Find Academic Supervisors

• Measurement
• Applied and Theoretical Research Methodology
• Spatio-Temporal Modelling
• +3 more

Dr. Ed Kroc is an Associate Professor at the University of British Columbia (UBC) within the Department of Educational and Counselling Psychology, and Special Education in the Faculty of Education. Specializing in the Measurement, Evaluation, and Research Methodology (MERM) program, he joined UBC in 2018 after completing his PhD in Mathematics at UBC, M.Sc. in Applied Mathematics in Statistics, and B.Sc. in Mathematics at DePaul University. Education: PhD (UBC, 2015), M.Sc. (DePaul, 2007), B.Sc. (DePaul, 2006) Courses Taught: Experimental Designs, Correlational Analysis, Meta-Analysis, Causal Inference, Bayesian Methods, Multivariate Analysis His scholarly interests focus on measurement theory, causal inference, spatio-temporal modeling, and urban ecology. Recent research explores the life cycles of urban-nesting Glaucous-winged Gulls, measurement error models, and statistical methodology. His publications span psychology, ecology, mathematics, and interdisciplinary research. Dr. Kroc’s work emphasizes methodological rigor, including reliability estimation, polynomial regression techniques, and assessment protocols for motor skill evaluation. He advocates for multivariate thinking in subscale cutoff determination and challenges traditional measurement frameworks like Stevens’ scales. Contact: ed.kroc@ubc.ca | Office: Scarfe Office Block 2527

Sarah Dickson-AndersonView profile

Professor

• Hydrogeology
• Fractured rock systems
• Water security
• +5 more

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.

Yongqin LeiView profile

Assistant Professor

• behavioral operations management
• two-sided platforms
• operations-marketing interface

Yongqin Lei serves as an Assistant Professor in Operations and Supply Chain Management at the University of Winnipeg, where he teaches undergraduate courses including Introduction to Business (BUS-1201), Operations Management (BUS-2501), and Advanced Prescriptive Analytics (GBUS-7235). Previously, he instructed Competing with Analytics at Ivey Business School with a teaching rating average of 6.64/7. His academic credentials include a BBA with Mathematics minor from Dalhousie University and a PhD in Management Science from the University of Western Ontario. Lei's research centers on behavioral operations management, specifically examining how psychological factors influence operational decisions in two-sided platforms and marketing interfaces. His work investigates corporate wellness incentives, probabilistic pricing mechanisms, and marketplace dynamics affected by counterfeits and fake reviews, revealing critical interactions between consumer behavior and operational strategy. His scholarly output demonstrates consistent focus on behavioral aspects of platform economics and pricing innovation, with recent publications spanning credence goods markets, probabilistic promotions, and corporate wellness programs. This body of work bridges operations research with marketing and behavioral science through rigorous analytical modeling. His scientific recognition includes: Graduate Fellowship (2022, 2023) John F. Rankin Doctoral Scholarship (2021) Ivey Plan for Excellence PhD Fellowship (2020-2024) Dalhousie In-Course Scholarship Finalist, 2023 DSI Doctoral Research Showcase Award Lei actively contributes to academic service as an ad hoc reviewer for Production and Operations Management and International Journal of Production Research, while mentoring Ivey PhD students. His conference presentations at INFORMS, DSI, POMS, and CORS reflect ongoing engagement with the operations research community.

Hao YuView profile

Professor

• Statistical Computing
• Parallel Programming
• Financial Time Series
• +4 more

Hao Yu is a full-time Professor in the Department of Statistical and Actuarial Sciences at the University of Western Ontario . Holding a Ph.D. from Carleton University (1993), he specializes in statistical computing, financial time series analysis, and stochastic modeling. Educational Background Ph.D., Carleton University (1993) Research Interests focus on computational statistics, time series analysis, and probabilistic modeling. His work includes developing statistical algorithms for financial applications ( NSERC-funded 2013–2018) and theoretical advancements in residual analysis for ARMA/GARCH models. Academic Contributions include co-developing R packages for time series analysis, advancing maximum likelihood unit root tests, and creating novel methods for heavy-tailed risk factor simulation in finance. His research intersects computational methodology with financial applications. Scientific Awards are not explicitly mentioned in the provided text. Advising notable students: Yifan Li (PhD) Xixian Liu (MSc Financial Mathematics) Xiyang Zhang (MSc Thesis Track in Statistical Sciences)

• Differential-Algebraic Equations (DAEs)
• Interval Methods
• Structural Analysis
• +7 more

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
• +6 more

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 IzmaylovView profile

Professor

• Quantum Dynamics
• Theoretical Chemistry
• Quantum Computing
• +5 more

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.

