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Nicolas VermeysView profile

Professor

• Cyberjustice
• Information Security Law
• Artificial Intelligence in Law
• +6 more

Nicolas Vermeys is a Full Professor at the Université de Montréal’s Faculty of Law, serving as Director of the Centre de recherche en droit public (CRDP) and Associate Director of the Cyberjustice Laboratory. He holds advanced degrees (LL.D. and LL.M.) from Université de Montréal and is a certified CISSP (Certified Information Systems Security Professional). His work focuses on intersections between technology and law, particularly cybersecurity, AI governance, and cyberjustice innovation. Education: LL.D. and LL.M. (Université de Montréal) Affiliations: Cyberjustice Laboratory, CRDP, Regroupement stratégique Research Interests: Cyberjustice systems, AI ethics in law, information security obligations, civil liability in digital contexts, and legal frameworks for emerging technologies. He frequently advises governments, legal professionals, and international organizations on these topics. Recent Article Trends: Explores cybersecurity challenges in judiciary systems, AI’s role in judicial processes, and privacy implications of smart technologies. Key themes include ODR systems, digital evidence reliability, and regulatory strategies for autonomous systems. Grants & Projects: Lead researcher on initiatives like Lex Electronica (digital law research) and Human-Centric Cybersecurity Partnership. Co-leads projects on judicial data processing, blockchain arbitration, and AI-driven legal tools. Labs & Teams: Directs CRDP (Canada’s largest legal research center) and co-manages the Cyberjustice Lab, which develops software solutions for justice system challenges. Collaborates internationally with institutions like William & Mary Law School and the University of Fortaleza.

Erick DelageView profile

Professor

• Optimization under Uncertainty
• Operations Research
• Robust Optimization
• +7 more

Erick Delage is a Professor in the Department of Decision Sciences at HEC Montréal, holding the Canada Research Chair in Decision Making Under Uncertainty. He is a member of the Group for Research in Decision Analysis (GERAD) and an associate academic member of MILA. His research focuses on optimization under uncertainty, robust and stochastic optimization, machine learning, and risk management, with applications in finance, energy systems, and transportation. Delage holds a Ph.D. in Electrical Engineering from Stanford University, where he worked with renowned scholars like Andrew Y. Ng and Yinyu Ye. His teaching includes courses on Quantitative Risk Management, Decision Analysis, and Robust Optimization at institutions like HEC Montréal, Politecnico di Milano, and EPFL. He has supervised numerous PhD and master's students, leading to impactful contributions in areas like distributionally robust optimization and deep reinforcement learning for financial engineering. Delage's work emphasizes bridging theory and practice, with notable contributions to contextual optimization methods, energy transition pathways, and risk-averse decision-making. His research has been recognized with awards such as the Nicholson Award (2008) and membership in the Royal Society of Canada's College of New Scholars (2020). His laboratories and collaborations include the Supply Chains and Mobility research cluster funded by IVADO, focusing on data-driven decision-making for resilient systems. Key grants include leadership in energy transition optimization and robust supply chain frameworks.

Pierre-Olivier PineauView profile

Professor

• Energy policy
• Electricity markets
• Decision analysis
• +4 more

Pierre-Olivier Pineau is a Professor at HEC Montréal, holding the Chair in Energy Sector Management within the Department of Decision Sciences. His research focuses on energy policy, electricity markets, and sustainable development. He has a Ph.D. in Administration from HEC Montréal and a Master's in Philosophy from Université de Montréal. His expertise spans energy policy, electricity market dynamics, decision analysis, and decarbonization strategies. Current research includes environmental impacts of energy trade, electricity market modeling, and voluntary price mechanisms for energy conservation. Pineau has supervised over 30 master's and doctoral students, contributing to impactful studies in energy transition and policy. Recent publications emphasize reinforcement learning in energy demand response, price forecasting in Ontario, and decarbonization challenges in North America. He co-authored the book Games in Management Science: Essays in Honor of Georges Zaccour and received the 2024 Hubert-Reeves Prize for his work on energy balance. Pineau teaches courses on energy value chains and sustainable supply chains, reflecting his commitment to bridging academia and industry. His work addresses global energy challenges, advocating for integrated regional energy systems and equitable climate policies.

