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.
Geoffrey E. Hinton is a distinguished Professor in the Department of Computer Science at the University of Toronto. He is renowned for his foundational contributions to machine learning, particularly in the development of deep learning and neural networks. His research focuses on understanding learning processes in both artificial and biological systems, with key contributions including Boltzmann machines, backpropagation, and deep belief networks. He teaches advanced machine learning courses such as CSC2535, emphasizing topics like graphical models, variational inference, and deep learning architectures. His work has been published extensively in top journals and conferences, with recent papers exploring forward-forward algorithms, analog diffusion models, and scalable neural network training methods. Hinton has advised numerous PhD and master's students and collaborates with institutions like Vector Institute. He is a central figure in the global AI community, regularly presenting at conferences (e.g., 2023 talks on CBS 60 Minutes, BBC, and PBS). His lab focuses on advancing machine learning theory and applications, addressing challenges in vision, language, and generative models.
Dr. Keith Alexander Sharkey is a Professor in the Department of Physiology and Pharmacology at the Cumming School of Medicine, University of Calgary. He is also a Full Member of both the Hotchkiss Brain Institute and The Calvin, Phoebe and Joan Snyder Institute for Chronic Diseases. Dr. Sharkey has been at the University of Calgary since his appointment as Assistant Professor in 1990, dedicating his career to understanding the neural control of the gastrointestinal tract. From 2005-2021, he held the Crohn's and Colitis Canada Chair in Inflammatory Bowel Disease Research, demonstrating his longstanding commitment to advancing knowledge in gastrointestinal sciences. Dr. Sharkey's educational journey began with a BSc (Hons) in Nutrition from the University of London in 1981, followed by a PhD in Physiology from the University of Liverpool in 1985. After completing postdoctoral training in Hungary, the UK, and Canada, he joined the University of Calgary faculty in 1990, where he has remained throughout his distinguished academic career. Dr. Sharkey's research program centers on understanding the physiology and pathophysiology of the neural control of the gastrointestinal tract and brain-gut interactions in health and disease. His laboratory investigates two main areas: the role of the endocannabinoid system in regulating GI motility and intestinal barrier function, and the physiology of the enteric nervous system in controlling motility and barrier function. His work employs state-of-the-art approaches including live cell imaging and molecular genetics. Recent publications show his expanding interest in connections between gut function and neurodegenerative conditions like ALS, with multiple 2024-2025 papers examining sexual dimorphism in disease processes and gut-brain axis mechanisms. Fellow, Royal Society of Canada (2024) Cumming School of Medicine's van de Sande Distinguished Achievement Award for Mentorship (2021) Distinguished Research Award, American Physiological Society (2021) Finkelstein Award for Excellence, Crohn's and Colitis Canada (2016) Fellow, Canadian Academy of Health Sciences (2015) Fellow, Canadian Association of Gastroenterology (2015) Killam Annual Professor Award, University of Calgary (2013) Dr. Sharkey has mentored numerous students throughout his career, as evidenced by his 2021 Mentorship Award. His research is supported by significant funding, including a CIHR Foundation grant exploring how enteric nerves and glia maintain intestinal homeostasis. He has also participated in university strategic initiatives focused on Brain and Mental Health (2015-2021) and Infections, Inflammations and Chronic Diseases (2015-2021), demonstrating his interdisciplinary approach. The Sharkey Lab provides a safe, inclusive, and diverse research environment that encourages open scientific exchange from diverse viewpoints. His laboratory is actively engaged in multiple collaborative projects, including investigations with UBC Okanagan on diet and probiotics targeting the gut-brain metabolic interactome, and with University of Calgary colleagues studying gut microbiota's role in ALS. Dr. Sharkey continues to advance our understanding of neural mechanisms controlling gastrointestinal function with upcoming courses scheduled for Fall 2024 and Winter 2025.
