جستجوی استاد راهنمای دانشگاهی

• Geophysics
• Inverse Problems
• Computational Methods
• +۵ مورد دیگر

Eldad Haber is a Professor in the Department of Earth, Ocean & Atmospheric Sciences at the University of British Columbia (UBC), affiliated with the Faculty of Science. He holds memberships in the Institute of Applied Mathematics and the CAIDA: UBC ICICS Centre for Artificial Intelligence Decision-making and Action. His research focuses on advancing computational methods at the intersection of geophysics, machine learning, and optimization, with applications in inverse problems, electromagnetic imaging, and reservoir characterization. Key areas of investigation include geophysical inversion techniques, machine learning for data-driven modeling, and the development of efficient algorithms for large-scale problems. Haber’s work integrates disciplines such as geophysics, computer science, and applied mathematics to address challenges in subsurface imaging, environmental monitoring, and resource exploration. Recent research trends in his publications emphasize the application of neural networks to inverse problems, optimization under uncertainty, and multiscale methods for improving computational efficiency. His work on ‘Synthetic Geology’ and ‘Graph Neural Networks’ highlights innovative approaches to geoscientific challenges using deep learning. Collaborations with industry and academic partners have yielded practical solutions for airborne electromagnetic data inversion and mineral prospectivity mapping. His contributions span theoretical advancements in adjoint methods, stochastic optimization, and data assimilation, alongside applied projects in hydrogeology and mineral exploration. Haber’s publications reflect a commitment to bridging computational innovation with real-world geophysical applications.

Renjie Liaoمشاهده پروفایل

استادیار

• Deep Learning
• Machine Learning
• Computer Vision
• +۴ مورد دیگر

Renjie Liao is an Assistant Professor (tenure-track) in the Department of Electrical and Computer Engineering (ECE) at the University of British Columbia (UBC), with an associated appointment in the Department of Computer Science. He is also a Faculty Member at the Vector Institute and a Canada CIFAR AI Chair. Prior to UBC, Dr. Liao was a Visiting Faculty Researcher at Google Brain and held a Senior Research Scientist position at Uber Advanced Technologies Group during his PhD. He earned his B.Eng. (Automation) from Beihang University, M.Phil. (CS) from the Chinese University of Hong Kong, and PhD (CS) from the University of Toronto. His research focuses on probabilistic and geometric deep learning , with key contributions in deep generative models, geometric deep learning, neural algorithmic reasoning, and generalization bounds. Notable areas include 3D point cloud analysis, self-driving systems, and healthcare applications using graph neural networks. His work bridges theoretical foundations (e.g., PAC-Bayes bounds) with practical applications like motion forecasting and medical imaging. Education: B.Eng. in Automation, Beihang University (2011) M.Phil. in Computer Science, CUHK (2015) PhD in Computer Science, UofT (2021) Dr. Liao has received awards such as the RBC Graduate Fellowship and Connaught International Scholarship. His lab (Deep Structured Learning Lab) emphasizes principled mathematical approaches to solving complex problems. He advises students in machine learning, computer vision, and robotics, encouraging applications from those with strong coding/mathematical backgrounds. Labs/Teams: Deep Structured Learning Lab (UBC) Vector Institute Collaboration

Yves Lucetمشاهده پروفایل

استاد

• Computational Mathematics
• Optimization
• Convex Analysis
• +۶ مورد دیگر

Yves Lucet is a Professor in the Department of Computer Science, Mathematics, Physics and Statistics at the University of British Columbia Okanagan. He holds a PhD from the University of Toulouse. His research spans computational mathematics, optimization, and convex analysis, with a focus on algorithm development for computer-aided convex analysis and road design optimization. He leads the development of the CCA (Computational Convex Analysis) toolbox under Scilab, emphasizing hybrid symbolic-numerical algorithms. His work includes visualizing set-valued operators using Virtual Reality (VR) and collaborating on large-scale road design projects funded by NSERC, aiming to minimize construction costs while meeting safety and environmental constraints. Lucet advises graduate students and contributes to interdisciplinary teams involving mathematics, statistics, and engineering. His research also explores applications in computational geometry, image processing, and network communication. Education: PhD in Mathematics from the University of Toulouse. Research Interests: Computational Convex Analysis Algorithm Design for Optimization VR Visualization of Mathematical Operators Multi-objective Road Design Optimization Data-Driven Process Monitoring Key Projects: Development of the CCA toolbox, road design optimization funded by NSERC, and reinforcement learning approaches for prescriptive process monitoring. His work bridges mathematics and computer science, with applications in engineering and transportation.

