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John Capobianco, PhD, is a Professor in the Department of Chemistry and Biochemistry at Concordia University and holds the Honorary Concordia University Research Chair in Nanoscience. His research focuses on lanthanide-doped nanoparticles, upconversion luminescence, and biomedical applications. PhD, University of Geneva Key research areas include: Nanomaterials synthesis and spectroscopy Upconversion for biomedical imaging Drug delivery systems Photodynamic therapy for cancer treatment Optical thermometry and sensing Recent publications highlight advancements in: X-ray detection via photochromic nanoparticles Lipid-coated nanoparticles for lung permeation Cooperative energy transfer in Yb3+/Eu3+ complexes Biocompatible nanomaterials for secure information storage Pr3+-doped radiosensitizers for glioblastoma therapy Scientific recognitions: Honorary Concordia University Research Chair in Nanoscience Teaching includes undergraduate and graduate courses in inorganic chemistry and spectroscopy. His work bridges fundamental material science with applied biomedical engineering, emphasizing optical properties and therapeutic applications of lanthanide-based nanomaterials.

• Nanoscience
• Inorganic Chemistry
• Materials Science
• +7 more

Mustafa Gül is a Professor in the Department of Civil and Environmental Engineering at the University of Alberta’s Faculty of Engineering. He also serves as Director of Internationalization at the Faculty of Engineering’s Deans Office. His research focuses on smart, sustainable, and resilient cities, with an emphasis on infrastructure monitoring and energy-efficient systems. Education: PhD in Civil Engineering (University of Central Florida, 2009), MSc in Electrical Engineering (University of Central Florida, 2011), MSc in Civil Engineering (Boğaziçi University, 2004), BSc in Civil Engineering (Boğaziçi University, 2002). Dr. Gül’s research spans two primary domains: Crowdsensing-based Monitoring of Built and Natural Environments (CoMBiNE) using AI, signal processing, and data analytics for infrastructure health; and Energy-Efficient Smart Cities through solar PV integration, IoT applications, and net-zero energy homes. His work bridges structural engineering, machine learning, and sustainable urban development. Recent publications highlight advancements in smartphone-based damage detection, UAV-assisted disaster assessment, and AI-driven energy systems. His team’s work on crowdsensing bridges, solar PV optimization, and smartphone analytics has been widely recognized in journals like Structural Control and Health Monitoring and Energy and AI . Notably, his 2017 paper earned the Best Paper Award at ISARC. Students supervised include Azim, R. , Keskin, M. , and Do, N. T. , among others. Scientific Awards: Best Paper Award, 34th International Symposium on Automation and Robotics in Construction (ISARC 2017) Dr. Gül’s projects often involve interdisciplinary collaboration, leveraging sensor networks, computer vision, and optimization algorithms to address urban resilience and energy sustainability. His leadership extends to course CIV E 779 and ongoing research initiatives in Alberta, Canada.

• Smart Sustainable and Resilient Cities
• Energy Efficient Communities & Cities
• Crowdsensing-based Infrastructure Monitoring (CbIM)
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Salem Lahlou is an Assistant Professor in the Machine Learning Department at the Mohamed bin Zayed University of Artificial Intelligence (MBZUAI), having joined in September 2024. He previously served as a Senior Researcher at the Technology Innovation Institute (TII) in 2024. His academic background includes a PhD from Mila and Université de Montréal (UdeM) under Yoshua Bengio (2023), with prior studies in applied mathematics at École Polytechnique and statistical learning at École Normale Supérieure Paris-Saclay. His research focuses on developing more capable and reliable AI systems through three interconnected pillars: Novel Method Development : Core contributions to Generative Flow Networks (GFlowNets), uncertainty estimation techniques (DEUP), and curriculum learning frameworks Large Language Model Advancement : Enhancing reasoning capabilities and alignment through preference optimization and trace-based learning Community Tooling : Creation of torchgfn library for GFlowNets and benchmarks including BabyAI, FinChain, and LLM-BabyBench Core research areas span Machine Learning, GFlowNets, Uncertainty Estimation, LLM Reasoning, Reinforcement Learning, and AI for Science. Recent publications (2023-2025) demonstrate strong emphasis on GFlowNet theory/improvements (8+ papers), LLM reasoning evaluation (FinChain, LLM-BabyBench), uncertainty quantification, and societal AI impacts. Key application domains include mathematical reasoning, financial systems, privacy preservation, and cognitive science. He currently advises graduate students including Junyi (privacy risks in SNNs) and Abhijith (LLM reasoning). His group collaborates with MBZUAI faculty (Nils Lukas, Alham Fikri, Mingming Gong, Martin Takac) and industry partners on projects involving Conversational AI, Personalization, and Affective AI.

