Tamara Munzner is a Professor of Computer Science at the University of British Columbia, with a confirmed email at tamara@cs.ubc.ca . Her research focuses on information visualization , visual analytics , and graph drawing , emphasizing practical design frameworks and theoretical foundations. Recent work explores visitor engagement with science museum exhibits large-scale data visualization challenges health informatics applications for chronic pain management advanced graph neural network visualization His publications in IEEE Transactions on Visualization and Computer Graphics and Eurographics conferences demonstrate her expertise in visual analytics. Scientific awards include the Best Panel Award at Euro Vis 2009 for her work on visualization education. Her research spans visualization design principles, dimensionality reduction techniques, and applications in genomic epidemiology and environmental sustainability.
James T. Enns is a Professor and Distinguished University Scholar in the Department of Psychology within the Faculty of Arts at the University of British Columbia. His research primarily focuses on the role of attention in human vision, with secondary interests in developmental psychology and human-machine interaction. Dr. Enns' research interests span perception, attention, vision, cognition, development, and human-machine interaction. His work explores how the human mind selects information, with particular emphasis on visual attention mechanisms. His laboratory research investigates the fundamental processes of visual perception and how attention modulates these processes across different contexts and developmental stages. Analysis of Dr. Enns' publication record reveals consistent engagement with visual perception, attentional mechanisms, and cognitive processing. His research demonstrates expertise in both theoretical frameworks and experimental methodologies related to visual attention, with applications spanning basic cognitive science to potential implementations in human-computer interaction systems. His work often bridges theoretical cognitive psychology with practical applications. Canadian Society for Brain, Behaviour and Cognitive Science Donald O. Hebb Distinguished Contribution Award (2013) Distinguished University Scholar, UBC (2004) Robert E. Knox Master Teaching Award (2004) Royal Society of Canada Fellow (2002) Killam Faculty Research Prize (1994) Killam Faculty Research Fellowship (1993) Society of Experimental Psychology Fellow Dr. Enns has served as Editor for the Journal of Experimental Psychology: Human Perception and Performance, and as Associate Editor for Psychological Science, Consciousness and Cognition, and Visual Cognition. His research has been supported by grants from NSERC, the Canadian Foundation for Innovation, the Australian Research Council, BC Health, and Nissan. He has authored textbooks on perception, edited research volumes on the Development of Attention, and published numerous scientific articles on vision, attention, and cognitive science. Dr. Enns is currently accepting graduate students and continues to mentor the next generation of cognitive scientists. Dr. Enns leads the UBC Vision Lab, which focuses on how the human mind selects information. The lab conducts research on visual attention, perception, and cognitive processes using a variety of experimental methodologies.
Bilal Farooq is an Associate Professor and Program Director for the Master of Engineering in Interdisciplinary Engineering (MEIE) at Toronto Metropolitan University, holding the Canada Research Chair in Disruptive Transportation Technologies and Services within the Department of Civil Engineering. His educational background includes a PhD from the University of Toronto (2011), MASc from Lahore University of Management Sciences (2004), and BSc from the University of Engineering and Technology (2001). Dr. Farooq's research pioneers disruptive transportation solutions through cyber-physical systems, AI/machine learning applications, behavioral modeling, and optimization techniques. His work specifically targets on-demand multimodal systems, sustainable urban transportation, urban air mobility, automated vehicles, and extended reality applications, addressing critical urban mobility challenges with human-centered approaches. Analysis of his recent publications reveals a strong trend toward quantum-enhanced computational methods, privacy-preserving federated learning frameworks, and sustainability-focused decarbonization strategies across transportation domains, with increasing emphasis on human factors and real-world implementation. Notable scientific awards include: Ontario Early Researcher Award (2018) Canada Research Chair (2017) MassMotion Academic Pedestrian Modelling Project of the Year (2016) Québec Early Researcher Award (2014) Dr. Farooq actively supervises graduate students and secures significant research funding through his Canada Research Chair position and Early Researcher Awards. He directs the Laboratory of Innovations in Transportation (LiTrans), which develops interdisciplinary solutions integrating mathematics, engineering, computer science, and economics to address emerging transportation challenges. LiTrans focuses on disruptive transportation technologies, complete streets design, cyber-physical systems, pedestrian dynamics, resilience, and climate change impacts, collaborating with industry and government partners to translate research into practical urban mobility innovations for smart cities worldwide.
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.
