by Youssef Marzouk) BASc in Engineering Science from University of Toronto Dr. Baptista's research focuses on developing mathematical foundations for probabilistic machine learning models with applications to science and engineering problems. He specializes in computational measure transport, with particular interest in generative models, inverse problems, and statistical computing. His work bridges theoretical statistics with practical applications in complex systems. His recent publications demonstrate a strong emphasis on the mathematical underpinnings of machine learning, particularly in contrastive learning frameworks, conditional simulation techniques, monotone transport maps, and nonlinear ensemble filtering methods. These works reveal a consistent theme of developing rigorous theoretical frameworks for probabilistic modeling while maintaining practical applicability to real-world problems. Dr. Baptista has collaborated with prominent researchers including Andrew Stuart, Houman Owhadi, and Youssef Marzouk, and has presented his work at significant venues such as the CIRM-Marseille Digital Twins for Inverse Problems Workshop and the USNCCM Large-Scale TTA colloquium. Prior to his faculty position, he was a von Kármán instructor at Caltech and a Postdoctoral Scientist at Amazon Search.

Brendan Freyمشاهده پروفایل

استاد

• Machine Learning
• Bioinformatics
• Genomics
• +۳ مورد دیگر

Brendan Frey is a Professor in the Departments of Communications and Biomedical Engineering at the University of Toronto, cross-appointed in the Banting and Best Department of Medical Research, the Centre for Cellular and Biomolecular Research, and the Department of Computer Science. He holds the Edward S. Rogers Sr. Endowed Chair in Engineering and is a Canada Research Chair (Tier 1). His research focuses on developing machine learning algorithms for addressing challenges in genome biology, including gene expression analysis, alternative splicing inference, and computational modeling of biological systems. Notable contributions include the sum-product algorithm, affinity propagation, and image epitomes techniques. Frey’s awards include the John C Polanyi Award, NSERC Steacie Fellowship, and Fellowships from AAAS and IEEE. He advises six graduate students and two postdoctoral fellows, many of whom have become prominent academics and industry leaders. Education: Studies in Canada, United States, and United Kingdom (specific degrees not listed). Consulting: Technical Advisor to Microsoft Research India and consultant for international companies.

Paul Fieguthمشاهده پروفایل

استاد

• Machine Learning
• Computer Vision
• Image Processing
• +۷ مورد دیگر

Paul Fieguth is a Professor and Associate Vice President - Academic Operations at the University of Waterloo. He holds affiliations with the Full-time Faculty, Faculty of Mathematics, and the Intelligent and Autonomous Systems research group. His work focuses on interdisciplinary areas including machine learning, computer vision, medical imaging, and deep learning techniques for solving complex engineering and biological problems. He has contributed to advancements in photoacoustic remote sensing, autonomous systems, and large-scale biodiversity datasets like BIOSCAN-5M. His research bridges theoretical foundations (e.g., pattern recognition, inverse problems) with practical applications in robotics, medical diagnostics, and environmental monitoring. Education details are not explicitly provided in the text, but his professional roles suggest advanced training in computer science and engineering disciplines. His research interests span a wide range, including but not limited to: pattern recognition algorithms, deep learning architectures, remote sensing technologies, and computational methods for medical imaging. Recent work emphasizes innovations in rail defect detection, 3D reconstruction, and biodiversity assessment through multimodal datasets. Publications from 2022–2025 highlight contributions to fields like neural network optimization, uncertainty quantification, and generative adversarial networks for medical applications. While no specific awards are listed, his prolific publication record reflects recognition in academic circles. Advising and grants sections remain underdeveloped in the provided information, though his leadership roles suggest involvement in institutional research initiatives. He is a key member of teams advancing technologies such as PARS imaging and autonomous systems at the University of Waterloo.

Kumaraswamy Ponnambalamمشاهده پروفایل

استاد

• Design Optimization under Uncertainty
• Systems Theory & Optimization
• Sustainable Development
• +۹ مورد دیگر

Kumaraswamy Ponnambalam is a Professor in the Department of Systems Design Engineering at the University of Waterloo, Canada. His academic journey includes roles at the University of Toronto, University of Ottawa, Technical University of Delft, and College of Engineering, Guindy (Chennai, India). He holds a PhD in Civil Engineering from the University of Toronto, an MSc in Engineering Hydrology from the National University of Ireland, and a BSc in Civil Engineering from Madras University. His research focuses on design optimization under uncertainty, intelligent systems applications (e.g., hydrology, health decision-making), and sustainable development. Key collaborations include projects with colleagues from diverse disciplines and industries. He has secured funding from NSERC, FEDDEV, MITACS, and others. Professor Ponnambalam has received the 2006 Faculty of Engineering Distinguished Performance Award and the 2011 Best Student Paper Award. His teaching includes courses like SYDE 531 (Design Optimization) and SYDE 631 (Time Series Modelling). Industrial consulting includes work with Telesat (satellite communication efficiency) and fault detection systems for oil refineries. His recent research emphasizes interdisciplinary challenges in water resources, food systems sustainability, and infrastructure resilience. He maintains active roles in journal reviewing and conference organization, including a 2006 IASTED session on healthcare data analytics.

• Artificial Intelligence
• Machine Learning
• Reinforcement Learning
• +۵ مورد دیگر

Adam Ścibior is an Adjunct Professor in the Department of Computer Science at the University of British Columbia (UBC), situated in the Faculty of Science. His research focuses on artificial intelligence, machine learning, and probabilistic programming, with a strong emphasis on autonomous systems, generative models, and reinforcement learning applications in autonomous driving. He contributes to developing simulation benchmarks, diffusion models, and algorithms for multi-agent behavior prediction. His work bridges theoretical advancements in probabilistic inference with practical implementations in robotics and computer vision. Key research areas include autonomous vehicle navigation, scene simulation, and improving synthetic training data realism. He has published extensively on topics like diffusion models for traffic scene generation, reinforcement learning benchmarks for autonomous systems, and conditional permutation-invariant flows. His research often involves collaboration across disciplines, integrating probabilistic programming with real-world applications. Notable contributions include the TorchDriveEnv benchmark for autonomous driving and advancements in diffusion-based generative models for video inpainting and interactive navigation. While no specific awards or grants are explicitly listed, his work reflects a focus on impactful contributions to AI and robotics. No advising relationships or lab affiliations are detailed in the provided materials.

Tal Arbelمشاهده پروفایل

استاد

• Computer Vision
• Medical Image Analysis
• Artificial Intelligence in Healthcare
• +۴ مورد دیگر

Tal Arbel is a Professor in the Department of Electrical & Computer Engineering at McGill University, affiliated with the Centre for Intelligent Machines (CIM). Their research focuses on advancing AI-driven solutions in medical imaging, particularly in computer vision and neurology applications such as MRI analysis for multiple sclerosis and brain tumor studies. Key areas include developing robust algorithms for lesion segmentation, predictive modeling using multimodal data, and improving fairness and uncertainty quantification in medical AI systems. They are also involved in optimizing deep learning techniques for clinical workflows and addressing challenges in biomedical image analysis validation. Recent work emphasizes generative models (e.g., diffusion-based synthesis), reinforcement learning for medical image generation, and counterfactual explanations for interpretable AI. Their contributions span improving diagnostic accuracy, treatment prediction, and personalized medicine through computational imaging biomarkers.