Kyros Kutulakos is a Professor in the Department of Computer Science at the University of Toronto, where he leads research in computational imaging and 3D sensing. His affiliations include the Toronto Computational Imaging Group, Computer Vision Group, Dynamic Graphics Project (DGP), and Vector Institute Group. He teaches graduate and undergraduate courses such as CSC320 (Introduction to Visual Computing) and CSC2530 (Computational Imaging & 3D Sensing). His research interests span computational imaging, non-line-of-sight imaging, single-photon detectors, 3D sensing, and neural rendering. Notable contributions include advancements in structured-light imaging, time-of-flight systems, and super-oscillatory microscopy. He has advised numerous PhD and MSc students, fostering cutting-edge research in imaging technologies. Kutulakos has received prestigious awards, including the Dean’s Research Excellence Award (2023) and multiple best paper prizes (e.g., Marr Prize at ICCV 2023). He has served as program chair for ICCV 2013, ICCP 2010, and CVPR 2003, contributing to academic leadership in computer vision. His work bridges optics, photonics, and computation, with applications in autonomous systems, medical imaging, and astronomy. Current research focuses on extreme imaging scenarios, such as imaging in pitch-black environments and around corners, leveraging novel sensor designs and computational techniques.
David B. Lindell is an Assistant Professor in the Department of Computer Science at the University of Toronto, with affiliations to the Vector Institute and AXL. He is a founding member of the Toronto Computational Imaging Group. His research focuses on physically based intelligent sensing, integrating physical models, signal processing, and AI to advance sensing systems. Notable projects include imaging around corners, through scattering media, and developing machine learning algorithms for 3D scene reconstruction. Education: Ph.D. in Computational Imaging from Stanford University (advisor: Gordon Wetzstein). Awards include the 2024 Ontario Early Researcher Award and the Best Student Paper at CVPR 2025. His work combines computational imaging with applications in computer graphics and autonomous systems. Research interests span non-line-of-sight imaging, single-photon sensing, and neural representations. Key contributions include the Light-Cone Transform (Nature 2018), confocal diffuse tomography (Nature Communications 2020), and AutoInt (CVPR 2021). His lab develops systems for 3D reconstruction, transient imaging, and photon-efficient sensors. Selected grants and support: NSF CAREER Award, DARPA REVEAL program, and KAUST Visual Computing Center funding. Active collaborations with industry and academic institutions on autonomous driving and medical imaging applications.
Dr. Jianguo Wang is a Professor in the Department of Earth and Space Science Engineering at York University's Lassonde School of Engineering. He has been a faculty member since 2006 and is a founding member of the Lassonde School. With over 35 years of academic and industrial experience, he specializes in multisensor integration, GNSS technology, and precision engineering surveying. He holds a Dr.-Ing. in Geomatics Engineering from Universität der Bundeswehr München, Germany, alongside Bachelor’s and Master’s degrees from Wuhan Technical University of Surveying and Mapping (WTUSM). His research focuses on advanced data processing methodologies, including Kalman filtering, error analysis, and LiDAR systems. He has authored/co-authored over 60 publications, including textbooks like Error Theory and Foundation of Surveying Adjustment and Foundation of Geodesy . He is a Fellow of Engineers Canada and licensed as a Professional Engineer in Ontario. Education: Dr.-Ing., Geomatics Engineering, Universität der Bundeswehr München (Germany) M.Sc., Surveying Engineering, Wuhan Technical University of Surveying and Mapping B.Sc., Surveying Engineering, Wuhan Technical University of Surveying and Mapping Dr. Wang teaches courses such as Advanced Optimization and Applications , GNSS , and Global Geophysics and Geodesy . He leads the Earth Observation Laboratory (PSE 432), focusing on multisensor integration for navigation and positioning. His work explores innovative solutions for sensor calibration, data fusion, and geospatial applications. Grants & Labs: Active in lab-based research with collaborators like Baoxin Hu, his laboratory integrates GNSS, IMUs, LiDAR, and cameras for precision navigation. His recent work addresses challenges in sensor error calibration, LiDAR point cloud accuracy, and Kalman filter enhancements.