Sriram Subramaniam is a Professor in the Department of Biochemistry and Molecular Biology at the University of British Columbia (UBC) and holds the Gobind Khorana Canada Excellence Research Chair in Precision Cancer Drug Design. His research leverages cryo-electron microscopy (cryo-EM) to advance structural biology and drug design, focusing on protein dynamics and therapeutic target identification. Education: PhD in Physical Chemistry (1987) from Stanford University; MSc in Chemistry (1981) from Indian Institute of Technology, Kanpur. Subramaniam's interdisciplinary work combines cryo-EM with computational tools and molecular biology to study protein structures at atomic resolution. His lab has pioneered cryo-EM applications in precision medicine, including mapping small molecule drugs on patient-specific cancer mutants. Recent publications (2024-2022) highlight his contributions to understanding SARS-CoV-2 immune evasion, structural mechanisms of ATPases, and AI integration in structural biology. His research spans viral entry mechanisms, CRISPR systems, and neurodegenerative disease pathways. Scientific Awards: Gobind Khorana Canada Excellence Research Chair NIH Director’s Award for Scientific Excellence Fellow of the Biophysical Society Breakthrough Prize nomination Based at the Djavad Mowafaghian Center for Brain Health, Subramaniam leads the Program in Cryo-EM Guided Drug Design, contributing to over 177 peer-reviewed publications with a career h-index of 58 and citations exceeding 12,340.
Adrien Desjardins is a Professor at the University of British Columbia, jointly appointed in the Department of Mechanical Engineering and Department of Electrical and Computer Engineering within the Faculty of Applied Science. He joined UBC in 2024 after serving as a Full Professor at University College London from 2019-2024, following 13 years on faculty there. His educational background includes a B.Sc. from UBC (2001) and a Ph.D. from MIT and Harvard University (2007). Dr. Desjardins' research program focuses on interdisciplinary development of imaging and sensing modalities and autonomous robotics with marine and biomedical applications. His work integrates photonics, ultrasound, machine learning, and robotics to create innovative diagnostic tools and sensing systems, particularly in optical coherence tomography, diffuse optical spectroscopy, and photoacoustic imaging. His publication record reveals an evolution from foundational neuroimaging work (2001) toward increasingly sophisticated optical systems culminating in breakthroughs like ultrasensitive optical microresonators for ultrasound sensing (2017), demonstrating consistent innovation in biomedical optics with growing emphasis on machine learning integration and real-world applications. His scientific contributions have been recognized through prestigious awards: Research Chair from the Royal Academy of Engineering Healthcare Technologies Challenge Award from EPSRC Starting grants from ERC, EPSRC, and Royal Society World Economic Forum Young Scientist (2015) UCL Provost Teaching Prize (2013) Dr. Desjardins actively mentors graduate students and secures major research funding through competitive grants, with current openings for January/September 2025 intakes. His program involves close industry collaboration indicating strong translational focus, though specific lab names aren't mentioned. The interdisciplinary nature of his work suggests teams spanning engineering, computer science, and medical disciplines working on next-generation imaging systems for healthcare and marine exploration.
Tim G. J. Rudner is an Assistant Professor in the Department of Statistical Sciences at the University of Toronto, a Faculty Member at the Vector Institute, and a Title A Fellow at Trinity College, University of Cambridge. He was previously an Assistant Professor and Faculty Fellow at New York University. University: University of Toronto School: Faculty of Arts and Science Department: Department of Statistical Sciences Affiliation: Vector Institute, Trinity College (Cambridge) He holds a PhD in Computer Science and an MSc in Statistics from the University of Oxford, where he was advised by Yee Whye Teh and Yarin Gal, and a BS in Applied Mathematics and Economics from Yale University. PhD: Computer Science, University of Oxford MSc: Statistics, University of Oxford BS: Applied Mathematics and Economics, Yale University His research focuses on building robust, transparent, and trustworthy machine learning systems, particularly for high-stakes applications. He develops probabilistic models that improve generalization under distribution shifts, provide reliable uncertainty estimates, and enable fair and interpretable predictions. His work spans generative models, large language models, healthcare, and biomedical discovery. The recent publications highlight a strong trend toward function-space modeling, Bayesian regularization, and AI safety. Tim's work emphasizes principled uncertainty quantification, robustness to subpopulation and semantic shifts, and the development of frameworks for AI governance and specification. His research bridges theoretical advances with real-world applications, especially in safety-critical domains like medicine and defense. Tim has received numerous accolades including being named a Rhodes Scholar, Qualcomm Innovation Fellow, and 2024 Rising Star in Generative AI. He was awarded a $700,000 Foundational Research Grant and a $30,000 Apple Seed Grant for improving LLM trustworthiness. Rhodes Scholar Qualcomm Innovation Fellow AISTATS Notable Paper Award (2024) Outstanding Paper Award, ICLR GenAI4DM Workshop (2024) Apple Seed Grant ($30,000) Foundational Research Grant ($700,000) NeurIPS Spotlight Talk 2024 Rising Star in Generative AI He actively mentors students, particularly first-generation and low-income scholars, and has contributed to major policy frameworks including the OECD AI Classification Framework and a series of CSET issue briefs on AI safety. His work demonstrates a strong commitment to responsible AI development, combining technical rigor with societal impact. Tim leads research efforts at the intersection of machine learning theory and practical deployment, with ongoing projects in generative modeling, reliable LLMs, and AI governance. His lab produces high-impact work regularly published at top-tier conferences such as NeurIPS, ICML, and AISTATS.