Greg Moriمشاهده پروفایل

استاد

• Computer Vision
• Machine Learning
• Video Analysis
• +۴ مورد دیگر

Greg Mori is a Professor at the School of Computing Science, Simon Fraser University. He holds a Ph.D. from UC Berkeley and an Hon. B.Sc. from the University of Toronto. His research focuses on computer vision and machine learning, particularly human pose estimation and activity recognition. He has served as Director of the School of Computing Science (2015–2018) and is now Senior Research Director at Borealis AI. Mori has contributed to methodologies like hidden Conditional Random Fields (hCRF), LDA variants, and deep networks. His work emphasizes features capturing visual intuition operationalized via machine learning. Education: Ph.D. in Computer Science, University of California, Berkeley (2004) Hon. B.Sc. in Computer Science and Mathematics, University of Toronto (1999) Research Interests: Computer vision, machine learning, video analysis, human activity recognition, human pose estimation, pedestrian detection and tracking, object recognition. Awards: CIPPRS Research Excellence Award (2008) NSERC Discovery Accelerator Supplement (2008, 2015) ICCV Helmholtz Prize (2017) Outstanding Reviewer Awards (ICCV 2009, CVPR 2010) Grants & Service: Program Chair for CVPR 2020 and General Chair for CVPR 2023 Editorial roles for IJCV and T-PAMI Organizing committee member for CVPR, ICCV, ECCV, NeurIPS, and ICLR Labs/Teams: Vision and Media Lab at Simon Fraser University, leading research in computer vision and machine learning applications.

Kelsey Allenمشاهده پروفایل

استادیار

• Cognitive Science
• Machine Learning
• Robotics
• +۴ مورد دیگر

Kelsey Allen is an Assistant Professor in the Department of Computer Science at the University of British Columbia (UBC) and a Senior Research Scientist at DeepMind. Her research bridges cognitive science, machine learning, and robotics, focusing on understanding and replicating human-like problem-solving, tool use, and physical reasoning. She holds a PhD from MIT (2016) under Josh Tenenbaum and a B.Sc. in Physics from UBC (2010). Research Interests : Allen investigates computational mechanisms underlying human complex behaviors, particularly tool use and design. Her work emphasizes endowing machines with flexible problem-solving abilities. Key themes include lifelong learning, embodied cognition, and integrating symbolic and neural approaches. Awards & Recognition Best Paper Award at Robotics: Science and Systems (RSS) 2018 Oral Presentation at Cognitive Science Society 2019 Spotlight at NeurIPS 2018 and ICLR 2019 Key Projects : Includes developing graph network simulators for rigid body dynamics, tools for physical design optimization, and studies on human tool-use learning. Her work often combines empirical experiments with machine learning models to bridge human and artificial intelligence.

Jiarui Dingمشاهده پروفایل

استادیار

• Bioinformatics
• Computational Biology
• Machine Learning
• +۵ مورد دیگر

Jiarui Ding is an Assistant Professor in the Department of Computer Science at the University of British Columbia , within the Faculty of Science. His research focuses on the intersection of bioinformatics, computational biology, and machine learning, with an emphasis on single-cell genomics and probabilistic deep learning. Key interests include computational immunology, cancer biology, and the application of AI to biomedical problems like food allergy neuroscience. He is affiliated with the CAIDA: UBC ICICS Centre for Artificial Intelligence Decision-making and Action and the Data Science Institute , indicating strong interdisciplinary engagement. He actively recruits doctoral students for research projects in these areas, with desired start dates year-round. His work bridges computational methods with biological systems, exemplified by publications on single-cell data integration ( e.g. , CellUntangler), generative models for T-cell receptors, and mechanistic studies of immune responses in diseases like eosinophilic esophagitis. His research also addresses challenges in multiomics data analysis and the development of novel algorithms for genomic data interpretation. No awards or grants are explicitly listed in the provided materials, though his involvement in high-impact projects suggests potential external funding. He emphasizes collaboration, stating availability for interdisciplinary projects and undergraduate research mentorship.