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.
WonSook Lee is a tenured Full Professor in the School of Electrical Engineering and Computer Science at the University of Ottawa’s Faculty of Engineering. Her expertise spans medical imaging, machine/deep learning, computer graphics, and computer vision. She earned her Ph.D. in Computer Science from the University of Geneva (Switzerland) and holds degrees from POSTECH (Korea) and NUS (Singapore). Before academia, she worked at Korea Telecom, Samsung Advanced Institute of Technology, and Eyematic Interfaces Inc. (USA). Her research focuses on applications such as virtual/augmented reality, MRI/CT/Ultrasound analysis, and 3D mesh modeling. She has authored over 130 publications, including 30+ journal papers, and serves on conference committees and editorial boards. Lee has secured major grants (NSERC, CFI, ORF) as Principal Investigator and contributed to global initiatives like South Korea’s National Research Foundation. Her lab explores cutting-edge techniques in medical imaging, AI-driven object detection, and multimodal systems. Notable projects include adversarial perturbation analysis for model robustness, cross-domain GANs for semantic segmentation, and real-time ultrasound-enhanced pronunciation training. She actively promotes interdisciplinary research in healthcare technology and autonomous systems.
Pascale Biron is a Professor in the Department of Geography, Urban Planning and Environment at Concordia University, Montreal. She holds a Ph.D. in Geography from Université de Montréal (1995) and has been with Concordia since 1998. Her research focuses on river dynamics, stream restoration for fish habitat, flood modeling, and climate change impacts. She specializes in hydrogeomorphology, river management in agricultural watersheds, and numerical modeling of fluvial processes. Research Interests: Her work includes river restoration strategies, flood risk assessment using LiDAR technology, and the 'river freedom' concept promoting ecosystem resilience. She collaborates closely with government agencies to translate research into practical river management policies. Professional Affiliations: Canadian Geomorphology Research Group, Canadian Association of Geographers, American Geophysical Union, GRIL (Limnology Research Group), and RIISQ (Quebec Flood Risk Network). Publications & Research: Recent studies address global salmonid biomass patterns, fluvial hazard detection via machine learning, and large-scale flood modeling. She supervises 19 graduate students in Ph.D./M.Sc. programs in Geography and Environmental Studies, focusing on topics like river confluence hydraulics and agricultural stream restoration. Grants & Funding: Active projects include river dynamics in fish habitats, flood modeling for road infrastructure vulnerability, and computational fluid dynamics simulations of river flows. She also leads research on societal dimensions of river restoration and policy frameworks for flood resilience.
Soo Jeon is a Professor in the Department of Mechanical and Mechatronics Engineering at the University of Waterloo, part of the Faculty of Engineering. He holds a PhD from the University of California at Berkeley (2007) and prior degrees from Seoul National University. His research focuses on mechatronics, dynamic systems, and control, with applications in robotics, autonomous systems, and precision motion control. He has held roles as Assistant Professor (2009–2015), Associate Professor (2015–2024), and Full Professor (2024–present). Professor Jeon’s expertise includes intelligent sensing and control for mechatronic systems, nonlinear dynamics, and autonomous systems. He has received notable awards such as the 2022 Engineer of the Year Award (AKCSE/KOFST), 2015 NSERC Discovery Accelerator Supplement, and 2010 ASME Rudolf Kalman Best Paper Award. He serves as an Associate Editor for several journals, including the ASME Journal of Dynamic Systems and IEEE Transactions on Automation Science and Engineering. His research interests span robotics, control systems, and automation, with recent work on autonomous navigation, tactile exploration, and model predictive control. He supervises graduate students in MASc and PhD programs and teaches courses like ME 649 (Control of Machines and Processes) and ME 360 (Introduction to Control Systems). Jeon holds patents in areas like low-power magnetic locks and remote plasma source seasoning. His lab, the Waterloo Mechanical Systems & Control Laboratory (WMSCL), focuses on advanced mechatronics and robotics projects, including collaborations with international institutions such as the Korea Institute of Machinery & Materials (KIMM).
