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Alla ShefferView profile

Professor

• Geometry Processing
• Computer Graphics
• Discrete Geometry
• +7 more

Alla Sheffer is a Professor and Associate Head of Faculty Affairs in the Department of Computer Science at the University of British Columbia, Faculty of Science. She is affiliated with multiple research centers including CAIDA (Centre for Artificial Intelligence Decision-making and Action), the Institute of Applied Mathematics, and ICICS (Institute for Computing, Information and Cognitive Systems). B.Sc., Hebrew University, Jerusalem (1991) M.Sc., Hebrew University, Jerusalem (1995) Ph.D., Hebrew University, Jerusalem (1999) Postdoctoral Research Associate, University of Illinois, Urbana-Champaign (1999-2001) Assistant Professor, Technion, Israel (2001-2003) Assistant Professor, University of British Columbia (2003-2008) Associate Professor, University of British Columbia (2008-present) Professor Sheffer's research focuses on geometry processing, addressing algorithmic challenges in digital shape modeling and manipulation. Her work primarily deals with discrete geometry representations, specifically meshes (polygonal model representations), with applications in computer graphics and computer-aided engineering. She utilizes tools from computational and differential geometry, discrete mathematics, and graph theory to generate, manipulate, and edit discrete geometric models. Her research spans virtual and augmented reality, visual computing, and 3D modeling, with significant contributions to sketch-based modeling, mesh processing, and cloth simulation. The 15 most recent publications reveal a consistent research trajectory in geometry processing, with recent work focusing on vector sketch processing, VR drawing tools, and advanced mesh manipulation techniques. Her work demonstrates a strong connection between human perception and computational methods, particularly in the interpretation of freehand sketches and the generation of perceptually-accurate geometric representations. The recurring themes across her publications include flowlines, curve networks, mesh parameterization, and the application of perceptual studies to improve algorithmic outputs. Eurographics Fellow ACM Fellow IEEE Fellow Royal Society of Canada Fellow SIGGRAPH Academy Member UBC Killam Research Prize NSERC Discovery Accelerator Supplement IBM Faculty Award Professor Sheffer has supervised numerous doctoral and master's students, with recent theses focusing on geometric mesh processing, vector sketch interpretation, VR drawing tools, and garment modeling. Her research group maintains strong connections with industry through various partnerships and has received substantial grant funding to support their innovative work in geometry processing and computer graphics. She teaches courses in computer graphics, geometric modeling, and video game programming, contributing significantly to both undergraduate and graduate education in computer science.

• Computer Vision
• Robotics
• Medical Imaging
• +4 more

Yuhao Chen is a Research Assistant Professor at the University of Waterloo, specializing in cutting-edge research at the intersection of computer vision, robotics, and healthcare. His work focuses on 3D reconstruction, food tracking, medical imaging, and AI-driven solutions for nutrition analysis and sports analytics. He has contributed to benchmark datasets like NutritionVerse, MetaGraspNet, and FoodVerse, advancing applications in robotic grasping, dietary intake estimation, and human-object interaction analysis. Research interests include egocentric video analysis, real-time 3D reconstruction, zero-shot learning, and multi-task learning. His projects often integrate Gaussian splatting, photometric SLAM, and diffusion models to solve complex problems in food tracking, medical image segmentation, and sports player motion analysis. Recent work highlights include FoodTrack for dietary monitoring and RepViT-MedSAM for medical image segmentation. Yuhao Chen’s innovations span robotics, healthcare, and AI, with a focus on practical applications such as automated nutrition assessment, robotic bin picking, and athlete performance analysis. His research emphasizes scalable frameworks and physically informed 3D reconstruction methods to address real-world challenges in health, sports, and automation.

Jiju PoovvancheriView profile

Associate Professor

• Computer Graphics
• 3D Vision
• Machine Learning
• +8 more

Dr. Jiju Poovvancheri is an Associate Professor in the Department of Math & Computing Science at Saint Mary’s University, Halifax, Canada. He holds affiliations with the Graphics & Spatial Computing Lab and previously held postdoctoral positions at the University of Victoria and University of Calgary. His research focuses on computer graphics, 3D vision, and machine learning, with applications in virtual/augmented reality, autonomous robotics, urban planning, and bio-mechanical studies. Key research areas include point cloud processing, semantic surface reconstruction, spatial data structures, and geometric deep learning. Education: PhD from Indian Institute of Technology Madras (2011–2014), supervised by Prof. Ramanathan Muthuganapathy. Postdoctoral work at University of Calgary (EYES-HIGH Fellowship, 2015–2017) and University of Victoria (MITACS Elevate Fellowship, 2018). Research & Awards: Winner of MITACS Elevate Fellowship (2018), EYES-HIGH Fellowship (2015–2017), and multiple grants including NSERC DG (2019–2026). His work has been supported by NVIDIA GPU hardware, NSERC, CFI, and industry partners like Modest Tree Media and Caterpillar. Professional Activities: Associate Editor for IEEE Access , Guest Editor for special issues in Remote Sensing and Sensors , and reviewer for top conferences like CVPR, ICCV, and ECCV. Member of ACM SIGGRAPH, Solid Modeling Association, and IEEE Geoscience & Remote Sensing Society. Lab & Collaborations: Leads the Graphics & Spatial Computing Lab, collaborating with institutions globally. Current projects include "Interaction and navigation in virtual spaces" and industry partnerships with Modest Tree Media for real-time object recognition. Advising: Supervised over 20 graduate and undergraduate students, with notable alumni advancing to roles at ReelData AI, Royal Canadian Air Force, and academic institutions like Dalhousie University.

