Find Academic Supervisors

• Machine Learning
• Artificial Intelligence
• Neural Networks
• +3 more

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.

Adrien DesjardinsView profile

Professor

• Imaging and sensing
• Robotics
• Machine learning
• +6 more

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.

Wenhu ChenView profile

Assistant Professor

• Natural Language Processing
• Deep Learning
• Multimodal Learning
• +5 more

Wenhu Chen is an Assistant Professor at the University of Waterloo's Computer Science Department and a CIFAR AI Chair at the Vector Institute. He also holds a part-time role as a Senior Research Scientist at Google DeepMind (20% allocation). His research focuses on natural language processing, deep learning, and multimodal reasoning, with contributions to models like MAmmoTH, OpenCoderInterpreter, and VISTA. He received awards including the Canada CIFAR AI Chair (2022) and the UCSB CS Outstanding Dissertation Award (2021). Education: PhD in Computer Science from the University of California, Santa Barbara (under William Wang and Xifeng Yan). Research interests include complex reasoning, controllable GenAI, and multimodal benchmarks like MEGABench and MMMU. Grants include CIFAR AI Chair Funding (2022-2027), NSERC Discovery Fund (2023-2028), and multiple NRC Canada grants. He directs the TIGER Lab, advancing generative models in text, images, videos, and music. Recent talks include presentations on multimodal reasoning at Apple and NeurIPS workshops.

John ZelekView profile

Associate Professor

• Robotics
• Computer Vision
• Artificial Intelligence
• +6 more

John Zelek is an Associate Professor in the Department of Systems Design Engineering at the University of Waterloo. He co-directs the VIP (Vision & Image Processing) lab and previously served as Associate Graduate Chair (2013-2017). He co-founded two startups: Tactile Sight (haptic navigation for disabled individuals) and Sweep3D (3D modeling technology). His research focuses on autonomous robotics, 3D scene understanding, infrastructure assessment, medical imaging, and sports analytics using AI/deep learning techniques. Education includes a BASc from Waterloo (1985), MASc from Ottawa (1989), and PhD from McGill (1996). He teaches courses like SYDE 283 (Physics), SYDE 572 (Pattern Recognition), and SYDE 675 (Pattern Recognition). Research interests span robotics, computer vision, anomaly detection, and SLAM. His work applies to infrastructure monitoring, sports analytics (hockey/pitcher analysis), medical imaging (OCT/fundus), and assistive technologies. Recent publications emphasize 3D modeling, SLAM enhancements, and sports tracking algorithms. Zelek advises graduate students (SSPS status) and collaborates with companies like Intelligent Health Solutions and EyeCheck through advisory roles. Key innovations include hybrid SLAM systems, puck localization algorithms, and medical robotic swab systems demonstrated on moving phantoms.

Peter F. DriessenView profile

Professor

• Communication
• Signal Processing
• Control Systems
• +6 more

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

Ian StavnessView profile

Professor

• Biomedical Computation & Visualization
• Computer Graphics
• 3D Displays
• +6 more

Dr. Ian Stavness is a Professor and Department Head in the Department of Computer Science at the University of Saskatchewan. His research focuses on interdisciplinary applications of computer science, including deep learning in agriculture, biomedical computation, and 3D display technologies. He leads the Biological Imaging & Graphics (BIGLAB) laboratory, which develops tools for plant phenotyping and musculoskeletal modeling. Education: Ph.D. in Computer Engineering, University of British Columbia, 2010 M.A.Sc. in Computer Engineering, University of British Columbia, 2006 B.Sc. in Computer Science & B.Eng. in Electrical Engineering, University of Saskatchewan, 2004 Research Interests: Deep Learning for Plant Phenomics & Agriculture 3D Displays and VR/AR Technologies Musculoskeletal Biomechanical Simulation (OpenSim, ArtiSynth) Computer Vision and Image Analysis Awards: ACM CHI 2019 Honourable Mention ACM VRST 2018 Polyphony Digital Award Key Projects: Deep Plant Phenomics Platform Parametric Human Project (Digital Human Modeling) P2IRC (Plant Phenotyping & Imaging Research Center)

WonSook LeeView profile

Professor

• Medical Imaging
• Machine Learning
• Deep Learning
• +4 more

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.