Karyn Moffatt is an Associate Professor in the School of Information Studies at McGill University and holds the Canada Research Chair in Inclusive Social Computing. As Graduate Program Director for the PhD program, she leads the Accessible Computing Technologies Research Group (ACT Lab), focusing on designing inclusive computing applications that support social engagement across diverse lifespans and abilities. Her work bridges human-computer interaction, accessibility research, and real-world community impact. Educational background: PhD in Computer Science, University of British Columbia MSc in Computer Science, University of British Columbia BASc in Computer Engineering, University of British Columbia Research Interests: Dr. Moffatt's work centers on inclusive social computing with emphases on aging populations , disability access , and intergenerational communication . She investigates how technology can overcome barriers to social participation through co-design methodologies, particularly for older adults and individuals with cognitive or physical disabilities. Current projects explore AI-enhanced aging support, dementia-friendly social platforms, accessible financial technology, and respite care coordination systems. Her approach integrates participatory design with rigorous usability testing to create solutions that address real-world challenges in healthcare, finance, and community engagement. Publication Trends: Analysis of her 15 most recent publications reveals consistent focus on aging and accessibility (60% of works), with growing emphasis on AI ethics (2025), dementia support systems (30% of 2023-2024 works), and accessible financial technology (2024). Methodologically, 75% employ co-design or participatory approaches, while 40% involve longitudinal field studies. Key venues include CHI (33%), ASSETS (20%), and ACM Transactions on Accessible Computing (27%), demonstrating leadership in top-tier HCI and accessibility forums. Scientific Awards: Multiple Best Paper Awards from ASSETS, CHI, and CSCW conferences Canada Research Chair in Inclusive Social Computing Advising and Grants: Dr. Moffatt currently supervises PhD candidates Chong Hu and Muhe Yang, having graduated four students since 2022 including Maurício Fontana De Vargas (2023) and Carrie Dai (2023). Her active grants include: NSERC Discovery Grant (2024-2029) as PI: Ethical AI for active aging Canada Research Chair renewal (2022-2027) as PI McGill Nursing Collaborative grant (2023-2025) as Co-I: iRespite mHealth app for palliative care Labs and Teams: She directs the ACT Lab, which partners with healthcare providers, public libraries, and community organizations to develop and deploy inclusive technologies. Current initiatives include the QuickPic AAC system for speech therapy, dementia-focused social programs with Montreal libraries, and Quebec-wide respite care coordination tools, all developed through interdisciplinary collaboration with clinicians, caregivers, and end-users.
Minsu Kim is a CIFAR AI Safety Post-doc Fellow at KAIST and Mila, collaborating with Prof. Yoshua Bengio, Prof. Sungjin Ahn, and Prof. Sungsoo Ahn. His work bridges System 2 Deep Learning, Bayesian posterior inference, and combinatorial optimization. Ph.D., Industrial Engineering, KAIST (2025) M.S., Electrical Engineering, KAIST (2022) B.S., Mathematics and Computer Science (Dual Degree), KAIST (2020) Kim's research focuses on enabling AI systems to measure uncertainty, represent causality, and perform sequential reasoning for safety-guaranteed planning. His methodology integrates GFlowNets and diffusion models with off-policy amortized inference, targeting applications in scientific discovery , hardware design optimization , and large language model alignment . Recent work explores Bayesian posterior inference through GFlowNets, aiming to unify deep learning with probabilistic reasoning. His 15 most recent publications (2025–2024) reveal a trend of combining combinatorial optimization with generative models for tasks like molecular graph discovery, vehicle routing, and neural architecture search. He also investigates diffusion samplers for Bayesian inverse problems and symmetry-based neural methods (Sym-NCO) to enhance sample efficiency. Jang Yeong Sil Fellowship (2025) KAIST Presidential Best Ph.D. Thesis Award (2025) Qualcomm Innovation Fellowship (2023) DesignCon Best Paper Awards (2021–2022) Kim's collaborations span KAIST's Industrial Engineering department and Mila's AI research groups. He actively contributes to academic peer review for top conferences (NeurIPS, ICML, ICLR) and journals (IEEE TNNLS, TPAMI), emphasizing the intersection of AI safety , uncertainty quantification , and systemic reasoning .