Kangkang YinView profile

Associate Professor

• Computer Animation
• Computer Graphics
• Humanoid Robotics
• +3 more

Kangkang Yin is an Associate Professor in the School of Computing Science at Simon Fraser University (SFU). His research focuses on computer animation, computer graphics, humanoid robotics, machine learning, and multimedia analysis. He teaches courses such as Computer Animation and Scientific Computing, and holds a PhD from the University of British Columbia (2007), MSc from Zhejiang University (2000), and BSc from Zhejiang University (1997). His work bridges robotics and animation through projects like physics-based character controllers, motion diffusion models, and robotic manipulation. Key contributions include the SIMBICON biped locomotion framework and research into emotion-driven dance animation. Recent efforts emphasize reinforcement learning applications in motion synthesis and robust visual navigation for unmanned ground vehicles. Yin's publications span over two decades, addressing challenges in motion control, physics-based simulation, and machine learning applications. His lab contributes to both academic advancements and practical robotics solutions. Current research trends show strong emphasis on combining generative AI with traditional animation techniques, as seen in recent work on auto-regressive motion models (AAMDM) and physics-augmented reinforcement learning (PARC).

• Machine Learning
• Representation Learning
• Geometric Deep Learning
• +4 more

Siamak Ravanbakhsh is an Associate Professor at McGill University's School of Computer Science and a Canada CIFAR AI Chair at Mila. His research focuses on machine learning, particularly representation learning with an emphasis on geometry, symmetry, and probabilistic inference. He has held academic positions at the University of British Columbia and was a postdoctoral fellow at Carnegie Mellon University. Education: B.Sc. in Computer Science, Sharif University of Technology M.Sc. and Ph.D. in Computer Science, University of Alberta (supervised by Russ Greiner) Postdoctoral Fellowship at Carnegie Mellon University (with Barnabás Póczos and Jeff Schneider) His research interests span geometric deep learning, equivariant networks, reinforcement learning, and AI for scientific applications. Notable contributions include work on symmetry-aware models, diffusion processes, and equivariant representation learning. Publications highlight advancements in causal abstraction, diffusion-based anomaly detection, and equivariant architectures for crystals and hierarchical structures. His work often bridges theory and application, emphasizing symmetry principles. Advising & Grants: Supervised over 20 graduate students and postdocs, including recent PhD graduates Daniel Levy and Mehran Shakerinava Active in mentoring M.Sc. and internship students He contributes to academic leadership roles at Mila and McGill, fostering interdisciplinary collaborations in AI research.

James StewartView profile

Professor

• Computer-assisted surgery
• Image processing
• Computer graphics
• +3 more

James Stewart is a Professor at Queen's University's School of Computing. His research focuses on biomedical computing and surgical navigation systems. Education: Ph.D. in Computer Science from Cornell University (1992) Location: Office Goodwin 732 Contact: Phone 613 533-3156 Research Interests Professor Stewart's work bridges computer graphics, image processing, and medical applications. Key areas include: Computer-assisted surgical navigation 3D medical visualization Geospatial data representation Robust geometric computation Human-computer interaction in clinical settings Medical imaging uncertainty analysis Scientific Awards Best Poster Award (2013) for 'Image-guided Osteochondral Autologous Autografting of the Ankle' Publications His publications span multiple domains including: Computer graphics algorithms Medical imaging techniques Geospatial visualization Biomedical engineering applications Surgical navigation systems Robust geometric computation