Stevan HarnadView profile

Professor

• Cognitive Science
• Consciousness Studies
• Artificial Intelligence
• +3 more

Stevan Harnad is a Professor of Cognitive Science at UQÀM, McGill University, and the University of Southampton. His research focuses on cognition, consciousness, and the symbolic grounding problem. He serves as Editor of Animal Sentience , a journal exploring sentience across species. Harnad is an advocate for open access publishing, emphasizing equitable scholarly communication. His work bridges cognitive science with interdisciplinary concerns, including AI ethics, animal welfare, and language structure. Key research interests include cognitive architecture, consciousness studies, and the implications of artificial intelligence. He has contributed to debates on open access policies, emphasizing mandates for self-archiving to reduce subscription costs. His interdisciplinary approach integrates neuroscience, philosophy, and linguistics to address foundational questions about mind and language. Recent scholarship explores the ethical dimensions of AI, the neurocognitive basis of categorization, and the latent structures of language. Harnad’s work demonstrates a commitment to both theoretical rigor and practical impact in academic publishing reform.

Stephen PistoriusView profile

Professor

• Medical Physics
• Medical Imaging
• Image Reconstruction
• +4 more

Stephen Pistorius is a Professor in the Department of Physics and Astronomy at the University of Manitoba's Faculty of Science. He serves as Associate Head and Director of the Medical Physics Program, focusing on interdisciplinary research at the intersection of medical physics, biomedical engineering, and artificial intelligence. Role : Professor, Associate Head, Director of Medical Physics Program Contact : Office 205 Allen Building, Stephen.Pistorius@umanitoba.ca , +1 204-474-6205 Research Interests Medical Imaging : Development and optimization of radar-based microwave sensing systems for breast cancer detection. Image Reconstruction : Innovation in computational algorithms for PET and microwave imaging. Artificial Intelligence : Application of machine learning to tumor detection and signal analysis. Publication Trends His recent work emphasizes microwave imaging hardware (antenna arrays, phantom materials), machine learning integration for tumor detection, and image quality metrics across modalities. Key subfields include radar systems , stochastic optimization , and clinical translation of microwave sensing . Additional Contributions He contributes to radiation oncology via EPID-based positioning automation and dose verification systems, while also addressing technical challenges in 3D-printed phantom modeling and microwave propagation in biological tissues.

Michael A. ChapmanView profile

Professor

• Image Processing
• 3D Topographic and Built Environment Modelling
• Deformation Measurement and Analysis
• +7 more

Dr. Michael A. Chapman serves as a Professor in the Department of Civil Engineering at Toronto Metropolitan University, specializing in image processing, deformation analysis, and sensor-integrated geospatial modeling for infrastructure applications. His work bridges civil engineering with advanced computational techniques for real-world problem solving. His academic credentials include a BT from Toronto Metropolitan University (1977), MSc from Ohio State University (1979), and PhD from Laval University (1989). BT: Toronto Metropolitan University (1977) MSc: Ohio State University (1979) PhD: Laval University (1989) Chapman's research centers on deformation monitoring of structures like the Rogers Centre roof, mobile mapping for road condition assessment, and sensor fusion for precision geospatial models. He pioneers applications in pavement deflection measurement using Doppler lasers and mobile laser scanning for infrastructure inspection, emphasizing practical engineering solutions derived from photogrammetry and image metrology. His methodology transforms mechanical observation into digital innovation for civil infrastructure management. His publication portfolio reveals a strong trajectory in merging deep learning with geospatial engineering, particularly in hyperspectral image classification and mobile mapping systems. These works consistently address civil infrastructure challenges through advanced computational approaches, demonstrating evolving sophistication from pavement crack extraction to sea ice mapping. His distinguished recognition includes: Wild Heerbrugg Photogrammetric Award - North America (1981) Chapman actively supervises graduate students and teaches core courses including CVL 207 (Graphics), CVL 352 (Geomatics Measurement Techniques), and CV8506 (Industrial Metrology). He emphasizes adaptive pedagogy to accommodate diverse learning styles, viewing teaching as both professional duty and personal passion. His industry-relevant research often involves partnerships with transportation authorities for real-time infrastructure assessment. His laboratory work focuses on mobile mapping systems and sensor integration platforms for deformation monitoring, particularly applied to large-scale structures and transportation networks. Current projects involve real-time pavement assessment technologies and 3D modeling of built environments using multi-sensor fusion approaches.