• Machine Learning
• Artificial Intelligence
• GFlowNets
• +7 more

Dongwook Yoon is an Associate Professor at the Department of Computer Science , University of British Columbia , and serves as Director of the SOCIUS Lab . He actively contributes to research in Human-Computer Interaction, Human-AI Interaction, and Virtual/Augmented Reality as a member of the Designing for People (DFP) and CAIDA research clusters. Education : PhD in Computer Science from Cornell University (2017), MS (2009) and BS (2007) in Computer Science from Seoul National University Research Focus : Designing socio-technical systems that bridge the gap between technology and human social processes, with innovations in AR/VR, multimodal interaction, and inclusive design Article Trends show his work spans: Temporal and bichronous learning environments AI self-clones and ethical implications Income inequality in virtual platforms Enhanced multimodal collaboration in VR Eyes-reduced interfaces for situational impairments Speculative participatory design for gig economy challenges Scientific Awards include: Google Academic Research Award (2024) Best Paper Award at CHI 2024 High Impact Award in Educational Technology (2024) CHCCS/SCDHM Graphics Interface Early Career Award (2023) Multiple Honorable Mentions at CHI, DIS, and CSCW Students & Collaborators range from active PhD candidates (Anika Sayara, Yuri Kim) to notable alumni (Thitaree Tanprasert, Ashish Chopra) across his SOCIUS Lab projects. His research receives funding from NSERC , KIST , Adobe , Microsoft , and Google grants.

• Human-Computer Interaction
• Human-AI Interaction
• Virtual and Augmented Reality
• +3 more

Mark Crowley is an Associate Professor in the Department of Electrical and Computer Engineering at the University of Waterloo , with a cross-appointment in the Cheriton School of Computer Science . He is actively involved in the Waterloo Artificial Intelligence Institute (WAII) , the Waterloo Institute for Complexity and Innovation (WICI) , and serves as National Secretary for the Canadian Artificial Intelligence Association (CAIAC) , coordinating the Canadian Conference on AI . Research interests span the theoretical and applied aspects of Reinforcement Learning , Deep Learning , Manifold Learning , and Ensemble Methods . His work addresses challenges in domains with spatial dynamics, multi-agent systems, and uncertainty, particularly in Computational Sustainability (forest fire management, sustainable forestry), Autonomous Driving , Medical Imaging , and Material Design . Recent research focuses on integrating causal modeling with generative representation learning to improve out-of-distribution robustness in motion forecasting applications. Key publications include foundational work on ChemGymRL environments for safe chemical process reinforcement learning, Generative Causal Representation Learning for robust forecasting, and collaborative work on multi-advisor reinforcement learning in multi-agent settings. He co-authored a textbook Elements of Dimensionality Reduction and Manifold Learning (Springer, 2023) with Prof. Ali Ghodsi and Prof. Fakhri Karray. Teaching includes graduate and undergraduate courses in Algorithm Design , Computational Intelligence , Reinforcement Learning , and Data Modeling at the University of Waterloo since 2018. His research group has produced several notable graduates including Benyamin Ghojogh (2021), who continued as a postdoc until 2022.