Farida CherietView profile

Professor

• Biomedical technology
• Artificial intelligence
• Medical imaging
• +4 more

Dr. Farida Cheriet is a Full Professor in the Department of Computer Engineering and Software Engineering at Polytechnique Montréal . She serves as Director of the 4D Imaging and Vision Laboratory (LIV4D) and holds membership in the Institute of Biomedical Engineering and the Institute for Data Valorization (IVADO) . Her research spans medical imaging , biomedical technology , artificial vision , and 3D movement estimation . Ph.D. (Montreal), DEA (France), Ing. (Algeria) Her work focuses on interpretable AI models for retinal pathology diagnosis, 3D anatomical reconstruction from medical imaging, and automated segmentation of vascular and spinal structures. She leads innovations in deep learning applications for diabetic retinopathy screening, coronary artery analysis , and spine surgery planning . Recent publications highlight her leadership in computer vision for biomedical applications, with 15 featured articles covering ICU readmission prediction, retinal vessel analysis, dental crown design, and spinal ultrasound processing. She received CFI funding (2021) for cutting-edge facilities and collaborates on pan-Canadian ophthalmic image databases (2018). Dr. Cheriet has supervised 28 Ph.D. students and 46 Master's students in biomedical computing, including projects on scoliosis analysis, retinal imaging, and cardiac MRI. Her teaching includes digital communications and computer graphics courses.

Mario Costa SousaView profile

Professor

• Computer Graphics
• Scientific Visualization
• Sketch-based Modeling
• +5 more

Mario Costa Sousa is a Professor in the Department of Computer Science at the University of Calgary. He holds a BSc from Catholic University of Petrópolis (1989), an MSc from Pontificia Universidade Católica do Rio de Janeiro (1994), and a PhD in Computer Science from the University of Alberta (1999). His primary research focuses on computer graphics, scientific visualization, and sketch-based modeling with applications to geoscience and reservoir engineering. He leads the illustrares research group, which develops novel visual computing paradigms for interdisciplinary challenges in science, engineering, and design. Research interests include non-photorealistic rendering, sketch-based interfaces, visualization/visual analytics, and human-data interaction. His work integrates geoscientific data with interactive modeling tools to address reservoir uncertainty and enhance decision-making in energy and environmental domains. Recent projects involve rapid prototyping of subsurface CO2 sequestration models and machine learning for drill core analysis. He teaches courses such as CPSC 591/691 Rendering (Fall 2024), CPSC 319 Data Structures (Winter 2025), and DATA 501 Data Science Capstone (Winter 2025). His research has contributed to energy innovation initiatives at the University of Calgary, focusing on geothermal energy and unconventional reservoirs. Key collaborations include work on Gaussian process-based uncertainty quantification, functional data analysis for production forecasting, and immersive VR tools for surgical training (e.g., NeuroSimVR and JackVR oil rig simulators). His publications span reservoir modeling, geological visualization, and interactive systems for spatial data exploration.

Pavol FederlView profile

Assistant Professor

• Computational Biology
• Computer Graphics
• Biological Modeling
• +4 more

Pavol Federl is an Assistant Professor (Teaching) in the Department of Computer Science at the University of Calgary, Faculty of Science. His research focuses on computational modeling in biological systems, particularly plant growth, fracture mechanics, and interdisciplinary applications of L-systems. He holds a PhD (2002), MSc (1997), and BSc (1995) in Computer Science from the University of Calgary. Research Interests Biological modeling using L-systems Fracture formation in growing materials Computer graphics and interactive simulation environments Plant growth dynamics and pattern formation His work integrates mathematical modeling with computational techniques to study natural phenomena, such as leaf venation patterns and tree bark fracture. Recent research highlights include: Developmental models of multicellular structures Finite element analysis of fracture dynamics Interactive design tools for bonsai tree modeling Grants and Advising No specific grants or advisees are listed in the provided text. His teaching includes courses such as CPSC 457: Principles of Operating Systems. Professional Contributions He has contributed to software development in virtual laboratories and license plate recognition systems, demonstrating expertise in both theoretical and applied computing.

Sheldon AndrewsView profile

Associate Professor

• Real-time physics simulation
• Articulated mechanism simulation
• 3D character animation
• +5 more

Sheldon Andrews is an Associate Professor of Software Engineering and IT at École de technologie supérieure (ETS) in Montreal, Canada, with an adjunct appointment in Computer Science at McGill University. He is a member of the Multimedia Research Laboratory and has established himself as a leading researcher in physics-based computer animation and simulation. Andrews earned his Ph.D. in Computer Science from McGill University (2015), MASc in Electrical and Computer Engineering from the University of Ottawa (2007), and B.Eng. in Computer Engineering from Memorial University (2004). His academic journey reflects a strong foundation in both theoretical and applied aspects of computer engineering and graphics. His research focuses on real-time physics simulation, articulated mechanism simulation, 3D character animation, motion capture, computational contact mechanics, and virtual environment modeling. Andrews' work bridges the gap between theoretical physics and practical applications in computer graphics, with particular emphasis on creating physically plausible animations that can run in real-time. His research has significant implications for video games, virtual reality, and robotics applications. Analysis of his recent publications (2022-2025) reveals a strong trend toward increasingly sophisticated physics-based character animation techniques, with growing integration of machine learning approaches. His work spans multiple subfields including collision detection, deformable object simulation, vehicle physics, and reinforcement learning for character control, demonstrating both breadth and depth in his research program. VRIPHYS 2012 best paper award for 'Policies for goal directed multi-finger manipulation' Andrews has advised numerous graduate students through their PhD and Master's degrees, with many going on to positions at major companies like DNEG, CM Labs Simulations, and AMD. His professional service is extensive, having served as Program Chair for SCA 2025 and MIG 2024, Conference Chair for I3D 2019, and on program committees for major conferences including SIGGRAPH, SCA, and MIG for multiple years. He has also been active in the Montreal SIGGRAPH Chapter as Secretary from 2018-2021. As a core member of the Multimedia Research Laboratory, Andrews collaborates with researchers across multiple disciplines to advance the state of the art in physics-based simulation. His lab maintains strong industry connections, including a visiting researcher position at Roblox Research, ensuring that theoretical advances translate to practical applications in gaming and virtual environments.