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.
David Vocadlo is a Distinguished Professor of Chemistry and Molecular Biology & Biochemistry at Simon Fraser University (SFU), holding the Canada Research Chair in Chemical Biology. His research focuses on Chemical Glycobiology, investigating carbohydrate-processing enzymes and developing chemical tools to study glycan roles in health and disease. His lab explores O-GlcNAc signaling, neurodegenerative disorders (e.g., Alzheimer’s, Parkinson’s), and enzyme inhibitors for therapeutic applications. Education: PhD from University of British Columbia (UBC), followed by a CIHR postdoctoral fellowship at UC Berkeley. Key roles include E.W.R. Steacie Memorial Fellow and Royal Society Fellow. Research highlights include O-GlcNAcase inhibitors for neuroprotection, glycan structure-function relationships, and enzyme activity imaging tools. Collaborates globally with experts in glycobiology and employs cutting-edge techniques like chemical synthesis, mass spectrometry, and live-cell imaging. Awards: Distinguished Professor title, Canada Research Chair, Royal Society Fellowship. Active in training researchers through SFU’s graduate programs, emphasizing interdisciplinary approaches. Lab members work on topics ranging from enzyme mechanisms to disease modeling.
Arash Arami is an Associate Professor in the Department of Mechanical and Mechatronics Engineering at the University of Waterloo, cross-appointed in Systems Design Engineering. He directs the Neuromechanics and Assistive Robotics Laboratory and maintains affiliations with Waterloo Robohub, the Centre for Bioengineering and Biotechnology, Waterloo AI institute, and KITE institute at Toronto Rehab Institute. He earned his Doctorate in Electrical Engineering from EPFL (2014), Master of Science from University of Tehran (2009), and Bachelor of Science from University of Tabriz (2006), all in Control Engineering. His research in Assistive Robotics and Rehabilitation Engineering integrates Machine Learning with Neuromechanics to develop intelligent systems for human movement analysis. Key focus areas include exoskeleton control algorithms, wearable sensor systems, and neural control modeling for rehabilitation applications. Recent publications demonstrate interdisciplinary work spanning robotics, biomedical engineering, and materials science, with emphasis on real-time human locomotion prediction, exoskeleton-human interaction, and data-driven health monitoring solutions. Dr. Arami serves as Chair of the NSERC Scholarship Committee (2021-2023) and mentors graduate students through the Mechatronics Exchange Study program. His teaching includes core courses in control systems, robot manipulators, and biomechanical engineering. The Neuromechanics and Assistive Robotics Laboratory fosters collaborations with clinical partners at Toronto Rehab Institute, focusing on translating robotic innovations into practical rehabilitation tools through interdisciplinary teamwork.
Houman Savoji is an Associate Professor in the Department of Pharmacology and Physiology at the Faculty of Medicine, University of Montreal. He is also a full-time researcher at the CHU Sainte-Justine Research Center and principal investigator in regenerative medicine, organs-on-chip, and bioprinting at TransMedTech Institute. Dr. Savoji received his PhD in Biomedical Engineering from the Institute of Biomedical Engineering at Polytechnique Montréal in 2016. He then completed a postdoctoral fellowship at the Institute of Biomaterials and Biomedical Engineering at the University of Toronto. His research expertise combines advanced manufacturing technologies (micro- and nano-fabrication, 3D bioprinting, microfluidics, cell electrospinning) with functional and composite materials for applications in tissue engineering, regenerative medicine, and organs-on-chip. His work focuses on the design, development, optimization, implementation, and characterization of innovative functional biomaterials using emerging engineering technologies, with particular emphasis on cardiac tissue engineering and biomimetic pulmonary heart valves for pediatric patients. Dr. Savoji has published extensively on biomaterials, tissue engineering, 3D bioprinting, and organ-on-chip technologies. His recent publications demonstrate expertise in viscoelastic characterization of soft tissues, engineering immune responses to biomaterials, ceramic engineering for biomedical applications, and advanced 3D bioprinting techniques for cardiac and vascular tissue engineering. 2017-2020, Postdoctoral Research Grant, CIHR 2017-2019, Postdoctoral Research Grant, FRQNT 2017-2018, Human Society of International Grant, Human Toxicity Assessment Project 2016, CR-CHUM Research Center Award 2015, Star Student-Researcher Award, FRQNT 2014-2015, Jane and Frank Warchol Fellowship, Society of Vacuum Coaters Foundation 2013, Institute of Textile Science Award 2012-2015, Excellence Doctoral Scholarship for Foreign Students, FRQNT Dr. Savoji has supervised Master's students including Ines Barrakad (2024) working on 'Advanced manufacturing technologies versus molding of corneal implants: 3D printing vs molding of a Keratoprosthesis' and Zineb Ajji (2023) researching 'Development of perfusable patches by 3D bioprinting for potential application in cardiac tissue regeneration.' He has secured numerous research grants from organizations including CIHR, NSERC, FRQNT, FRQS, MITACS, and others for projects related to 3D bioprinting of cardiac tissues, biomimetic heart valves, and other tissue engineering applications. The Savoji Laboratory, located within the Department of Pharmacology and Physiology and Institute of Biomedical Engineering of the Faculty of Medicine of the University of Montreal, the Research Center of the CHU Sainte-Justine (CHUSJ), and the TransMedTech Institute, focuses on multidisciplinary research involving 3D bioprinting using stem-cell derived human cardiac cells to fabricate functional cardiac tissues for transplantation and drug discovery applications.