• Reinforcement Learning
• Deep Learning
• Ensemble Methods
• +6 more

Lucia Lee is an Assistant Professor in the Department of Chemistry at Queen's University, affiliated with the Faculty of Arts and Science. Her research focuses on applying green chemistry principles to supramolecular interactions involving main-group elements, particularly sigma-hole interactions, with applications in materials science and medicine. She holds a PhD from McMaster University and has completed postdoctoral studies at the University of Geneva and Weizmann Institute of Science. Dr. Lee's educational background includes a PhD supported by an NSERC grant, which explored chalcogen bonding in supramolecular materials. Her postdoctoral work at Weizmann focuses on stimuli-responsive materials using chalcogen elements for photoswitching applications. She has also contributed to academic governance through roles in the McMaster Graduate Students Association. Her research interests span analytical chemistry, quantum chemistry, inorganic and bioinorganic chemistry, organic chemistry, and free radical chemistry. Key projects include integrating chalcogen bonding into d-metal coordination chemistry, catalysis, and chemical biology to create functional materials. Her lab, located in CHE513, emphasizes sustainable approaches to material design through main-group supramolecular systems. Her articles explore topics like chalcogen bonding mechanisms, anion transport, and photoswitching in confined spaces, reflecting a strong focus on molecular assembly and functional materials. She has no listed scientific awards but demonstrates significant contributions to supramolecular chemistry through her publications and cross-appointments at Queen's Carbon to Metal Coating Institute.

• Chemical Sciences
• Analytical Chemistry
• Quantum Chemistry
• +9 more

Manolis Savva is an Associate Professor in the School of Computing Science at Simon Fraser University and holds the Canada Research Chair in Computer Graphics. He specializes in 3D scene analysis, generative methods for 3D content, and computer graphics for AI. His research bridges computer graphics, vision, and robotics. Education: Ph.D. (Computer Science, Stanford University, 2016), MS (Computer Science, Stanford, 2012), B.A. (Physics & Computer Science, Cornell, 2009). Research Interests: Human-centric 3D scene analysis, generative 3D content creation, AI-driven rendering, and applications in robotics. Key projects include Habitat (Embodied AI platform), ScanNet , and ShapeNet datasets. Recent Articles: Focus on articulated object modeling, 3D scene synthesis, and AI-driven visualization. Notable work includes SceneMotifCoder (generating object arrangements) and R3DS (panoramic scene understanding). Awards: CHCCS Early Career Award (2022), ICLR 2023 Outstanding Paper Award, ICCV 2019 Best Paper Nomination, and SGP 2020 Dataset Award (ScanNet). Lab/Teams: Leads research groups in 3DLG (3D Learning and Graphics) and GrUVi (Graphics and Vision). Collaborates on projects like AI Habitat and HomeRobot .

• Computer Graphics
• Computer Vision
• 3D Scene Analysis
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Dr. Gabriel Wainer is a Professor in the Department of Systems and Computer Engineering at Carleton University's Faculty of Engineering and Design. He leads the Advanced Real-Time Simulation Lab and specializes in modeling and simulation methodologies, particularly focusing on discrete event systems, real-time modeling, cellular automata, and DEVS formalism. Research Interests: Discrete event systems, DEVS formalism, cellular automata, real-time simulation, IoT applications, and parallel/distributed simulation Affiliation: Carleton University Recent publications highlight his work in advanced simulation frameworks, energy-efficient 5G systems using deep reinforcement learning, and pandemic modeling with cellular automata. His lab develops tools like PROMETHEUS and Devsmap for standardized DEVS model representation, while also exploring applications in wireless communication, building energy systems, and behavioral epidemiology.

• Modelling and Simulation Methodologies
• Discrete Event Systems
• Real-Time Modelling
• +7 more

David Churchill is an Associate Professor in the Department of Computer Science at Memorial University of Newfoundland (MUN), specializing in Artificial Intelligence and Real-Time Strategy (RTS) Game AI. He holds a PhD from the University of Alberta and has been actively involved in AI research since 2009. His work focuses on AI for RTS games like StarCraft, emphasizing heuristic search, combat simulation, and build order optimization. He organizes the annual AIIDE StarCraft AI Competition and maintains open-source projects like UAlbertaBot and SparCraft. Education: BSc in Pure Mathematics and Computer Science (MUN) MSc in Computer Science (MUN, 2009) PhD in Computing Science (University of Alberta, 2016) Research Interests: AI in video games, heuristic search algorithms, RTS game strategies, multi-agent systems, and robotics. His work bridges theoretical AI with practical applications in competitive gaming and robotics. Publications & Awards: Notable contributions include Search Ordering for StarCraft Build Order Optimization (2024) and Hierarchical Portfolio Search in Prismata (2017, Best Student Paper Award). His research has advanced combat simulation (SparCraft) and build-order planning (BOSS) in RTS games. Awards: Best Student Paper Award (2017) Best Paper Award (2013, 2022) Advising & Labs: Supervised over 20 graduate and undergraduate theses at MUN. Leads the StarCraft AI Competition and contributes to open-source projects like UAlbertaBot and STARTcraft. Currently not accepting new graduate students due to funding constraints.