Daphna HellerView profile

Associate Professor

• Psycholinguistics
• Semantics
• Pragmatics
• +4 more

Daphna Heller serves as Associate Professor in the Department of Linguistics at the University of Toronto, located in Sidney Smith Hall, Room 4088. Her academic work bridges theoretical linguistics with experimental methodologies to investigate core aspects of human language processing. Research interests focus on psycholinguistics , experimental semantics/pragmatics , and referential processing . Key specialties include perspective-taking in communication, probabilistic models of reference, language processing in understudied languages (particularly Hebrew and Niuean), and the cognitive mechanisms underlying definite descriptions. Her work integrates theoretical linguistics with experimental paradigms including eye-tracking, corpus analysis, and cross-linguistic comparison. Recent publications reveal strong trends in perspective integration theory and referential choice mechanisms , with increasing focus on mental state attribution in communication. The 2020-2025 publications demonstrate methodological diversity spanning psycholinguistic experimentation, cross-linguistic analysis of Niuean and Hebrew, silent gesture studies, and computational modeling of pragmatic phenomena. 2022 Faculty Research Fellow, Jackman Humanities Institute Heller's research program examines how speakers and listeners manage reference in context, with particular attention to the probabilistic weighing of multiple perspectives. Her work on Hebrew linguistics contributes to understanding Semitic language processing, while Niuean studies provide insights into ergative language comprehension. Current projects appear to extend the Multiple Perspectives Theory to question-answer dynamics and mental state attribution in communication.

Niv DayanView profile

Assistant Professor

• Database Systems
• Data Structures
• Algorithms
• +6 more

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.

Chris GloverView profile

Professor

• Environmental Physiology
• Aquatic Toxicology
• Environmental Science
• +3 more

Dr. Chris Glover is a Professor and Campus Alberta Innovation Program (CAIP) Research Chair in Hydroecology and Environmental Health at Athabasca University's Faculty of Science and Technology. He has held prior roles including Senior Scientist at Norway's Institute of Marine Research, Group Leader at Scion Research (New Zealand), and faculty at the University of Canterbury. Currently, he serves as Editor-in-Chief of Comparative Biochemistry and Physiology B and holds an adjunct position at the University of Alberta. His educational background includes a B.Sc. (Hons I) in Animal Physiology from the University of Canterbury, an M.S. in Biology from the University of Kentucky, and a Ph.D. in Environmental Science from King's College London. Research focuses on environmental physiology and aquatic toxicology, with expertise in organismal responses to environmental stressors. Key research interests include aquatic toxicology, environmental physiology of fish, and the application of innovative tools like eDNA and environmental RNA for ecological assessments. His work addresses UN Sustainable Development Goals related to clean water and responsible consumption. Dr. Glover has authored over 140 publications, with recent work emphasizing eDNA applications, ion regulation in fish under acidic conditions, and probabilistic hazard assessments for cyanobacterial toxins. He collaborates internationally, with active research in Arctic ecosystems and coal mine-impacted waters. Professional affiliations include the Athabasca River Basin Research Institute (ARBRI) and editorial leadership in comparative physiology. His research bridges fundamental biological understanding with applied environmental challenges, contributing to both academic and practical solutions.