Marco PedersoliView profile

Assistant Professor

• Computer Vision
• Machine Learning
• Deep Learning
• +4 more

Marco Pedersoli serves as an Assistant Professor at École de technologie supérieure (ETS) in Montreal since February 2017, where he leads research in computer vision and machine learning. His work focuses on reducing computational costs and annotation requirements for deploying vision algorithms on embedded devices, positioning ETS at the forefront of Montreal's AI ecosystem. His academic journey includes: Ph.D. from Autonomous University of Barcelona (UAB) under Jordi Gonzàlez and Juan José Villanueva Post-doctoral research at INRIA Grenoble with Cordelia Schmid and Jakob Verbeek (2015-2016) Research at KU Leuven with Tinne Tuytelaars (2012-2015) Dr. Pedersoli's research tackles deep learning bottlenecks through weakly-supervised methodologies and computational efficiency innovations . His three core projects address: Reduced Supervision : Developing weakly/semi-supervised learning for images, video, audio and text Exploration Learning : Optimizing data selection in unstructured environments Efficient Computation : Accelerating deep learning training and inference These efforts enable vision algorithms to run on resource-constrained portable devices. Publication trends (2014-2022) reveal consistent focus on weak supervision (60% of works) and computational efficiency (30%), with recent expansion into medical imaging and multimodal emotion recognition. Key venues include CVPR, ICCV, NeurIPS and ECCV. His accolades include: Best Paper Award at ICIAR 2019 NVIDIA Titan X Pascal hardware donation Dr. Pedersoli actively mentors 18 graduate students across PhD and MSc programs, with notable placements at Huawei and Radio Canada. His lab secures competitive tax-free funding for projects with international collaborations, including Element AI and European institutions. Current openings emphasize Python/C++ proficiency and deep learning expertise. He leads a dynamic research group at ETS developing open-source tools for Roi-Pooling, weakly-supervised detection, and 3D object recognition, maintaining active GitHub repositories with community contributions. Recent WACV 2023 acceptances demonstrate ongoing productivity following medical leave.

M. Omair AhmadView profile

Professor

• Signal Processing
• Digital Image Processing
• Video Signal Processing
• +12 more

M. Omair Ahmad is a Professor and Tier I Concordia Research Chair in Multimedia Signal Processing at the Department of Electrical and Computer Engineering, Concordia University. He has held leadership roles, including Chair of his department from June 2002 to May 2005, and active involvement in IEEE committees. Education: Ph.D. in Electrical Engineering, Concordia University (1978) B.Eng. in Electrical Engineering, Sir George Williams University (Montreal, QC, Canada) Dr. Ahmad's research spans diverse areas of signal processing, including speech, image, and video processing, biomedical imaging, computer vision, and VLSI signal processing. His work addresses both theoretical and applied challenges in these domains, with applications in communication systems, object detection, and biometrics. Dr. Ahmad has contributed extensively to academia through leadership roles in conferences and editorial work. He served as an Associate Editor for the IEEE TRANSACTIONS ON CIRCUITS AND SYSTEMS PART I and held chairs in IEEE symposia, including the Local Arrangements Chairman for the 1984 IEEE International Symposium on Circuits and Systems. Scientific Awards and Honors: Wighton Fellowship from the Sandford Fleming Foundation Induction to Provosts Circle of Distinction for Career Achievements Award of Excellence in Doctoral Supervision IEEE Fellow

Niko TrojeView profile

Professor

• Vision Science
• Biological Motion Perception
• Cognitive Psychology
• +5 more

Niko Troje is a Professor at York University, affiliated with the Departments of Psychology, Biology, and Electrical Engineering & Computer Science. He holds cross-appointments and leadership roles, including Director of the BioMotion Lab. His research focuses on perceptual representations, biological motion, and vision science. Education: Ph.D. in Biology (1994) - Albert-Ludwigs Universität B.Sc. in Biology (1990), Physics & Mathematics (1987) - Albert-Ludwigs Universität Research Interests: Troje investigates how the brain processes biological motion, perception of human and animal movement, and applications in virtual reality. His work bridges neuroscience, psychology, and computer science. Key Contributions: Pioneered point-light displays for motion perception, explored gait analysis in mental health, and developed tools for motion capture and analysis (e.g., bmlTUX). Awards: Humboldt Research Prize (2014) NSERC Steacie Fellowship (2008-2009) Canada Research Chair (2003-2013) Grants & Labs: Led funded projects on movement perception, collaborated with institutions like the Max Planck Institute, and directs the BioMotion Lab at York University.