• Artificial Intelligence
• Real-Time Strategy Games
• Game Design
• +5 more

Gerda de Vries is a Professor in the Department of Mathematics & Statistical Sciences at the University of Alberta, Faculty of Science. Her research focuses on mathematical physiology, dynamical systems, and mathematical modeling, particularly in cellular biophysics, pattern formation, and systems biology. She has contributed extensively to understanding complex biological systems through interdisciplinary approaches combining mathematics and biology. Her work spans applications in radiation biology (e.g., cell cycle dynamics and low-dose radiation effects), biophysics (microtubule organization, motor proteins), ecology (predator-prey interactions, forest fire modeling), and education (adapting primary literature for STEM teaching). Recent research highlights include analyzing saddle-node bifurcations, bystander effects in radiation, and collective behavior in animal groups. De Vries has published over 50 peer-reviewed articles since 2000, with a focus on bridging abstract mathematical theory to concrete biological phenomena. Notable contributions include models of pancreatic β-cell dynamics, immune system versatility, and educational frameworks for mathematical biology. Her academic career includes leadership in curriculum development and interdisciplinary research, though no specific grants or awards are explicitly listed in the provided information.

• Mathematical Physiology
• Dynamical Systems
• Mathematical Modeling
• +3 more

Dr. Tristan A.F. Long is an Associate Professor in the Department of Biology at Wilfrid Laurier University's Faculty of Science in Waterloo, Ontario. A behavioral ecologist and evolutionary geneticist, he focuses on sexual selection and the role of female mate preference variation in evolutionary change. With teaching responsibilities for large introductory biology courses like BI111 and BI393, he has developed innovative active learning techniques using playing cards, iClickers, and role-playing games to teach population genetics and ecological principles. University of Western Ontario - BSc in Honours Ecology and Evolution (1999) University of Guelph - MSc in Zoology (2001) Queen’s University - PhD in Biology (2005) University of California Santa Barbara - Postdoctoral Fellow (2005-2009) University of Toronto - Postdoctoral Fellow (2009-2010) His research examines how female Drosophila melanogaster vary in their mating preferences and how these differences shape evolutionary trajectories. He has published extensively on reproductive plasticity, sexual conflict, and environmental interaction effects. His laboratory combines experimental evolution with computational modeling to explore genetic trade-offs and behavioral adaptations. Scientific awards include the Laurier Teaching Award for Sustained Excellence (2017). He has developed innovative classroom techniques like the Battle of the Beaks exercise for teaching adaptive evolution and an iClicker-based population genetics simulation using playing cards. His 2024 BI393 biostatistics course policy strongly discourages generative AI use due to concerns about educational integrity and environmental impact.

• Evolutionary genetics
• Behavioral ecology
• Experimental evolution
• +3 more

Patrick Hayes is a Full Professor in the Department of Chemistry at Université de Montréal. His research focuses on atmospheric chemistry of air pollution and climate-forcing agents, with expertise in aerosol chemistry, mineral dust dynamics, and analytical spectroscopy. He collaborates with Environment and Climate Change Canada, Quebec’s Ministry of the Environment, and private sector partners like Glencore. Education & Roles: PhD in Chemistry (not explicitly stated, inferred from academic rank) Director of the Hayes Group conducting Arctic fieldwork and computational modeling Teaches courses in environmental chemistry across multiple programs Research Interests: Chemical characterization of atmospheric particles and their climate impacts Mineral surface chemistry and dust emission processes Development of advanced analytical tools for environmental monitoring Study of Arctic aerosol dynamics and transboundary pollution Recent Trends in Publications: Focus on Arctic aerosol behavior and dust dynamics Analysis of urban pollution sources (Beirut/Montreal comparisons) Integration of remote sensing with ground-based measurements Health risk assessments of particulate matter components Awards & Recognition: 2021 Perkin-Elmer Award (CSASS) RSC Analyst Emerging Investigator Award Advising & Grants: Supervised 8+ graduate students since 2016 Lead researcher on 20+ projects, including $2.5M+ in funding from CRSNG, FRQNT, and industry Key roles in networks like EcotoQ and MOACC Labs & Teams: Hayes Group at UdeM with advanced instrumentation capabilities Partnerships with federal/provincial agencies and international collaborators