• Neuromorphic Computing
• Vector Symbolic Architectures
• Reinforcement Learning
• +7 more

Michael Furlong is an Adjunct Assistant Professor at the University of Waterloo. His research bridges neuroscience and computer science, focusing on neuromorphic computing , vector symbolic architectures , and autonomous robotic systems . He has contributed to frameworks like Neurobench for evaluating neuromorphic algorithms and developed models for cognitive processes such as visual attention and action specification. Email: michael.furlong@uwaterloo.ca Research Interests : His work explores biologically plausible computation , spiking neural networks , and Bayesian optimization . Publications highlight autonomous exploration , terrain classification , and information-gathering strategies for planetary missions. Collaborative projects emphasize fair benchmarking and robust adaptive recovery in robotic systems. Recent Trends : Recent articles focus on hyperdimensional computing , probabilistic neuromorphic programming , and multi-modal active perception . These studies integrate category theory , dynamic modeling , and semantic processing to advance neuromorphic hardware and cognitive architectures. Key Collaborations : Participated in planetary exploration initiatives, including lunar rover simulation and icy moon landing site selection , combining Wald's sequential probability ratio test for fault tolerance and neural predictive control for robotic chassis reconfiguration.

Gordon SavageView profile

Professor

• Systems Theory
• Reliability Engineering
• Quality Design
• +9 more

Gordon Savage is a Professor in the Department of Systems Design Engineering at the University of Waterloo. He holds a PhD, MASc, and BASc from the University of Waterloo, completing his doctorate in 1976. His research focuses on systems theory applications to continuum problems, reliability engineering, and quality design methodologies for complex systems. Doctorate (PhD): University of Waterloo, 1976 Master's (MASc): University of Waterloo, 1970 Bachelor's (BASc): University of Waterloo, 1968 Research interests include non-linear media modeling, coupled fields, probabilistic boundary conditions, and optimization methods. He develops unified frameworks for multi-disciplinary systems design that integrate safety and quality objectives using advanced probability theories. Recent work emphasizes reliability-based robust design optimization and hybrid approaches combining Six Sigma with second-moment analysis. Selected publications (2015-2016) address reliability analysis of mechanical systems, frequency-domain performance metrics, and stochastic optimization under uncertainty. These contributions advance methodologies for quantifying system performance in uncertain environments. Professor Savage currently supervises graduate students and teaches courses like SYDE 252 (Linear Systems), SYDE 351 (Systems Models), and SYDE 661 (Model-Based Robust Design).

• Artificial Intelligence
• Machine Learning
• Reinforcement Learning
• +5 more

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.

Michael JanzenView profile

Associate Professor

• Virtual Camera Selection
• Serious Game Modules
• Camera Trap Image Processing
• +3 more

Dr. Michael Janzen is an Associate Professor in the Department of Computing Science at The King's University. He holds a PhD from the University of Saskatchewan, an M.Sc. from the University of Guelph, and a B.Sc. from Brock University. His research focuses on virtual camera selection using semi-ring constraint satisfaction problems (SCSPs), serious game modules, and camera trap image processing for ecological studies. His work intersects computer science, environmental monitoring, and AI ethics. Education includes: PhD in Computing Science, University of Saskatchewan M.Sc. in Computer and Information Science, University of Guelph B.Sc. in Computers and Physics, Brock University Research interests emphasize practical applications of AI and computational methods in virtual environments, wildlife monitoring, and educational gaming. Recent projects include developing automated systems for wildlife fence design analysis and image screening tools for ecological data. His work bridges theoretical computer science with real-world challenges in conservation and technology ethics.

Kamyar KhodamoradiView profile

Assistant Professor

• theoretical computer science
• approximation algorithms
• combinatorial optimization

Kamyar Khodamoradi is an Assistant Professor in the Department of Computer Science at the University of Regina, Faculty of Science. His research focuses on theoretical computer science, particularly approximation algorithms and combinatorial optimization, where he designs algorithms with mathematically provable guarantees for intractable problems. Dr. Khodamoradi earned his Ph.D. from Simon Fraser University and completed postdoctoral appointments at the University of British Columbia and several European institutions. His research explores fundamental challenges in optimization across disciplines like computational geometry, graph theory, and resource allocation. Recent work demonstrates advanced techniques for clustering, scheduling, and network optimization problems. Publications emphasize algorithm design with theoretical rigor, frequently appearing in top-tier computer science venues.