Qingguo Li is a Professor and Associate Head at the Department of Mechanical and Materials Engineering , Queen's University , and a member of the Ingenuity Labs Research Institute . He specializes in biomechanical system design, energy harvesting, wearable sensors, gait analysis, and load carriage systems. His research integrates robotics, biomedical engineering, and sensor technology to develop human-centric devices and mobility aids. Current Roles : Professor, Associate Head, Queen's University Research Institute : Ingenuity Labs Research Institute Lab : Bio-Mechatronics and Robotics Laboratory His work focuses on biomechanical energy harvesting , IMU-based motion analysis , and assistive device development . Key applications include stroke rehabilitation, gait monitoring, and wearable power generation systems. Articles span cable-driven robots , smart walkers , and 3D printing mechanisms , emphasizing human-robot interaction and dynamic modeling . The lab explores sensor calibration , adaptive control algorithms , and human movement optimization . Areas of impact include rehabilitation engineering , load carriage stability , wearable sensor accuracy , and assistive robotics . His team develops solutions for gait asymmetry detection , post-stroke mobility , and low-cost energy systems , leveraging machine learning and kinetic modeling .
Professor Peter F. Driessen is a faculty member in the Department of Electrical and Computer Engineering at the University of Victoria, with a cross-appointment in the School of Music. He holds a BSc and PhD from the University of Victoria and is a Professional Engineer (PEng). His research focuses on communication systems, signal processing, control, and interdisciplinary projects in computer music and wireless technologies. Key areas include audio/video signal processing, radio propagation, sound recording, and multimedia systems. He leads the University of Victoria Propagation Laboratory, which explores radio wave propagation and Amateur radio integration with engineering education. His work spans theoretical research and applied projects like ECOSat satellite systems, software-defined radio (SDR), and innovative musical instruments such as the Radio Drum. He supervises undergraduate and graduate projects in these domains through ELEC 499 courses. Notable contributions include the APEGBC Editorial Board Award for Best Paper (2002) and patents in wireless networking and signal processing. His teaching includes courses in signal analysis and electromagnetics, and he collaborates on interdisciplinary programs like the Music/Computer Science degree. Education: BSc in Electrical Engineering, University of Victoria PhD in Electrical Engineering, University of Victoria Research Interests: Audio and video signal processing for music and media Software-defined radio and Amateur radio technologies Satellite communication and ground station development Gesture-based interfaces and musical instrument design Error mitigation in streaming audio/video Optical and microwave-photonic systems Labs & Collaborations: Propagation Laboratory (radio wave research) UVic Experimental Radio Group (Amateur radio club) UVic Satellite Design Team (ECOSat projects) UVic Centre for Aerospace Research Grants & Awards: APEGBC Editorial Board Award (2002) Multiple US patents in wireless systems and signal processing
Professor Dirk Bernhardt-Walther is an academic at the University of Toronto, serving as Program Director of the Cognitive Science Program and Department of Psychology . He investigates neural and computational mechanisms underlying high-level sensory perception, focusing on real-world scenes, mid-level vision, and visual aesthetics. Education: PhD in Computation and Neural Systems (Caltech, 2006), M.Phil (University of Cambridge) Research Focus: His lab employs fMRI, MEG, EEG , and GAN-generated stimuli to study scene categorization, perceptual organization, and aesthetic processing. Recent work explores curvature perception, emotion representation in scenes, and neural dissociations between computational and subjective visual metrics. Laboratory Members: The Bernhardt-Walther Lab includes PhD students like Gaeun Son (scene perception), Charlotte Leferink (scene representation), and Dela Farzanfar (aesthetic processing), alongside postdocs and collaborators. Advising: Supervises graduate students in projects combining computational modeling, psychophysics, and neuroimaging, particularly those with backgrounds in computer science or cognitive neuroscience.