Mahmoud El-Sakka is an Associate Professor at the Department of Computer Science, University of Western Ontario since 1999. Previously, he was a faculty member at the University of Waterloo (1997–1999). He holds a B.Sc. and M.Sc. from Alexandria University (Egypt) and a Ph.D. in Systems Design Engineering from the University of Waterloo. His research focuses on medical imaging, image processing, and computer-aided diagnostics. He has served as Chair of the graduate program (2002–2007) and undergraduate program (2017–present) in Computer Science at Western Ontario. El-Sakka is a Senior Member of the IEEE and a licensed Professional Engineer in Ontario. His work spans grants from NSERC, internal university funding, and industry collaborations. Major research areas include image compression, segmentation, and medical applications like vascular analysis and echocardiography. He has led over 20 funded projects since 1999, emphasizing interdisciplinary approaches in healthcare technology. Academic contributions include advisory roles in summer programs, thesis evaluations, and conference participation. His service includes roles as Pro-Chancellor at convocations and involvement in equipment purchasing committees. Collaborations include consulting with NCR Canada and VRP Web Technology.
Dr. Pascale Biron is a Professor in the Department of Geography, Planning and Environment at Concordia University, Montreal. Her research focuses on river dynamics, stream restoration, flood modeling, and climate change impacts. She has been at Concordia since 1998 and holds professional affiliations with key organizations like the Canadian Geomorphology Research Group and the American Geophysical Union. Education: PhD from Université de Montréal and Leeds University (fluvial geomorphology) Research Interests Her work integrates fluvial geomorphology, computational fluid dynamics, and societal aspects of river management. Key themes include: River restoration for fish habitat Floodplain modeling using LiDAR Climate adaptation strategies Socio-hydrological dimensions of restoration Hydrodynamic processes at river confluences Recent Research Trends Recent publications emphasize global salmonid habitat analysis, machine learning for fluvial hazard detection, and the socio-environmental impacts of urban flooding. Her work bridges technical hydrology with policy-relevant solutions for sustainable water management. Grants & Advising Supervises over 15 graduate students (PhD/MSc) in topics ranging from flood modeling to bioengineering. Active in collaborative projects like the 'Freedom Space for Rivers' initiative and large-scale floodplain mapping. Labs & Teams Member of GRIL (Limnology Research Group) and RIISQ (Quebec Flood Network), contributing to interdisciplinary water security research.
Dr. Marzieh Amini is an Associate Professor at Carleton University, cross-appointed to the School of Information Technology and Department of Systems and Computer Engineering . She coordinates the Optical Systems and Sensors undergraduate program and leads research in computer vision, sensor fusion, and biomedical signal processing . PhD in Electrical and Computer Engineering (2016), Concordia University Postdoctoral Fellow (2020), McGill University Research Interests focus on autonomous vehicle perception systems integrating machine learning and statistical modeling . Her work addresses multi-sensor integration for reliable operation in diverse environments, including biomedical applications and critical infrastructure monitoring . Recent publications emphasize wildfire management , LiDAR-based infrastructure monitoring , and adverse weather adaptation in autonomous systems . She has received grants from NSERC, NRC, and FRQNT . Honors & Awards include: Volunteer Recognition Awards (IEEE Montreal, 2022 & 2019) FRQNT Postdoctoral Fellowship (2018) IEEE ISCAS Travel Support (2016) Professional Service includes leadership roles in IEEE committees and conference organization.