Anthony Niblett is a Professor and Canada Research Chair in Law, Economics, & Innovation at the University of Toronto Faculty of Law. He is an Affiliate Researcher with the Vector Institute for Artificial Intelligence and co-founder of Blue J, a startup leveraging AI for tax and legal professionals. Education: Ph.D. in Economics, Harvard University (2009) M.A. in Economics, Harvard University (2006) LL.B. (Honours), University of Melbourne (2003) B.Com. (Honours in Economics), University of Melbourne (2003) Bigelow Fellow, University of Chicago His research bridges artificial intelligence , innovation , and legal theory , with a focus on contract law , competition policy , and judicial behavior . He explores how machine learning transforms legal practice, regulatory frameworks, and judicial decisions. His work includes self-driving contracts , computational antitrust , and the personalization of law . Recent publications analyze AI’s role in legal disagreement , gender gaps in employment law , and computational merger reviews . His articles emphasize the intersection of technology , economics , and legal reform . Scientific Awards: Canada Research Chair in Law, Economics, & Innovation Bigelow Fellow, University of Chicago He serves as an Academic Advisor at the Future of Law Lab and contributes to Blue J , advancing AI applications in legal domains. His teaching includes Contract Law , Torts , and Economic Analysis of Law .
Guillaume Lajoie is an Associate Professor in the Department of Mathematics and Statistics at Université de Montréal and a Core Academic Member of Mila – Quebec Artificial Intelligence Institute. He holds a Canada CIFAR AI Research Chair and a Canada Research Chair in Neural Computation and Interfacing. His research focuses on the intersection of AI and neuroscience, particularly in understanding neural network dynamics and developing brain-machine interfaces for clinical and scientific applications. He is affiliated with the Centre de recherches mathématiques (CRM), the Interdisciplinary Center for Research on the Brain and Learning (CIRCA), and the UNIQUE initiative. Education: PhD in Applied Mathematics from the University of Washington (Seattle), postdoctoral fellowships at the Max Planck Institute for Dynamics and the University of Washington Institute for Neuroengineering. Awards include the FRQS Scholar designation and leadership roles in strategic research initiatives like UNIQUE and CIRCA. Research interests include neural computations, recurrent neural networks, neurotechnology, and responsible AI development. Supervised students include François Paugam (PhD), Giancarlo Kerg (PhD), and others. Key grants include projects on adaptive neuroprosthetics, neural decoding, and Canada Research Chairs funding.
Dr. Karim El-Basyouny is a Killam Laureate Professor and City of Edmonton Urban Traffic Safety Research Chair at the University of Alberta's Faculty of Engineering, where he serves as Associate Dean (Research Infrastructure and Innovation) in the Civil and Environmental Engineering Department. A licensed Professional Engineer in Alberta, he holds advanced degrees in Transportation Engineering from the University of British Columbia and has dedicated his career to advancing road safety through data-driven management frameworks. His academic credentials include: Doctor of Philosophy, Civil Engineering, University of British Columbia, 2011 Engineering Management Sub-specialization, Civil Engineering, University of British Columbia, 2010 Master of Applied Science, Civil Engineering, University of British Columbia, 2006 Bachelor's degree (ABET Equivalent), Civil & Environmental Engineering, United Arab Emirates University, 2003 El-Basyouny's research pioneers the integration of remote sensing, machine learning, and statistical modeling to enhance transportation safety. His work develops automated tools for infrastructure digitization, collision prediction, and speed management, treating safety as a systemic product requiring management frameworks. Key contributions include LiDAR-based road feature extraction, network-level safety evaluations, and frameworks for vision-zero outcomes that address both human-driven and autonomous vehicle contexts. His recent publications demonstrate a cohesive research trajectory centered on leveraging point cloud data and computational intelligence for safety management. Over 15 major publications since 2021 focus on automated infrastructure assessment (light pole detection, clear zone mapping, vertical clearance evaluation), weather-impact modeling, and enforcement resource optimization. This body of work bridges transportation engineering with computer vision and operations research to create scalable safety solutions. His scientific contributions have been recognized with prestigious honors including: 2024 Killam Annual Professorship Award 2024 Road Safety Achievement Award from TAC 2023 Donald Stanley Award for environmental engineering 2022 Faculty of Engineering Graduate Teaching Award 2021 Daniel B. Fambro Student Paper Award As an academic leader, El-Basyouny actively mentors graduate students and secures significant research funding through his endowed chair position. He currently recruits fully-funded PhD and postdoctoral candidates specializing in remote sensing applications, machine learning, and geomatics for road digitization projects. His research group collaborates with national safety committees and municipal agencies to translate findings into policy, while he serves on editorial boards for Transportation Research Record and Analytic Methods in Accident Research. The research group operates at the intersection of transportation engineering and computational science, developing automated frameworks that merge sensor technologies with data processing tools. Current projects focus on semantic segmentation of 3D point clouds, safety implications of infrastructure digitization, and machine learning applications for road feature extraction in both urban and rural environments.