Alagan AnpalaganView profile

Professor

• Wireless Communication
• Radio Resource Management
• Green Communication
• +7 more

Alagan Anpalagan is a Full Professor in the ELCE Department at Toronto Metropolitan University, previously Ryerson University. He holds a PhD in Electrical Engineering from the University of Toronto. His research focuses on radio resource management (RRM), green communication, IoT networks, and wireless communication systems. He directs the WINCORE Lab, specializing in radio access & networking (RAN) and cross-layer design. Dr. Anpalagan has authored/co-edited multiple books and holds IEEE Fellow status (2024). He has received awards including the Ryerson Sarwan Sahota Distinguished Scholar Award (2022) and the IEEE Canada Outstanding Engineering Educator Medal (2018). He has served in editorial roles for IEEE Communications Surveys & Tutorials and led industry collaborations with companies like Bell Mobility and IBM. Education: B.A.Sc. in Electrical Engineering, University of Toronto M.A.Sc. in Electrical Engineering, University of Toronto Ph.D. in Electrical Engineering, University of Toronto Research Interests: His work spans RRM, energy-efficient networks, cognitive communication, and smart grid technologies. Recent projects include digital twin applications in IoT and 6G-based user localization for emergencies. Publications: His 15 most recent articles include studies on network routing protocols, IoT scheduling with digital twins, and AI-driven disaster response systems. These reflect his focus on integrating AI and edge computing into next-gen communication systems. Awards: Recognized for both research and teaching excellence, including IEEE Fellowships and institutional awards for graduate education and service. Labs/Teams: Lead researcher at the WINCORE Lab, collaborating with academia and industry on wireless resource management and heterogeneous networks. The lab’s alumni network includes over 100 researchers.

Warut Khern-am-nuaiView profile

Associate Professor

• Information Systems
• Management Information Systems
• Online Marketplaces
• +5 more

Warut Khern-am-nuai serves as Associate Professor of Information Systems at McGill University's Desautels Faculty of Management, where he directs the Analytics, AI and Advanced Digital Technologies Initiative and Business & Management Research Centre while co-directing the Retail Innovation Lab. His cross-disciplinary work bridges information systems, artificial intelligence, and business operations with significant industry impact. His educational credentials include a Ph.D. in Management (MIS) with Computer Science minor from Purdue University, M.S. in Economics from Purdue, MBA (Honors) from Thailand's National Institute of Development Administration, and B.Eng. in Computer Engineering (First-Class Honors) from King Mongkut Institute of Technology Ladkrabang. Dr. Khern-am-nuai's research centers on online marketplace dynamics , predictive analytics for business , and AI-driven platform design , with special focus on user behavior, security implications, and economic outcomes. His work employs rigorous experimental and data-driven methodologies to address real-world challenges in retail, e-commerce, and digital platforms. Analysis of his recent publications reveals a strong trajectory toward fairness in machine learning systems , behavioral impacts of platform design , and AI applications in post-pandemic retail . These studies consistently leverage large-scale user data and experimental approaches to generate actionable business insights across diverse contexts. His scientific recognition includes: AIS Early Career Award (2023) ISS Sandy Slaughter Early Career Award (2022) Invitational Fellowship from Japan Society for Promotion of Science (2023) Multiple teaching honors including Poets&Quants Top 50 Professor (2022) Dr. Khern-am-nuai has secured over $1.1 million in competitive research funding, including SSHRC Insight Grants for "AI Generated Content and the Future of Online Platforms" ($143,510) and NSERC Discovery Grants for "Helping Retail Industry Navigate the Post-Pandemic World with AI" ($155,000). His grant portfolio demonstrates exceptional success in translating theoretical research into practical business solutions through industry partnerships. As leader of the Retail Innovation Lab and Analytics Initiative, he drives collaborative research connecting academic rigor with industry challenges, particularly in applying AI to retail analytics, supply chain optimization, and consumer behavior prediction through interdisciplinary team structures.