Uri M. Ascherمشاهده پروفایل

استاد

• Numerical Analysis
• Scientific Computing
• Differential Equations
• +۴ مورد دیگر

Uri M. Ascher is a Professor in the Department of Computer Science at the University of British Columbia's Faculty of Science. He has established himself as a leading researcher in numerical analysis, scientific computing, and computational methods for differential equations. His work bridges theoretical mathematics with practical applications in computer animation, computational finance, and inverse problems. Ascher's research interests focus on numerical methods for evolutionary differential equations, boundary value problems, differential-algebraic equations, and their applications. His work spans both theoretical developments in numerical analysis and practical implementations in scientific computing. He has made significant contributions to the fields of computer animation through numerical methods for simulating deformable objects and physics-based animation. His publication record shows a consistent trajectory of high-impact research spanning several decades, with recent work focusing on the intersection of numerical methods with machine learning, particularly neural differential equations and data-driven approaches to inverse problems. His research demonstrates a unique ability to connect classical numerical analysis with emerging computational challenges in visual computing and scientific simulation. Fellow of the Royal Society of Canada (FRSC), 2018 SIAM Fellow, 2010 CAIMS Research Prize, 2010 Ascher has supervised numerous graduate students and collaborated extensively with researchers across disciplines. His work on numerical methods for computer animation has led to practical implementations used in graphics applications. He maintains active collaborations with researchers in scientific computing, applied mathematics, and computer graphics. Ascher is also affiliated with the Scientific Computing Laboratory and the Institute for Applied Mathematics at UBC, reflecting the interdisciplinary nature of his work.

Peter D. Lawrenceمشاهده پروفایل

استاد

• Human-Machine Interfaces
• Robotics and Control Systems
• Computer Vision
• +۴ مورد دیگر

Professor Peter D. Lawrence holds a faculty position at the University of British Columbia (UBC) within the Department of Electrical & Computer Engineering, part of the Faculty of Applied Science. He has been a Professor since 1974 and has held visiting research roles at Chalmers University of Technology (1970-1972) and MIT (1972-1974). His educational background includes a B.A.Sc. from the University of Toronto (1965), M.Sc. from the University of Saskatchewan (1967), and Ph.D. from Case Western Reserve University (1970). He is a Professional Engineer (P.Eng.) and Fellow of the Canadian Academy of Engineering (FCAE). Research interests focus on improving human-machine interfaces, sensor technologies for control systems, and medical robotics. Key areas include teleoperation of heavy machinery, vision-based control, EEG-based brain interfaces, and functional approximation methods for complex systems. Collaborations span multiple disciplines at UBC, including Mechanical Engineering, Computer Science, Mining Engineering, and Forestry, with funding from NSERC and PRECARN/IRIS. Medical Robotics: Brain-computer interfaces and ultrasound-guided surgery. Autonomous Systems: Path planning and vision-based tracking for excavators and haul trucks. Sensing Technologies: Eye-tracking, joint-angle sensors, and slip detection for mobile robots. His teaching contributions include coordinating the Project Integrated Program (PIP) for ECE students and co-developing the interdisciplinary New Venture Design course with the Sauder School of Business. He leads the RCL Lab and has authored books on real-time microcomputer systems and contributed to IEEE publications. Awards include recognition as a Fellow of the Canadian Academy of Engineering.

Carlos Venturaمشاهده پروفایل

استاد

• Earthquake Engineering
• Structural Dynamics
• Structural Health Monitoring
• +۵ مورد دیگر

Dr. Carlos Ventura is a Professor of Structural & Earthquake Engineering at the University of British Columbia (UBC), serving as Director of the Earthquake Engineering Research Facility (EERF). With over 30 years of experience, his research focuses on structural dynamics, seismic retrofit of buildings, and infrastructure resilience. He has published over 400 papers and led projects for seismic safety guidelines in schools and bridges. Research Interests: Earthquake engineering Structural vibration testing Performance-based design Regional seismic risk assessment Structural health monitoring Awards: Lieutenant Governor’s Award of Excellence (2013) Fellow, Canadian Academy of Engineering (2010) Champions of Earthquake Resilience Award (2015) Multiple best paper awards in international journals Labs & Projects: Director of UBC Earthquake Engineering Research Facility (EERF) Lead researcher in BC School Seismic Retrofit Program Principal investigator in structural health monitoring networks Contributions to bridge and dam safety evaluations

Ipek Orucمشاهده پروفایل

دانشیار

• Artificial Intelligence in Healthcare
• Medical Imaging
• Neuroscience
• +۶ مورد دیگر

