Patrick Mitran is a full-time Professor at the University of Waterloo's Department of Electrical and Computer Engineering, within the Faculty of Engineering. His research focuses on advanced wireless communication systems, including 5G/6G technologies, millimeter-wave and sub-THz communication, digital predistortion techniques, MIMO systems, and beamforming architectures. He leads projects addressing challenges in transmitter linearization, network resource allocation, and hardware-efficient signal processing. Key research interests include optimizing frequency multiplier-based transmitters, mitigating inter-cell interference in massive MIMO networks, and developing algorithms for reconfigurable intelligent surfaces (RIS). His work often intersects hardware design, signal processing, and network optimization, with applications in next-generation wireless infrastructure. Recent publications highlight innovations in ultrawideband signal generation for 6G testing, practical RIS configurations, and FPGA-based real-time digital predistortion implementations. His contributions emphasize both theoretical advancements and practical system-level solutions. Dr. Mitran's research group collaborates on cutting-edge topics such as hybrid NOMA in multi-cell networks, adaptive coding modulation for Gaussian channels, and interference decoding strategies. His work has been published in top-tier journals and conferences, reflecting a sustained impact on modern wireless communication technologies.
Hamid Mansoor is an Assistant Professor in the Department of Computer Science at the University of Manitoba. He holds a PhD in Computer Science from Worcester Polytechnic Institute under Prof. Emmanuel Agu, and was part of the DARPA-funded WASH project. His research focuses on data visualization, digital health, and smartphone-based behavioral analysis. He previously served as a Postdoctoral Fellow at the VIXI Lab, University of Victoria, Canada, under Prof. Miguel Nacenta. Education: PhD in Computer Science, Worcester Polytechnic Institute Research Interests: Interactive data visualization frameworks for health monitoring Mobile and ubiquitous computing for behavioral analysis Smartphone-sensed human behavior and health informatics Visual representation of text-based and sensor data Publications highlight trends in visual analytics for healthcare, including tools like ARGUS and INPHOVIS for detecting bio-behavioral disruptions and smartphone-based phenotyping. His work integrates machine learning with visualization to address challenges in health data interpretation. Awards: Best short paper honorable mention (EuroVis 2020) His contributions span academic collaborations in health informatics and mobile computing, with a focus on bridging theory and practical applications in healthcare technology.
Catherine Stinson is an Assistant Professor and Queen’s National Scholar in Philosophical Implications of Artificial Intelligence at Queen's University. She holds joint appointments in the School of Computing and the Philosophy Department, blending technical and philosophical expertise to address ethical challenges in AI. PhD in History & Philosophy of Science, University of Pittsburgh (2013) MSc in Computer Science, University of Toronto (2013) Dr. Stinson's research focuses on methodological and ethical dimensions of artificial intelligence, including: Bias and explanation in machine learning Computational psychiatry Embodied intelligence Community-led design AI ethics education Her recent publications analyze corporate influence in NLP, privacy implications of large language models, and documentation practices in AI datasets. They explore intersections between technical performance, ethical responsibility, and societal impact. Scientific awards include: Queen’s National Scholar Finalist for Outstanding Certification at Transactions in Machine Learning Research As director of the Ethics and Technology Lab at Queen's, she leads interdisciplinary projects challenging algorithmic bias, investigating AI creativity, and developing tools to resist targeted violence. Current initiatives include: Critical evaluation of chatbots for mental health support Tracking (In)justice project on police-involved deaths in Canada
Samuel Jean Bassetto is an Associate Professor in the Department of Mathematics and Industrial Engineering at Polytechnique Montréal. He serves as Director of the Continuous Improvement Laboratory (LABAC) and holds membership in multiple prestigious research groups including the Research Group on Globalisation and Management of Technology (GMT), Poly-Industries 4.0 Laboratory, Interuniversity Research Centre on Enterprise Networks, Logistics and Transportation (CIRRELT), and Institute for Data Valorization (IVADO). Dr. Bassetto's research spans multiple disciplines, focusing on continuous improvement through the integration of engineering, artificial intelligence, cognitive science, psychology, and design. His primary sphere of excellence is in New Frontiers in Information and Communication Technologies, with