• Atmospheric Chemistry
• Air Pollution
• Climate Forcing Agents
• +4 more

Marc GENDRON-BELLEMARE is an Associate Professor at the Department of Computer Science and Operations Research, Faculty of Arts and Sciences, Université de Montréal. He is also a Chief Scientific Officer at Reliant AI, Adjunct Professor at McGill University, Canada CIFAR AI Chair at Mila, and Associate Fellow at CIFAR LMB Program. His research focuses on reinforcement learning, deep learning, and generative models, with notable contributions to the Atari 2600 benchmark and applications in robotics and stratospheric balloon navigation. He has advised multiple PhD and MSc students, including Pierluca D'Oro and Rishabh Agarwal. Education: PhD from University of Alberta under Michael Bowling and Joel Veness. Notable collaborations include work at Google Brain and DeepMind. Research Interests: Reinforcement Learning, Deep Learning, Probabilistic Models, Online Learning, Generative Models, and Information Theory. Awards: Best Paper Awards at NeurIPS 2021, ICLR 2020, and ICML Exploration Workshop 2019. His work on stratospheric balloon navigation using RL was published in Nature (2020). Grants and Labs: Core member of Mila, involved in projects like the Dopamine research framework and the Arcade Learning Environment (ALE). Active in open-source contributions and industry partnerships through Reliant AI.

• Reinforcement Learning
• Deep Learning
• Generative Modeling
• +3 more

Sarath Chandar is an Associate Professor at Polytechnique Montréal and Core Faculty Member at Mila, the Quebec AI Institute. He holds a Canada CIFAR AI Chair and Canada Research Chair in Lifelong Machine Learning. His research focuses on developing interactive learning algorithms for continual and lifelong learning, with expertise in deep learning, reinforcement learning, and natural language processing. Education: Ph.D. in Computer Science, University of Montreal (advisor: Yoshua Bengio) M.S. in Computer Science, Indian Institute of Technology Madras (advisor: Balaraman Ravindran) Research Themes: Continual Learning and Lifelong Learning Deep Reinforcement Learning Optimization for Deep Networks Natural Language Processing AI for Scientific Discovery Notable Contributions: Founder of the Conference on Lifelong Learning Agents (CoLLAs) Developed Chandar Research Lab (CRL), focusing on adaptive learning algorithms Contributions to model-based reinforcement learning and bias mitigation in AI systems Awards & Grants: Canada CIFAR AI Chair Canada Research Chair Tier 2 MITACS-funded projects on reinforcement learning applications Lab & Collaboration: CRL collaborates with academic/industrial partners (e.g., IBM, Samsung) Hosts annual symposium showcasing research in AI, optimization, and multi-agent systems

• Artificial Intelligence
• Machine Learning
• Deep Learning
• +7 more

Dr. Terje Haukaas is a Professor of Structural & Earthquake Engineering at the University of British Columbia (UBC), Department of Civil Engineering, Faculty of Applied Science. He holds a PhD and Master's from UC Berkeley (2003, 1999) and a bachelor's from the Norwegian University of Science and Technology (1996). His research focuses on probabilistic modeling, structural reliability, and earthquake engineering, with contributions to software development (e.g., FERUM, OpenSees). He teaches courses like Structural Analysis, Nonlinear Analysis, and Reliability & Safety. Education: PhD in Civil Engineering, UC Berkeley, 2003 Master's in Civil Engineering, UC Berkeley, 1999 Bachelor's in Civil Engineering, NTNU, Trondheim, 1996 Engineering Degree (Stavanger University College, 1994) and Technician Degree (Stavanger Technical College, 1992) Research Interests: Probabilistic mechanics and reliability analysis Seismic vulnerability and risk assessment Software tools for finite element analysis (FERUM, OpenSees) Timber engineering and structural optimization Awards & Recognition: UBC Killam Teaching Prize (2016) President of CERRA (2015–2019) Keynote/Semi-plenary speaker at major conferences (ICASP12, COMPDYN 2017) Student Appreciation Awards (Top Professor rankings) Grants & Labs: Recipient of grants supporting seismic risk research Developed computational frameworks for structural analysis

• Structural Reliability
• Earthquake Engineering
• Probabilistic Mechanics
• +3 more