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John Paul MindaView profile

Professor

• Cognitive Psychology
• Category Learning
• Concept Formation
• +3 more

Dr. John Paul Minda is a Professor in the Department of Psychology at Western University and an associate director of the Western Institute for Neuroscience. He holds a PhD from the University of Buffalo and was a Beckman Fellow at the University of Illinois. His research focuses on categorization, concept formation, and their influence on decision-making. He has authored influential books like *The Psychology of Thinking* and *How to Think*. Dr. Minda is renowned for his teaching excellence, receiving the 2020 Dean’s Award for Teaching and the 2023 OUSA Teaching Excellence Award. He directs the Minda Lab, a cognitive psychology group affiliated with the Brain and Mind Institute. The lab studies how people learn categories and how these concepts shape behavior. Funded by NSERC, SSHRC, and Mitacs, the lab has produced over 100 publications. Recent work explores implicit learning, cultural differences in categorization, and mindfulness interventions. Key lab members include PhD students Anthony Cruz and Chelsea McKenzie, and postdoc Priya Kalra. Dr. Minda advises over 20 graduate and undergraduate students, mentoring through grants and collaborative projects. His teaching emphasizes accessibility and innovation, reflected in courses on cognitive psychology. He lives in London, Ontario, and balances academia with running and family life.

• Geophysics
• Inverse Theory
• Data Science
• +6 more

Lindsey Heagy is an Assistant Professor in the Department of Earth, Ocean & Atmospheric Sciences at the University of British Columbia (UBC), Faculty of Science. Her research focuses on inverse theory, machine learning, and geophysical data analysis, with applications in mineral exploration, carbon sequestration, and groundwater systems. She leads a group developing open-source software like SimPEG for geophysical simulations and inversions, alongside GeoSci.xyz for geoscience education resources. Key research areas include geophysical inversions of electromagnetic and potential field data, detection of unexploded ordnance (UXO) using machine learning, and collaboration with industry partners on carbon sequestration projects. Heagy actively recruits graduate students and postdoctoral researchers, emphasizing interdisciplinary approaches to geophysical challenges. Her work emphasizes open science practices, with contributions to open-source tools for data processing and reproducible workflows in geophysics. Recent publications highlight advancements in electromagnetic modeling, magnetic vector inversion, and the integration of neural networks in geophysical parameterization. Her lab's projects often involve large-scale datasets and cutting-edge computational methods, reflecting a commitment to both theoretical innovation and practical applications in environmental and resource geoscience.

• Artificial Intelligence
• Network Science
• Computational Biology
• +3 more

Yong Gao is a Professor of Computer Science, Data Science, and Mathematics at the University of British Columbia (UBC) Okanagan, affiliated with the Irving K. Barber Faculty of Science. He holds a PhD from the University of Alberta and leads research in algorithmic and computational problems in artificial intelligence, network science, and computational biology. His work emphasizes graph theory, probabilistic methods, and applications in social media and biological systems. Educational Background : PhD in Computer Science, University of Alberta Research Interests : Algorithmic foundations of AI and network science Graph-based methods for computational biology and social media analysis Probabilistic modeling of complex systems Awards & Grants : Recipient of multiple NSERC Discovery Grants (2006–2019) UBC Okanagan Startup Grant (2005–2008) Senior Member, Association for the Advancement of AI (AAAI) Professional Roles : Member, Centre for Optimization, Convex Analysis and Nonsmooth Analysis Graduate student supervisor Teaching : Courses in algorithm design, artificial intelligence, discrete mathematics, and network science.

Brian KengView profile

Adjunct Professor

• Artificial Intelligence
• Machine Learning
• Bayesian Statistics
• +3 more

Brian Keng is an Adjunct Professor in Data Science at the Rotman School of Management, University of Toronto, and Senior Director at RBC Borealis, a research center focused on AI solutions for financial services. At Rotman, he contributes to data science education through the Management Data and Analytics Lab and the Master of Management Analytics program. PhD, University of Toronto (Electrical and Computer Engineering) MASc, University of Toronto (Electrical and Computer Engineering) BASc, University of Waterloo (Electrical and Computer Engineering) His research focuses on Bayesian statistics, probabilistic frameworks for deep learning, and AI-driven automated decision making. He has published peer-reviewed work on industrial AI applications and holds multiple patents in AI systems. Prior roles include Chief Data Scientist at Kinaxis Inc. and Rubikloud Technologies Inc., where he led AI development for supply chain solutions.