secondary expertise in Modeling and Artificial Intelligence and Human Health. He develops tools that place humans at the center of technology to enhance organizational performance while respecting human rhythms and cognitive limitations. His recent publication portfolio reveals a strong interdisciplinary approach, with research bridging industrial engineering, cognitive neuroscience, and AI ethics. His work addresses practical challenges in lean manufacturing assessment, racial bias in medical AI systems, cognitive data collection in natural environments, and condition monitoring for industrial machinery. The research consistently demonstrates a commitment to developing practical solutions that integrate human factors with technological innovation. NSERC Synergy Prize for Innovation recipient Principal investigator on multiple research grants from NSERC, FRQ, and MITACS Collaborations with over a dozen institutions across multiple countries Supervision of over 150 highly qualified personnel throughout his career Dr. Bassetto teaches specialized courses including CAP7011 (Creativity in Research), IND8444 (Continuous Improvement), IND8203 (Industrial Launch), and previously taught IND8178 (Production). His teaching philosophy emphasizes practical application, with courses featuring hands-on exercises, real-world scenarios, and gamification techniques to enhance learning. His supervision portfolio includes numerous Ph.D. and Master's students working on topics ranging from human-technology collaboration to reinforcement learning for production management. Through LABAC, Dr. Bassetto leads research initiatives focused on developing human-centered tools for continuous improvement in organizational settings. The laboratory conducts projects related to industrial IoT applications, cognitive aspects of process improvement, and the development of practical frameworks for organizations to enhance performance while maintaining respect for human rhythms and cognitive capabilities.
Rachel Pottinger is a Professor in the Department of Computer Science at the University of British Columbia within the Faculty of Science. She has been at UBC since 2004, progressing from Assistant Professor to Associate Professor in 2012 and to full Professor in 2021. She is affiliated with research centers including CAIDA (Centre for Artificial Intelligence Decision-making and Action) and DFP (Designing for People), and is part of ICICS (Institute for Computing, Information and Cognitive Systems). Her research focuses on data management, particularly semantic data integration, metadata management, and making data more accessible and understandable to users. She leads the Data Management and Mining Lab and has supervised numerous doctoral and master's students. Her work addresses three main areas: helping people understand and explore their data, managing data not well supported by databases, and coordinating data across multiple databases. Her recent publications demonstrate strong trends in database usability, data provenance visualization, query recommendation systems, and building information modeling integration. Her work bridges theoretical database concepts with practical human-centered applications, particularly in making complex data systems more accessible to non-expert users. UBC Computer Science Department Faculty Teaching Award 2013 Computer Science Department Teaching Award 2010 CS Department Teaching Award Denice Denton Emerging Leader Award 2007 Pottinger has supervised numerous PhD and Master's students, with research focusing on data provenance, database usability, and data coordination. She has been involved in significant research projects related to data lakes, open data navigation, and query recommendation systems. Her current research explores table annotation and discovery in data lakes, query refinement for aggregation queries, and query prediction based on past user behavior. She is actively involved in the academic community, serving as Secretary-Treasurer for SIGMOD, on the VLDB Journal editorial board, and as a member of the Computing Research Association's Board of Directors. She previously served as General Co-Chair of SIGMOD 2020 and as Associate Head for the Undergraduate Program of the Department of Computer Science from 2018-2020.
Paul Ward is an Associate Professor in the Department of Electrical and Computer Engineering at the University of Waterloo and a faculty fellow at the IBM Centre for Advanced Studies. He holds a PhD (2002) and MASc (1993) from Waterloo and a BScE (1998) from the University of New Brunswick. His research focuses on distributed systems management, dependable systems, autonomic computing, wireless networks, and IoT. Key areas include fault detection in web services, service-oriented networking, and optimization of wireless mesh networks. Ward's publications span computer networks, cognitive science, and sports analytics, reflecting interdisciplinary applications of computational methods. He holds two patents in mobile web services and fault resolution.