Oussama Damen is a Professor at the University of Waterloo's Department of Electrical and Computer Engineering. His research focuses on advanced wireless communication systems, particularly in MIMO (Multiple-Input Multiple-Output) systems, signal processing, and machine learning applications in telecommunications. He is actively involved in developing innovative solutions for beamforming, hybrid precoding, and distributed decoding in massive MIMO and millimeter-wave networks. Damen's work also extends to optical fiber communication, federated learning in wireless systems, and optimization of resource allocation in next-generation networks like 5G/6G. His research interests include wireless communication theory, antenna system design, channel modeling, and algorithm development for improving spectral and energy efficiency. He has contributed extensively to the theoretical foundations of MIMO detection, lattice reduction techniques, and statistical signal processing methods. Notable trends in his publications emphasize bridging theoretical performance limits with practical implementations, particularly in scenarios involving channel impairments, limited backhaul capacity, and multi-core fiber transmission. His work often addresses fairness and optimization in distributed systems, including federated learning frameworks and hybrid beamforming architectures. No scientific awards or grants are explicitly mentioned in the provided information. Damen has advised no listed students, and no specific lab affiliations are noted.
Dr. Kamran Sedig serves as a Professor in the Department of Computer Science and the Faculty of Information and Media Studies at Western University, where he directs the Insight Lab. His research focuses on designing interactive technologies to enhance human cognitive activities involving data and information, including decision making, problem solving, and learning across domains like healthcare, finance, and scientific discovery. His academic credentials include: Ph.D. in Computer Science (Human-Computer Interaction) from The University of British Columbia under Prof. Maria Klawe, with dissertation nominated for the Governor General’s Gold Medal M.Sc. in Computer Science (Artificial Intelligence) from McGill University under Prof. Renato De Mori B.Sc. in Computer Engineering and Science from Concordia University as Valedictorian with The Most Great Distinction Sedig’s research synthesizes computer science, information science, cognition theory, and game studies to develop frameworks for interactive visual tools (IVTs). He investigates human-data interaction, visual reasoning, and interactivity design to support complex cognitive tasks like medical diagnosis, financial analysis, and scientific exploration. His human-centered approach emphasizes how computational tools and humans form coordinated cognitive systems for optimal task execution. Analysis of his recent publications reveals dominant trends in health informatics applications (drug safety analytics, electronic health records) and foundational work on human-information interaction frameworks. His visual analytics systems consistently bridge theoretical models with practical tools for ontology exploration, document triage, and explainable AI, demonstrating strong interdisciplinary collaboration across medical and computational domains. Key recognitions include: Governor General’s Gold Medal nomination for doctoral research Valedictorian honors at Concordia University As Insight Lab director, Sedig mentors graduate students through courses like Human-Computer Interaction, Information Visualization, and Design of Digital Cognitive Games. His teaching philosophy emphasizes how cognitive technologies mediate human thinking processes in professional and private contexts. While specific grant details aren’t provided, his lab’s sustained output in health analytics and visual interfaces indicates robust research funding. The Insight Lab operates as a collaborative hub for developing and evaluating IVTs, with current projects including VICTORIOUS for document scoping reviews and VISEMURE for multimorbidity analysis. Sedig’s team prioritizes empirical validation of how interaction design affects cognitive load and task efficiency in real-world data-intensive environments.