Dr. Ipek Oruc is an Associate Professor in the Department of Ophthalmology & Visual Sciences at the University of British Columbia, leading the NOVA Lab. Her interdisciplinary research focuses on applying computational techniques, AI, and data science to advance visual health, particularly in developing low-cost diagnostic tools for retinal imaging and rehabilitation strategies for eye/brain disorders. Her work emphasizes equity in healthcare access for underserved communities. Research interests include AI-driven medical diagnostics, neuroimaging analysis, visual perception mechanisms, and understanding face processing deficits in autism spectrum disorder (ASD). The lab’s recent studies highlight atypical facial exposure patterns in ASD and novel retinal biomarker discovery using deep learning. Current projects aim to democratize AI-based healthcare through virtual health integration. Key achievements include a HIFI Award (2024) for Alzheimer’s detection research and a Killam Teaching Award (2024) for Todd Kamensek. The lab collaborates widely, with postdoctoral fellows (e.g., Gulce Ozturan) and doctoral candidates (e.g., Todd Kamensek, Parsa Delavari) advancing projects in AI ethics, small-data classification, and neuroimaging. NOVA Lab locations include the Blusson Spinal Cord Centre (Vancouver General Hospital), with ongoing clinical partnerships. Research outputs span 80+ peer-reviewed articles, emphasizing translational applications of AI in vision science and neurology.

Christian Gagnéمشاهده پروفایل

استاد

• Deep Learning
• Computer Vision
• Machine Learning
• +۷ مورد دیگر

Christian Gagné is a Full Professor in the Department of Electrical and Computer Engineering at Laval University and Director of the Institute for Intelligence and Data (IID). He holds a prestigious Canada CIFAR AI Chair and is an Associate Academic Member at Mila – Quebec Artificial Intelligence Institute. His research spans multiple interdisciplinary domains with significant contributions to machine learning and its applications. Dr. Gagné completed his doctorate in electrical engineering at Université Laval in 2005, followed by a postdoctoral fellowship at INRIA Saclay (France) and the University of Lausanne (Switzerland) from 2005-2006. Prior to his academic appointment, he worked as a research associate in an industrial environment between 2006 and 2008. His research interests focus on deep learning and stochastic optimization, with particular emphasis on the robustness and generalization of deep neural networks, neurosymbolic approaches to enhance interpretability, and the development of multimodal foundational models. His work has practical applications across computer vision, super-resolution microscopy, healthcare, transportation, and energy sectors. Dr. Gagné has developed important methodologies in representation learning, meta-learning, transfer learning, and optimization approaches based on probabilistic models and evolutionary algorithms. Recent publications demonstrate a strong focus on improving the robustness of machine learning models, with significant contributions to data augmentation techniques for medical imaging, adversarial attacks on visual object trackers, and test time adaptation methods. His research group has produced influential work at the intersection of theoretical machine learning and practical applications, particularly in medical imaging and computer vision domains. Canada CIFAR AI Chair As an academic advisor, Dr. Gagné has supervised numerous doctoral and master's students including Frédéric Beaupré, Catherine Bouchard, Fatemeh Nourilenjan Nokabadi, Sabyasachi Sahoo, and Adam Tupper. His research is supported through various grants, including his Canada CIFAR AI Chair position and collaborations with multiple research centers. Dr. Gagné is actively involved with several research entities including the Vision and Digital Systems Laboratory (LVSN), the Center for Research in Robotics, Vision and Machine Intelligence (CeRVIM), the Center for Research in Massive Data (CRDM), as well as strategic groups REPARTI and UNIQUE of the FRQNT, the VITAM center of the FRQS, and the International Observatory on the Societal Impacts of AI and Digital Technology (OBVIA).

Daipayan Guhaمشاهده پروفایل

استادیار

• Neurosurgery
• Spine surgery
• Medical robotics
• +۵ مورد دیگر

Daipayan Guha is an Assistant Professor in the Department of Surgery at McMaster University, with expertise in neurosurgery and spine surgery. His research integrates medical robotics , virtual reality , and intraoperative imaging for spinal procedures, alongside health economics and clinical decision-making in spinal disorders. Academic ranks: Assistant Professor, Surgery Key affiliations: McMaster University, Canadian Spine Outcomes and Research Network (CSORN) Research interests span robotic-assisted spinal navigation, augmented reality surgical systems, degenerative cervical myelopathy treatment strategies, and socioeconomic factors in spine care. His work emphasizes medical imaging , error propagation analysis , and machine vision for craniospinal procedures. Recent publications focus on robotic spine surgery dose comparisons, telemedicine in spinal care, cervical myelopathy progression models, and augmented reality training systems. These studies often employ systematic reviews , meta-analyses , and prospective cohort studies . Clinical collaborations include AO Spine Community initiatives, WFNS symposia, and multicenter studies on spinal fusion outcomes and trauma management.