Dr. Philippe Dixon is an Assistant Professor in the Department of Kinesiology & Physical Education at McGill University, with a division in Biomechanics and Neuroscience. He holds adjunct professor roles at the University of Montreal (School of Kinesiology and Physical Activity Sciences) and the University of Laval (Department of Kinesiology). His research focuses on human movement biomechanics using motion capture systems and wearable sensors, combined with machine learning for health and athletic performance optimization. He has expertise in gait analysis, muscle coactivation patterns in cerebral palsy, and predictive modeling of physiological states. Dr. Dixon earned a Post-doctoral fellowship in Public Health at Harvard University, a PhD in Engineering Science from the University of Oxford, and dual degrees in Biomechanics and Physics from McGill University. His education includes a Bachelor of Education in Mathematics and Physics (McGill), a Master of Science in Biomechanics (McGill), and a PhD in Engineering Science (Oxford). He has received grants such as the NSERC Discovery Grant (2022–2027) and the FRQSC AUDACE Grant (2022). His work emphasizes wearable sensor integration, with contributions to datasets like NACOB and tools like OpenOFM. He currently supervises Master’s and PhD students in biomechanics and machine learning applications. Key research themes include gait adaptations on uneven surfaces, machine learning for cough detection via smart garments, and musculoskeletal coordination in clinical populations. His articles span biomechanical modeling, wearable sensor validation, and neuro-musculoskeletal analysis, reflecting interdisciplinary innovation in human movement science.
Craig Coburn is a Full Professor in the Department of Geography and Environment at the University of Lethbridge, where he has served since 2002 with promotions to Associate Professor in 2009 and Full Professor in 2019. His research centers on remote sensing physics, specializing in bidirectional reflectance properties and the development of low-cost remote sensing instrumentation for environmental monitoring and satellite calibration. Dr. Coburn's academic foundation includes: B.Sc. (Honours) in Geography from the University of Saskatchewan (1994) M.Sc. in Geography from the University of Alberta (1996) Ph.D. in Geography/Remote Sensing from Simon Fraser University (2002) His work spans instrument design to data processing algorithms, with emphasis on surface bidirectional reflectance. He pioneered world-leading goniometers and low-cost camera systems deployed via aircraft, UAVs, and high-altitude balloons for agricultural monitoring, biological system analysis, and global satellite validation. His research bridges theoretical physics with practical environmental applications. Analysis of his 2017-2021 publications reveals sustained focus on radiometric calibration, BRDF characterization, and sensor development. Key trends include interdisciplinary collaborations in atmospheric science (airborne metals), ecology (riparian systems), and soil science (erosion detection), alongside growing emphasis on UAV platforms and low-cost sensor validation for democratizing remote sensing. No scientific awards were specified in the source material. Dr. Coburn holds Principal Investigator status for the Prairie Farm Rehabilitation Administration-funded cattle wintering sites project ($45,000) and contributes as Co-Investigator to Alberta Ingenuity Centre for Water Research initiatives totaling $568,000. His grant portfolio spans riparian ecology, watershed analysis, and historical projects including National Land and Water Information System (Agriculture Canada) and Mountain Pine Beetle monitoring. His laboratory innovations include robotic goniometers for surface reflectance measurement and thermal imaging systems deployed globally for satellite calibration, supporting both research objectives and hands-on student training in remote sensing physics.