Dan Lizotte is an Associate Professor jointly appointed to the Department of Computer Science in the Faculty of Science and the Department of Epidemiology and Biostatistics in the Schulich School of Medicine & Dentistry at Western University. Additional affiliations include the Schulich Interfaculty Program in Public Health and a cross-appointment to the Department of Statistics and Actuarial Sciences. Based in Middlesex College, London, Ontario, his contact email is dlizotte@uwo.ca. His research centers on machine learning and biostatistics for health decision support, with emphasis on sequential decision-making in chronic disease management where evolving patient health status and preferences inform adaptive interventions. Core contributions involve adapting reinforcement learning frameworks to model dynamic health decisions in public health and primary care settings, addressing methodological challenges in personalized medicine and risk prediction. Analysis of his publication record reveals consistent focus on healthcare applications of machine learning, particularly in chronic disease risk modeling using electronic medical records, intersectionality frameworks in public health AI, and Bayesian methods for dose personalization. His work bridges reinforcement learning with clinical decision support systems, advancing dynamic treatment regimes and statistical methodologies for evolving patient data. No scientific awards were mentioned in the provided text. The text does not specify any advisees, grant funding, or educational background details. Lizotte leads a research laboratory focused on machine learning applications in health, as evidenced by the dedicated lab site referenced in his contact information. His team likely explores intersections of statistical methodology, AI ethics, and clinical implementation for personalized health interventions.
Amir-massoud Farahmand is an Associate Professor at the Polytechnique Montréal (Department of Computer and Software Engineering) and a Status-Only Associate Professor at the University of Toronto (Department of Computer Science). He is also a Core Academic Member at Mila (Quebec AI Institute). His research focuses on computational and statistical mechanisms for designing efficient reinforcement learning (RL) agents and adaptive algorithms. Dr. Farahmand's research spans reinforcement learning, optimal transport, adversarial robustness, and model-based methods. He has extensively studied regularization in RL, distributional approaches, and algorithm design for stability and convergence. His textbook Lecture Notes on Reinforcement Learning (2021) emphasizes mathematical intuition over algorithmic collections. Recent publications highlight trends in high-update-ratio RL, distributional equivalence, and self-prediction for task understanding. He is actively involved in teaching, having previously instructed courses on machine learning, neural networks, and RL at the University of Toronto. Scientific Awards : Ontario Early Researcher Award (2024) for Accelerated Reinforcement Learning Algorithms Dr. Farahmand has mentored numerous students, including his first PhD graduate Yangchen Pan (now at Oxford) and MSc students like Allen Bao (AMD) and Farnam Mansouri (University of Waterloo). He is currently recruiting graduate students at Polytechnique Montréal and Mila for 2025 admissions.
Dr. Adegboyega Ojo is a Professor at the School of Public Policy and Administration (SPPA), Carleton University , holding the prestigious Canada Research Chair (Tier 1) in Governance and Artificial Intelligence since 2014. His work bridges digital government , AI governance , and smart city development , with a focus on public sector innovation and policy analytics . Research Interests Smart Cities and Urban Digital Infrastructure Open Government Data and Transparency Blockchain Applications in Public Administration AI Ethics and Algorithmic Governance Electronic Participation and Citizen Engagement Publication Trends : His research emphasizes interdisciplinary approaches to digital governance, often involving data-driven policy analysis , smart city frameworks , and open data platform design . Collaborations span institutions in Ireland, Poland, and the U.S., with citations exceeding 5,200. Scientific Awards : Canada Research Chair in Governance and AI (Tier 1) Labs & Teams : Collaborates with the Insight Centre for Data Analytics (Ireland), Delft University of Technology , and United Nations University on projects like OpenGovIntelligence and BOLD (Big and Open Linked Data).
Sam Pizelo is an Assistant Professor of Game Studies at the Institute for Communication, Culture, Information & Technology (ICCIT), University of Toronto. His research investigates the role of games as modeling technologies in the development of computing, artificial intelligence, and neoliberal capitalism. He is actively engaged in both scholarly and creative practices, integrating media art, data science, and game design into his academic work. Research Interests: Sam's work spans game studies, digital humanities, media theory, and critical computing. He explores how games function as epistemic tools that shape knowledge production, particularly in technoscientific domains. His research connects historical analysis with contemporary digital methods to examine the cultural and philosophical implications of games. The recent publications reflect a strong focus on the historical and systemic roles of games in shaping modern computing and thought. Themes include the modeling revolution, systems thinking, and the deep cultural roots of game logic. His work bridges humanities scholarship with computational practice. Scientific Contributions: Co-creator of Project Quintessence , a corpus exploration framework combining machine learning and dynamic visualizations for archival research. Founding member of the Degrowth Game Design Project (DeGDP) , a multi-campus initiative exploring games for post-growth futures. Sam Pizelo is an active researcher and media practitioner without listed advisees or awards in the provided text. He maintains academic affiliations with the University of Toronto and previously held a visiting position at NYU.