• Transportation Systems Optimization
• Operations Research
• Machine Learning
• +7 more

Emma Frejinger is a full Professor at the Department of Computer Science and Operations Research, Faculty of Arts and Science, Université de Montréal. She holds the Canada Research Chair in Demand-Driven Optimization of Transport Systems and the CN Chair in Railway Operations Optimization. Her work bridges operations research and statistical learning to solve large-scale transportation challenges. Ph.D. in Mathematics, École Polytechnique Fédérale de Lausanne, Switzerland Her research focuses on transportation network optimization , demand forecasting , and discrete choice modeling . She develops data-driven methodologies for railway operations, EV charging infrastructure, and traffic prediction, emphasizing scalability and real-world applicability. Recent publications highlight trends in integrating machine learning with combinatorial optimization , particularly for MIPs , competitive facility location , and stochastic transport systems . These works span freight logistics , urban mobility , and reinforcement learning applications . Scientific Awards: Two-time INFORMS Transport Science and Logistics Society best Ph.D. thesis award 2017 Grand Prix d'excellence en transport (freight category) She has supervised 21+ graduate students in topics ranging from locomotive routing to electric vehicle adoption , with projects funded by NSERC, FRQ, and industry partners like CN Rail and Purolator. Her lab affiliations include CIRRELT and OPTIM , focusing on simulation and optimization.

Michel C. DesmaraisView profile

Professor

• Artificial Intelligence
• Cognitive Modeling
• Learning Environments
• +5 more

Michel C. Desmarais is a Full Professor in the Department of Computer Engineering and Software Engineering at Polytechnique Montréal, where he has been faculty since 2002. With a PhD in Psychology from Université de Montréal, his research bridges artificial intelligence, educational technology, and human-computer interaction. He holds affiliations with IVADO and LAMA-WeST research groups, and has held visiting positions at Sorbonne University, Eindhoven Technical University, and other European institutions. His research focuses on three interconnected pillars: 1) Cognitive modeling and educational data mining , developing algorithms for student knowledge assessment and adaptive learning systems; 2) AI-driven educational tools , including automated grading systems and peer instruction platforms; and 3) Recommendation systems and user modeling , particularly for personalized learning interfaces. His work consistently applies machine learning to solve practical challenges in technology-enhanced education. Analysis of his 150+ publications reveals strong trends in educational NLP (sentence similarity for short-answer grading), generative AI (LLM-generated code validation), and Bayesian modeling (Q-matrix refinement). Recent work increasingly focuses on transformer architectures and real-world educational datasets. He maintains an active supervision record, having graduated 35+ graduate students. Current PhD candidates work on NLP for educational applications (Bakhtiari, Kamdem) and AI for engineering (Wang). His teaching covers user interface design, recommender systems, and intelligent interfaces. Professional service includes editorial leadership (JEDM journal), conference co-chairing (UMAP 2017, EDM founding), and grant review panels for NSERC, MITACS, and EU programs. Industry experience includes prior roles as R&D Director at MVM Inc. and researcher at Montreal Computer Research Center.

Gale RussellView profile

Associate Professor

• Decolonization of Education
• Mathematics Education
• Ethnomathematics
• +4 more

Dr. Gale Russell serves as Associate Professor in the Faculty of Education at the University of Regina, concurrently holding leadership roles as Program Chair for Secondary Education and Subject Area Chair for Mathematics Education. Her research program is fundamentally dedicated to decolonizing mathematics education through: Interrogating impacts of Traditional Western versus Indigenous worldviews on mathematics teaching/learning Documenting polysemy between English and Aboriginal English in mathematics registers Investigating conceptual understanding of zero across cultural contexts Analyzing preservice teachers' probabilistic reasoning development Exploring ethnomathematics as a decolonizing practice Identifying factors influencing teacher education program selection This integrated work consistently centers Indigenous knowledge systems while challenging colonial frameworks in mathematics pedagogy and epistemology.