Doug Welchمشاهده پروفایل

استاد

• Light Echoes
• Supernovae
• Variable Stars
• +۴ مورد دیگر

Doug Welch is a Professor in the Department of Physics and Astronomy at McMaster University and serves as Vice-Provost and Dean of Graduate Studies. He earned his PhD in Astronomy & Astrophysics from the University of Toronto in 1986, followed by postdoctoral work at the Dominion Astrophysical Observatory. His research focuses on light echoes from supernovae/transient events and pulsating variable stars (Cepheids, RR Lyrae, R CrB stars), with collaborations including Armin Rest (STScI), Geoff Clayton, and Patrick Tisserand. Welch's work leverages light echoes to study asymmetry in historical supernovae and stellar eruptions, advancing constraints for computational models. His articles emphasize supernovae, Cepheid variables, and novel light echo methodologies, reflecting trends in time-domain astronomy and precision distance measurements. Awards: McNeil Medal of the Royal Society of Canada (2010) for science communication. He contributes to major observatories as a Board Member of the Canada-France-Hawaii Telescope Corporation and previously served on the Thirty Meter Telescope Board of Governors. Welch co-leads teams in transient surveys and mentors through institutional roles, though specific grants/students are not detailed.

Osmar Zaianeمشاهده پروفایل

استاد

• Data Mining
• Machine Learning
• Social Network Analysis
• +۱۴ مورد دیگر

Osmar Zaiane is a Professor in the Department of Computing Science at the University of Alberta's Faculty of Science. With over 20 years of service at the university, he has established himself as a leading researcher in data mining and machine learning with applications across multiple domains. His educational background includes a Ph.D. in Computer Science from Simon Fraser University (1999), a Master's in Computer Science from Laval University (1992), a Master's in Electronics from Institut National des Sciences et Techniques Nucléaires and Paris XI University (1989), and a Bachelor's in Computing Science from Institut Supérieur de Gestion, Université de Tunis (1988). Zaiane's research focuses on discovering patterns in large complex datasets with practical applications. His primary research interests include data mining, machine learning, social network analysis (particularly community mining and link prediction), and precision health applications. He has made significant contributions to associative classifiers, class imbalance learning, explainable AI, and educational data mining. His work spans multiple application domains including healthcare (particularly for Alzheimer's disease prediction, diabetic retinopathy grading, and lung cancer detection), social media analysis, and natural language processing. His publication record shows a strong focus on medical AI applications, with numerous recent papers on medical image segmentation, brain network analysis, and diagnostic systems. His work increasingly integrates large language models and transformer architectures with traditional machine learning approaches. Best Paper Award at IEEE/ACM Int. Conf. on Advances in Social Network Analysis and Mining (2023) Best Paper Award at International Symposium on Foundations and Applications of Big Data Analytics (2022) Best Paper Award at International AAAI Conference on Web and Social Media (2019) Best Paper Award at 29th International Conference on Database and Expert Systems Applications (DEXA) (2018) Zaiane has supervised over 80 graduate students during his career at the University of Alberta. His research has been supported by numerous grants, particularly in the areas of precision health and educational data mining. He maintains an active research lab focusing on applied machine learning with strong industry and healthcare partnerships. His current work explores the intersection of traditional machine learning techniques with emerging large language models and vision-language models for healthcare applications.

• Human-Robot Interaction
• Haptics
• Virtual Reality
• +۶ مورد دیگر

Matthew Pan is an Assistant Professor in the Department of Electrical and Computer Engineering at Queen's University and a core member of the Ingenuity Labs Research Institute. He leads the Machine Intelligence and Human-Robot Interaction Laboratory (MItHRIL), focusing on human-robot interaction, haptics, and virtual/augmented reality technologies for practical applications in assistive robotics and entertainment. His educational background includes: PhD and MASc in Mechanical Engineering, University of British Columbia BASc in Mechatronics Engineering, University of Waterloo Dr. Pan's research centers on human-robot interaction , haptics , and virtual/augmented reality , with applications in assistive technologies and human augmentation. His work explores affective computing , psychophysiological responses , and haptic feedback systems to enhance natural human-robot collaboration. Key projects include robotic handover systems, mixed reality interfaces, and social robotics for Disney Parks. Analysis of his 15 most recent publications reveals a strong focus on robotic handovers , human search/approach , and haptic proxies in virtual reality . His work integrates machine learning for adaptive robot behaviors and studies social cues (gaze, touch, gestures) to improve interaction fluency. Trends indicate growing emphasis on real-world deployment of social robotics in assistive and entertainment contexts. Dr. Pan actively advises graduate students in the MItHRIL laboratory and has led industry projects at Disney Research, developing animatronic technologies and interactive guest experiences for Walt Disney Imagineering and Disney Parks. The MItHRIL laboratory serves as an interdisciplinary hub for robotics research, combining expertise in mechanical engineering, computer science, and human factors to advance intelligent systems for human augmentation and collaborative robotics.