Charles Perin is an Assistant Professor of Computer Science at the University of Victoria, leading the UViz research group. He holds a PhD from Université Paris-Sud (2014) and has held roles including Post-doc at the University of Calgary and Lecturer at City, University of London. His research focuses on information visualization, personal visualization, human-computer interaction, and sports visualization. Education: PhD in Computer Science (2014), Université Paris-Sud; Post-doc at University of Calgary (InnoVis lab); MS and earlier studies in Computer Science and HCI in France. Research interests include designing interactive visualization tools for personal data reflection, health data communication, and sports analytics. He emphasizes authoring tools for non-experts and physical/tangible visualization systems. Recent work explores embedded data physicalizations and mobile visualization design. His articles span topics like data storytelling, patient-generated health visualizations, and soccer data analysis, often appearing in top venues like IEEE VIS, CHI, and Eurovis. He has advised over 20 students across PhD, MSc, and undergraduate levels. Teaching includes courses on Information Visualization and HCI at UVic, City, and other institutions. He co-organized workshops on topics like Personal Visualization and Sports Data at IEEE VIS. His UViz lab collaborates internationally with institutions like Monash University and the National Archives (UK).
Dr. April Nowell is a Professor of Anthropology in the Department of Anthropology at the University of Victoria's Faculty of Social Sciences, specializing in Paleolithic archaeology, cognitive archaeology, and the archaeology of children. Currently on leave, she leads internationally recognized research projects across Europe, the Levant, Australia, and Africa while actively accepting graduate students. She earned her PhD from the University of Pennsylvania and has developed expertise in Neanderthal lifeways, Paleolithic art, and hominin life histories. Her academic journey reflects deep engagement with both theoretical frameworks and fieldwork across diverse geographical contexts. Nowell's research examines how prehistoric societies structured knowledge transmission, childhood development, and symbolic expression. Her groundbreaking work on finger flutings in Australian caves reveals children's roles in Paleolithic storytelling traditions, while her analysis of Levantine wetland ecosystems demonstrates how Pleistocene humans adapted to environmental shifts. She challenges conventional narratives about Neanderthal cognition and has pioneered methodologies for reconstructing prehistoric childhood experiences through skeletal and material evidence. Her recent publications show consistent innovation in archaeological methodology, particularly in digital documentation of rock art and interdisciplinary approaches to human development. Key trends include integrating bioarchaeological data with cognitive models, examining material culture as evidence of social learning, and analyzing environmental archives to understand human dispersal patterns. Her scientific recognition includes: 2023 EAA Book Prize for Growing Up in the Ice Age: Fossil and Archaeological Evidence of the Lived Lives of Plio-Pleistocene Children Nowell secures major research funding including Social Sciences and Humanities Research Council grants supporting her Azraq Basin project in Jordan and Koonalda Cave research in Australia. She mentors graduate students in Paleolithic theory while collaborating with Indigenous communities and international scholars on field projects spanning five continents. Her work bridges academic research and public engagement through TEDx talks and media appearances examining science communication. She directs field programs at Jordan's Azraq Basin wetlands and Australia's Koonalda Cave, working with multidisciplinary teams including geochronologists, bioarchaeologists, and Traditional Owners to investigate Pleistocene human adaptation and cultural transmission.
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.