Bryan TrippView profile

Associate Professor

• Computational Neuroscience
• Deep Learning
• Robotics
• +1 more

Bryan Tripp is an Associate Professor at the University of Waterloo, specializing in computational neuroscience, deep learning, robotics, and medical AI. He leads the BRAIN Lab, which focuses on developing neural system models that interact with the physical world through robots. His research integrates neurobiological models with advanced machine learning techniques to study visuomotor processes and robotic applications. Tripp teaches courses such as Computational Neuroscience (SYDE 552), Deep Learning (SYDE 577), and Biomedical Engineering Design Workshops (BME 461/462). His lab has achieved milestones including the OREO robotic head, the first spiking neural network model for complex action planning, and comprehensive datasets for robotic grasping. His recent work emphasizes Medical AI applications, with graduate positions available. The BRAIN Lab is affiliated with the Centre for Theoretical Neuroscience and Waterloo.AI, contributing to interdisciplinary AI research initiatives.

Sadaf SalehkalaibarView profile

Assistant Professor

• Artificial Intelligence
• Machine Learning
• Generative models
• +5 more

Sadaf Salehkalaibar is an Assistant Professor in the Department of Computer Science at the University of Manitoba, Winnipeg, Canada. She holds an office in the EITC building (E2-416) and has previously held academic positions at the University of Tehran, University of Toronto as a research associate, and visiting roles at McMaster University, Telecom Paristech, and National University of Singapore. Her research focuses on explainable artificial intelligence, generative models, and information theory with an emphasis on rate-distortion-perception tradeoffs in video and image processing. Her educational background includes teaching courses such as Signals and Systems, Digital Signal Processing, and Network Security at the University of Tehran. She currently teaches COMP4190 (Artificial Intelligence) at the University of Manitoba. Research interests revolve around developing efficient algorithms for AI systems, with key contributions in learned video compression, federated learning, and privacy-preserving techniques. Notable work includes the M22 algorithm for communication-efficient federated learning and the NSERC Discovery Grant-funded project on data-driven learning efficiency. Recent publications highlight advancements in perception loss functions, Gaussian vector source analysis, and secure distributed hypothesis testing. She actively serves on editorial boards (e.g., IEEE Transactions on Communications) and conferences (ISIT, ITW). Awards include the prestigious NSERC Discovery Grant (2025). Supervision highlights 13 MSc students at the University of Tehran, focusing on topics like privacy-preserving systems and distributed learning. Labs/teams: Leads research group at University of Manitoba focusing on AI and information theory applications in multimedia systems.

Matthew HoldenView profile

Associate Professor

• Surgical Data Science
• Machine Learning
• Computer-Assisted Surgery
• +3 more

Matthew Holden is an Associate Professor in the School of Computer Science at Carleton University. He holds a PhD (2018) and MSc (2014) from Queen's University and a BScH (2012) from Western University. His research focuses on Surgical Data Science, applying machine learning to surgical time-series data from operating rooms and simulations to improve patient outcomes and surgical training. Key areas include real-time decision support, performance assessment, and surgical efficiency through domain-knowledge integration. Research interests emphasize machine learning for surgical workflows, skill assessment via sensor data (e.g., motion tracking, EEG), and computer-assisted interventions. Notable work includes automated proficiency evaluation in cataract surgery, ultrasound-guided procedures, and neurosurgical training. His contributions span medical robotics, surgical education, and clinical decision support systems. Publications highlight advancements in surgical workflow anticipation, tool detection, and skill metrics across domains like ophthalmology, emergency medicine, and neurology. Holden advocates for interdisciplinary approaches combining computational methods with clinical expertise to enhance healthcare delivery.

• Machine Learning
• Artificial Intelligence
• Neural Networks
• +2 more

Geoffrey Hinton is a Professor in the Department of Computer Science at the University of Toronto , where he has been a pivotal figure in advancing artificial intelligence research. His work focuses on neural networks, deep learning, and machine learning, revolutionizing how machines process information and learn from data. With collaborations spanning institutions like NYU and IIT Mumbai, Hinton’s influence extends beyond academia into public discourse through lectures like the Romanes Lecture (2024) . His research explores Deep Belief Networks , Gradient Methods , Neural Network Architectures , and Probabilistic Models , with recent publications addressing novel algorithms like the Forward-Forward Algorithm and frameworks for Panoptic Segmentation . Though he no longer accepts students, current advisees include Jimmy Lei Ba and Cem Anil. Hinton’s contributions to AI are complemented by media engagements, including CBS 60 Minutes (2023) and CNN Amanpour (2023) , reflecting his role as a thought leader. His technical outputs, such as Nature Deep Learning Review (2015) with Y. LeCun and Y. Bengio, remain foundational texts in the field.

Marzieh AminiView profile

Associate Professor

• Computer Vision
• Sensor Fusion
• Autonomous Vehicles
• +7 more

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.