Nancy SalayView profile

Associate Professor

• Philosophy of Cognition
• Philosophy of Language
• Metaphysics
• +3 more

Nancy Salay is an Associate Professor in the Department of Philosophy at Queen's University, with a cross-appointment in the School of Computing. She holds affiliations as Editor of Dialogue: Canadian Philosophical Review and founder of ESC (Embodiment, Systems, and Complexity). Her research focuses on philosophy of cognition, language, and metaphysics, informed by embodied cognitive science. She earned her PhD in philosophy of mind from Dalhousie University, followed by a research fellowship at Brandeis and work as a computational linguist at Cycorp. Her research explores how language expands cognitive capacities like reflective consciousness, challenging traditional computational models of cognition. Recent work critiques limitations of machine learning systems in understanding abstract representational properties. Nancy's interdisciplinary approach bridges philosophy with computational linguistics and cognitive science. Publications span analysis of knowledge systems' failures, non-universality in computation, and philosophical critiques of neural networks' representational paradigms. Her work emphasizes organism-level engagement over neural reductionism in grounding intentionality. Collaborations include projects like the Halo Pilot evaluating knowledge representation systems.

François RivestView profile

Associate Professor

• Artificial Intelligence Engineering
• Machine Learning Systems
• Computational Neurosciences
• +3 more

Dr. François Rivest is an Associate Professor at Queen’s University, affiliated with the Department of Biomedical and Molecular Sciences (School of Medicine, Faculty of Health Sciences). He also holds cross-appointments in the School of Computing (Faculty of Arts and Science) and is a member of the Centre for Neuroscience Studies. His research focuses on machine deep reinforcement learning and animal interval timing, aiming to bridge computational neuroscience insights with advanced AI systems. He leads the Natural and Artificial Adaptive Intelligent Systems Laboratory. Education: PhD in Computer Science (Computational Neuroscience/Machine Learning) – Université de Montréal (2010) MSc in Computer Science – McGill University (2002) BSc in Mathematics and Computer Science – McGill University (2000) Research Interests: Dr. Rivest’s work integrates principles of animal learning, particularly reward-based systems and temporal cognition, into machine learning algorithms. Key areas include: Reinforcement learning frameworks inspired by dopamine signaling Drift-diffusion models for interval timing Adaptive representation construction in real-time systems Applications in smart homes, robotics, and neuroscience Publications: Over 20 peer-reviewed articles (2001–2022) span computational neuroscience, reinforcement learning, and machine learning systems. Recent work includes modeling interval timing dynamics and applying reinforcement learning to smart home systems. Lab & Affiliations: As Principal Investigator of the Natural and Artificial Adaptive Intelligent Systems Lab, he explores interdisciplinary AI applications. Collaborations include the Royal Military College of Canada (2010–present) and Queen’s University’s Center for Neuroscience Studies (2011–present).