William Sulis is an Associate Clinical Professor in the Department of Psychiatry and an Associate Member of the Department of Psychology at McMaster University, where he also directs the Collective Intelligence Lab (CILab). With a unique interdisciplinary background spanning mathematics, physics, and psychiatry, Dr. Sulis bridges the gap between theoretical science and clinical practice. His educational journey is exceptionally diverse: B.Sc. (Hon) in Mathematics with minor in Theoretical Physics, Carleton University (1976) M.D., University of Western Ontario (1980) M.A. in Mathematics, University of Western Ontario (1984) Ph.D. in Mathematics, University of Western Ontario (1989) FRCPC in Psychiatry (1984) Ph.D. in Theoretical Physics, University of Waterloo (2014) CRCPC in Geriatric Psychiatry (2015) Dr. Sulis's research explores the intersection of complex systems theory with psychological and psychiatric phenomena. His work on Collective Intelligence investigates how group dynamics emerge from individual interactions, while his research on Temperament and Psychobiology examines the continuum between normal personality variations and mental illness. He has made significant contributions to understanding Synchronization in Complex Systems and developed the concept of Transient Induced Global Response Synchronization (TIGoRS) , which has implications for neural coding and information processing. His theoretical work extends to Quantum Foundations and Process Algebra Theory , where he proposes novel approaches to quantum mechanics. Analysis of his recent publications reveals a consistent thread connecting complex systems theory with psychological and psychiatric applications. His work increasingly focuses on bridging the gap between temperament theory and clinical psychiatry, using mathematical and computational approaches to understand mental illness. Simultaneously, he continues to develop theoretical frameworks in quantum physics through process algebra models, demonstrating remarkable interdisciplinary range. Dr. Sulis has received several prestigious awards including The Governor General's Medal for having the highest overall grade point average in his graduating class, the Henry Marshall Tory Scholarship, and multiple Harry Stevenson Southam Scholarships. Throughout his career, Dr. Sulis has mentored numerous students across disciplines, supervising research projects spanning collective intelligence, semantic space modeling, network dynamics, and temperament studies. His Collective Intelligence Lab has served as a hub for interdisciplinary research connecting computer science, psychology, and psychiatry. Dr. Sulis has also been actively involved in professional organizations, serving as President of The Society for Chaos Theory in Psychology and the Life Sciences (1996-1998) and holding editorial positions for several journals including "Dynamical Psychology" and "Nonlinear Dynamics in Psychology and the Life Sciences." As Director of the Collective Intelligence Lab at McMaster University, Dr. Sulis fosters research exploring how complex adaptive systems can model cognitive and social phenomena. The lab serves as an intellectual nexus where mathematics, computer science, psychology, and psychiatry converge to address fundamental questions about intelligence, both individual and collective.
Richard Zemel is a Professor in the Department of Computer Science at the University of Toronto, where he has been since 2000. He holds an Industrial Research Chair in Machine Learning and co-founded the Vector Institute for Artificial Intelligence. His research focuses on machine learning, including unsupervised learning, deep learning, and ethical AI, with contributions to probabilistic models, fairness, and representation learning. Zemel has developed influential systems like the Toronto Paper Matching System and holds awards such as the NVIDIA Pioneers of AI Award and multiple NSERC grants. Education: B.Sc. in History & Science from Harvard University (1984), Ph.D. in Computer Science from the University of Toronto (1993). Postdoctoral work at the Salk Institute and Carnegie Mellon University. Research Interests: Machine Learning (unsupervised/deep learning), probabilistic models, fairness in algorithms, computer vision, natural language processing. He emphasizes ethical AI and practical applications like recommendation systems and causal inference. Awards & Affiliations: Fellow of CIFAR, member of the Neural Information Processing Society (NIPS) Executive Board, and advisor to the Creative Destruction Lab. His work is funded by NSERC, CIFAR, Google, Microsoft, and DARPA. Grants & Labs: Active in grants supporting machine learning research, including projects on fairness and invariant learning. Collaborates with industry partners and leads teams at the University of Toronto and Vector Institute.