Angel Xuan Chang is an Associate Professor at Simon Fraser University's School of Computing Science, where she leads research at the intersection of natural language processing, computer vision, and 3D scene understanding. She holds the prestigious Canada CIFAR AI Chair position and is affiliated with multiple research groups including 3DLG, GrUVi, SFU NatLang, SFU AI/ML, and VINCI. PhD in Computer Science, Stanford University MSc in Computer Science, Stanford University M.Eng in Electrical Engineering and Computer Science, MIT BSc in Computer Science and Engineering, MIT Professor Chang's research primarily focuses on connecting language to 3D representations of shapes and scenes, with particular emphasis on grounding language for embodied agents in indoor environments. Her work spans natural language processing and understanding, linking natural language with visual and 3D representations, multimodal grounding of language, embodied AI, and machine learning applications for biodiversity monitoring through the BIOSCAN project. She has developed methods for synthesizing 3D scenes and shapes from natural language and created various datasets for 3D scene understanding. Her recent publications reveal a strong trend toward integrating language understanding with 3D scene generation and manipulation, with increasing focus on practical applications in embodied AI and biodiversity monitoring. The research shows progression from foundational work on text-to-3D scene generation to more sophisticated approaches for evaluating semantic coherence in generated scenes and developing efficient methods for zero-shot scene modeling. Canada CIFAR AI Chair TUM-IAS Hans Fischer Fellow (2018-2022) Best paper award at 3DV 2025 for 'An Object is Worth 64x64 Pixels: Generating 3D Object via Image Diffusion' Professor Chang actively advises numerous graduate students who appear as first authors on her publications, indicating a strong mentoring program. Her research is supported through multiple channels including the CIFAR AI Chair position and likely various research grants supporting her BIOSCAN-related work and 3D scene understanding projects. She has been involved in organizing multiple workshops at major conferences including ICML, CVPR, and ICLR. Her research is conducted through several interconnected groups: 3DLG (3D Language and Graphics), GrUVi (Graphics, Vision, and Interaction), SFU NatLang (Natural Language Processing), SFU AI/ML, and VINCI. These groups work collaboratively on problems spanning language grounding, 3D scene understanding, embodied AI, and biodiversity applications, creating a rich interdisciplinary research environment.
Yi Gu is an Associate Professor of modern and contemporary art and visual culture at the University of Toronto Scarborough (UTSC), with cross-appointments in the Department of Arts, Culture and Media and the Department of East Asian Studies. Her scholarly work centers on Asian visual culture, particularly China's artistic and photographic history from the early twentieth century to the present. Her research interests include cold war visual culture, post-socialist art, Chinese photography history, contemporary photography in Asia, mass art and amateurism, and visual methodologies across disciplines. Gu's work is driven by examining the political ambitions and social functions of art, as well as the politics of aesthetics. Her recent research has focused on aesthetic capitalism and digital surveillance in China, leading to studies of the Digital Countryside initiative and CCP aesthetics anchored in agrarian imaginary. Gu's publications reveal a consistent engagement with visual epistemology, particularly how Chinese artists and photographers have negotiated modernity, nation-building, and transnational influences. Her work spans from early twentieth-century open-air painting practices to contemporary digital visual culture, demonstrating how visual practices serve as critical sites for understanding China's social and political transformations. As co-editor of the open-access academic journal Trans Asia Photography and convening member of the research project 'Alternative Collections and Digital Humanities: Twentieth-Century Chinese Art and Visual Culture,' Gu actively contributes to expanding the field of Asian visual studies through collaborative scholarly initiatives. At UTSC, Gu teaches undergraduate courses on modern and contemporary Asian art, lens-based media in Asia, public art, and art and digital culture. Her scholarly approach combines art historical methods with visual culture studies, media theory, and cross-disciplinary perspectives to examine how visual practices shape and are shaped by political and social contexts in China and beyond.