Tucker CarringtonView profile

Professor

• Quantum Chemistry
• Spectroscopy
• Computational Molecular Science
• +4 more

Tucker Carrington is a Full Professor in the Department of Chemistry at Queen's University, Kingston. He holds a cross-appointment in the Department of Physics, Engineering Physics and Astronomy within the Faculty of Arts and Science. His research focuses on developing computational methods for studying molecular vibrations and chemical reactions using quantum mechanics. Carrington earned his BSc from the University of Toronto (1981) and PhD from the University of California, Berkeley (1985). His research interests include quantum dynamics, potential energy surface construction, and applications of neural networks in chemistry. He leads projects on anharmonic vibrational spectra, van der Waals systems, and high-dimensional ab initio calculations. Current supervision includes postdoctoral researcher Robert Wodraszka. Key contributions involve iterative eigensolver methods, Smolyak interpolation, and rectangular collocation algorithms. His work bridges computational chemistry with experimental spectroscopy, addressing challenges in molecular energy levels and reaction dynamics. Affiliated with the Canada Research Chair in Computational Quantum Dynamics, he collaborates internationally and publishes in top journals like Journal of Chemical Physics and Physical Review . His research impacts drug design, energy storage, and climate science through advanced computational techniques.

Werner DietlView profile

Associate Professor

• Programming Languages
• Static Analysis
• Software Security
• +4 more

Werner Dietl is an Associate Professor in the Department of Electrical and Computer Engineering at the University of Waterloo. His work focuses on programming languages, static analysis, software security, and formal verification techniques. He has contributed to type system design, low-power computing, and approximate data types through projects like EnerJ. His research also addresses challenges in compiler design, cryptographic protocol validation, and runtime enforcement mechanisms. Key research areas include: Type systems for imperative and domain-specific languages Static analysis of implicit control flow (e.g., Java reflection, Android intents) Approximate computing and energy-efficient computation Formal verification of security properties Publications span topics from unit measurement type inference to ownership-based security models, reflecting a focus on practical formal methods. His work emphasizes scalability and precision in type systems while addressing real-world software engineering challenges.

Lap Chi LauView profile

Professor

• 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.

Mohammad SalahuddinView profile

Research Professor

• Machine Learning
• Deep Learning for Networking
• Network Security
• +6 more

Mohammad Salahuddin is a Research Assistant Professor at the University of Waterloo. He holds a Ph.D. in Computer Science from Western Michigan University (2014), an M.Sc. from Western Michigan University (2003), another M.Sc. from Shaheed Zulfiqar Ali Bhutto Institute of Science and Technology (2001), and a B.Sc. from University of Karachi (FAST-ICS) (1999). His research focuses on machine/deep learning applications in networking, network security, encrypted traffic analysis, and cognitive management of wired/wireless networks. He also explores network softwarization, including SDN and NFV. His work bridges theoretical advancements and practical network system optimization. Research Interests: Machine Learning-driven Network Security Encrypted Traffic Classification using Deep Learning Resource Allocation in 5G/6G and Edge Computing Software-Defined Networking (SDN) Architectures Cognitive Radio Networks and Dynamic Spectrum Management Dr. Salahuddin maintains an active research profile with publications tracked on Google Scholar . His academic webpage at https://cs.uwaterloo.ca/~m2salahu provides further details on ongoing projects and collaborations.

Ian McQuillanView profile

Professor

• Natural Computing
• Biological Modelling
• Bioinformatics
• +3 more

Ian McQuillan is a Professor in the Department of Computer Science at the University of Saskatchewan. He is affiliated with the Bioinformatics Lab and the Bioinformatics Program, and an associate member of the Division of Biomedical Engineering. He holds a Ph.D. and undergraduate degree in Computer Science and Mathematics from the University of Western Ontario. His research focuses on natural computing, biological modeling, bioinformatics, formal language theory, and theoretical computer science. Current projects include modeling transposable elements, plant growth simulation, and gene assembly in ciliates. He is supported by the Natural Sciences and Engineering Research Council of Canada (NSERC). McQuillan teaches courses such as Bioinformatics and Computational Biology, Modelling and Algorithms of Biological Systems, and Automata and Formal Languages. His lab emphasizes professional conduct, collaboration, and inclusivity, adhering to a detailed Code of Conduct. He advises a diverse group of graduate and undergraduate students, with research contributions spanning computational biology, formal languages, and algorithm design. His work bridges theoretical foundations with practical applications in systems biology and bioinformatics.