Ross Otto is an Associate Professor in the Department of Psychology at McGill University. He holds an office at 2001 McGill College, 711, and can be reached via email at ross.otto@mcgill.ca. His research focuses on understanding the mechanisms underlying reflective versus reflexive decision-making, particularly how cognitive resources, stress, and contextual factors influence effort-based choices. He employs computational modeling, behavioral experiments, and neuroimaging techniques. Notable contributions include studies on stress-induced cognitive avoidance and the neural correlates of effort evaluation. His work is affiliated with the Otto Lab, and he collaborates widely across disciplines such as neuroscience and behavioral economics.
Wesley Willett is an Associate Professor in the Department of Computer Science at the University of Calgary, holding the NSERC CRC II Chair in Visual Analytics. His primary research focuses on information visualization, human-computer interaction, and new media applications. He leads the Data Experience Lab and Interactions Lab, exploring innovative methods for data representation and interaction in augmented/virtual reality environments. Education includes a B.S. in Computer Science from the University of Colorado (2006) and a Ph.D. in Computer Science from UC Berkeley (2012). His work bridges technical innovation with user-centered design principles, emphasizing ethical considerations in data visualization and inclusive representation. Key research contributions include: spatial visualization techniques for large environments, gesture-based interfaces for AR/VR, and physical data representations through projects like Cetonia (swarm robotics visualization) and Data Embroidery. His work has been recognized with Best Paper awards at CHI 2015 and Pervasive 2010. Current research emphasizes immersive analytics, wearable visualization systems, and demographically diverse anthropographics. He collaborates with urban designers, neurologists, and environmental scientists to apply visualization in diverse domains like epilepsy surgery planning and air quality monitoring.
Laurie Wilcox is a Full Professor in the Department of Biology at York University, affiliated with the Faculty of Science. Her research focuses on stereopsis, binocular vision, and depth perception, particularly exploring how the visual system processes binocular disparity signals. She leads a laboratory investigating cortical systems for fine and coarse disparities, with studies on amblyopia and applied collaborations with companies like Christie Digital and IMAX. Her work bridges basic neuroscience and applied research, addressing depth perception in 2D/3D displays and VR environments. Key interests include stereoscopic volume representation, perceptual grouping, and the impact of monovision on depth judgments. Recent studies examine lightness constancy in virtual reality, depth magnitude errors in 3D displays, and neural activation patterns in object-selective visual cortex. Wilcox has published extensively on binocular vision mechanisms, including coarse stereopsis in strabismus patients and the role of motion parallax in depth perception. Her applied projects evaluate visual fidelity in stereoscopic content, compression algorithms, and ergonomic considerations for XR devices. She also investigates how environmental context (e.g., familiar size, natural scenes) modulates depth perception accuracy across real and virtual environments. Her research emphasizes translational applications, aiming to optimize display technologies through insights from human visual processing. Ongoing work explores perceptual integration of binocular and monocular cues, attention modulation by depth, and the neurophysiological underpinnings of stereoscopic vision.
Bradley Buchsbaum is an Associate Professor and Senior Scientist at the Rotman Research Institute, Baycrest , Toronto, Canada. His research focuses on cognitive neuroscience, particularly on working memory, episodic memory, and functional neuroimaging using fMRI. PhD in Cognitive Science (2003) from University of California, Irvine BSc in Bio-Psychology (1997) from University of California, Santa Barbara His lab investigates how memories are stored, represented, and reactivated in the brain, combining functional neuroimaging, eye-tracking, and computational modeling. Key areas include multivariate statistics, machine learning applications in neuroimaging, and quantifying memory fidelity through behavioral and neural data. Recent publications emphasize neuroimaging methodology (e.g., MRI data consistency checks), memory pattern completion mechanisms, and feature-specific neural reactivation during episodic recall. Research trends highlight interdisciplinary approaches merging cognitive neuroscience with advanced statistical techniques. Lab team includes post-doctoral fellows (Stephen Rhodes), graduate students (Carolyn Guay, Corey Loo, Michael Bone, Nick Hoang, Ryan Barker), and research assistants. The lab actively develops open-source tools like rMVPA and neuroim2 for fMRI analysis.