Waël Jaafar is a Professor in the Department of Software Engineering and IT at École de technologie supérieure (ETS), a constituent school of the Université du Québec system in Montreal, Canada. His research spans multiple critical domains in modern communications and computing infrastructure, with a particular focus on next-generation wireless networks and intelligent systems. Dr. Jaafar holds a B.Eng. from Sup'Com Tunisie, and both M.Sc.A. and Ph.D. degrees from Polytechnique Montréal. His academic background provides a strong foundation for his interdisciplinary research that bridges theoretical concepts with practical engineering solutions. His research interests center around wireless communications systems, with particular emphasis on 5G/6G networks, UAV communications, space telecommunications, and machine learning applications for networking. He has developed significant expertise in federated learning techniques for distributed networks, cybersecurity applications for next-generation mobile systems, and edge computing architectures. His work frequently explores the intersection of communication theory, artificial intelligence, and network security, with applications ranging from industrial IoT to public safety communications. Analysis of his recent publications reveals a strong trend toward integrating artificial intelligence with wireless networking infrastructure, particularly focusing on UAV-assisted communications, federated learning approaches for distributed networks, and security enhancements for 5G/6G systems. His research demonstrates increasing emphasis on practical implementation challenges including energy efficiency, communication overhead reduction, and reliability in non-ideal network conditions. As an academic supervisor, Dr. Jaafar actively mentors numerous graduate students across various projects. He currently supervises doctoral candidates working on blockchain-enhanced security for 5G networks, green network slice orchestration, and federated learning approaches for Open RAN architecture. His master's students are engaged in diverse topics including LiDAR-based power line monitoring, multimodal behavioral authentication, and 5G/6G security using AI techniques. Dr. Jaafar is affiliated with two prominent research laboratories at ETS: LASI (Computer System Architecture Research Laboratory) and LACIME (Communications and Microelectronic Integration Laboratory). At LASI, he contributes to research in AI-based systems engineering, resource orchestration in edge/cloud environments, and intelligent network design. Through LACIME, he engages with broader communications research spanning from microelectronic components to complex communication systems, with particular focus on wireless networks and signal processing applications.
Catherine Neish is an Associate Professor in the Department of Earth Sciences at The University of Western Ontario. She serves as the Associate Director of Research for Western Space and is a Co-Investigator on NASA's Dragonfly mission to Titan. Her research focuses on planetary radar observations, impact cratering processes, and the geological evolution of planetary surfaces, particularly on the Moon, Titan, and other Solar System bodies. Ph.D. in Planetary Sciences, University of Arizona (2008) B.Sc. in Combined Honours Physics and Astronomy, University of British Columbia (2004) Her recent publications highlight studies on lunar impact crater thermophysics, Titan's impact melt dynamics, and radar-based analyses of planetary surfaces. She has contributed to missions including Lunar Reconnaissance Orbiter (Mini-RF), Cassini RADAR, and Dragonfly. Scientific Awards: College of New Scholars, Royal Society of Canada (2021) Early Researcher Award, Ontario (2017) Minor Planet 16972 Neish (2017) AGU Ron Greeley Award (2014) NASA Postdoctoral Fellowship (2012) NASA Group Achievement Award (2010) NSERC Postgraduate Scholarship (2005-2008) Julie Payette-NSERC Research Scholarship (2004-2005) Dr. Neish supervises a dynamic lab with current and former students investigating planetary geology, impact cratering, and remote sensing. Her work bridges field studies (e.g., Earth analogs), laboratory experiments, and spacecraft data analysis.
Gunho Sohn is an Associate Professor and Department Chair in the Earth and Space Science and Engineering (ESSE) Department at York University's Lassonde School of Engineering. His research focuses on advanced geomatics engineering applications, including 3D urban modeling, photogrammetric computer vision, and geospatial data integration. He specializes in developing innovative solutions for navigation systems, energy optimization, and autonomous robotics through interdisciplinary approaches. Dr. Sohn's work emphasizes practical implementations of remote sensing technologies, with notable contributions to LiDAR data processing, SLAM systems, and BIM-GIS integration. His research has addressed real-world challenges such as improving air quality models using industrial plume observations and creating inclusive pedestrian navigation tools using open geospatial datasets. Recent trends in his publications highlight advancements in deep learning for geospatial tasks, including semantic segmentation of aerial LiDAR data, noise reduction in sensor networks, and UAV positioning systems. His work also explores digital twin applications for simulating urban environments and optimizing building energy consumption through BIM data analysis. While no specific awards or grants are listed, his extensive publication record reflects significant contributions to the fields of geomatics and computer vision. His research group collaborates on large-scale datasets like YUTO MMS and Yuto Semantic, advancing mobile mapping and semantic understanding of urban infrastructure.