Tim WelshView profile

Professor

• Perception
• Cognition
• Cognitive Neuroscience
• +6 more

Dr. Timothy Welsh is a full-time Professor in the Action & Attention Lab at the University of Toronto's Faculty of Kinesiology & Physical Education , where he also serves as Associate Dean, Research since 2023. With cross-appointments from the Faculty of Physical Education and Health, his work bridges cognitive neuroscience and motor behavior research. Academic Background Ph.D. in Human Biodynamics (McMaster University, 2002) M.Sc. in Human Biodynamics (McMaster University, 1999) B.P.H.E. in Physical and Health Education (University of Toronto, 1997) Dr. Welsh's research explores neural mechanisms of goal-directed actions across typical and neurodiverse populations. His lab investigates joint action dynamics , self-objectification effects on motor performance , and cross-reality sensorimotor adaptation , with significant focus on autism and developmental coordination disorder. Recent publications (2024-2025) reveal systematic differences in motor imagery between autistic and non-autistic adults , gender-specific learning patterns , and cross-modal sensory integration challenges in mixed reality environments . These studies demonstrate his lab's expertise in combining behavioral experimentation , EEG analysis , and computational tool development like TAT-HUM. Scientific Recognition Franklin Henry Young Scientist Award Early Distinguished Career Award (NASSP) Outstanding Graduate Supervision Award As principal investigator on 15 active grants (2015-2027), including projects on digital health platforms for concussion rehabilitation and AI-assisted VR learning modules , Welsh's work impacts both basic science and applied interventions. His lab trains students at all levels through KPE 362 (undergraduate) and KIN 5514 (graduate) courses.

Maria LantinView profile

Researcher

• 3D
• computer graphics
• mobility
• +10 more

Maria Lantin serves as Director of Research and Director of Intersections Digital Studio at Emily Carr University of Art + Design, where she bridges computing science expertise with artistic practice. With a PhD in Computing Science from Simon Fraser University, she previously led the Visualization Research Lab at Banff Centre's Banff New Media Institute before joining Emily Carr. Her research focuses on immersive digital environments , particularly exploring the intersection of physical and virtual breath in interactive installations. Lantin's work combines stereoscopic video, biometric sensing, and data visualization to create experiences that examine how humans interact with media environments. Her projects often investigate how information flows through social groups and how virtual spaces affect bodily awareness . Lantin's creative output reveals a consistent pattern of transforming abstract concepts into tangible visual experiences. Her projects demonstrate expertise in creating interactive systems that visualize social dynamics, group behavior, and environmental relationships through stereoscopic and immersive technologies. SSHRC-funded Breath I/O project investigating breath in virtual environments Collaborations with Leila Sujir (Concordia University), Joy James, and Ron Burnett Regular participation in conferences like Interactive Futures Lantin maintains an active research blog where she explores theoretical connections between technology, perception, and human behavior, particularly examining how digital environments affect physiological processes like breathing patterns. Her work positions her at the forefront of art-science collaborations that investigate human interaction with emerging media technologies.

Nancy ReidView profile

Professor

• Statistical Theory
• Mathematical Statistics
• Asymptotic Methods
• +6 more

Nancy Reid is a University Professor in the Department of Statistical Sciences at the University of Toronto, Canada. Her academic leadership includes roles such as OC (Order of Canada), FRS (Fellow of the Royal Society), and FRSC (Fellow of the Royal Society of Canada). She specializes in foundational statistical theory, asymptotic methods, likelihood inference, and Bayesian-frequentist comparisons. Reid's research bridges theoretical advancements and practical applications in biostatistics, machine learning, and interdisciplinary data science. Her work emphasizes robust statistical methods for high-dimensional and complex data, including contributions to partial likelihood (Cox model), saddlepoint approximations, and accuracy in directional inference. Key recognitions include the COPSS Distinguished Achievement Award, Gold Medal from the Statistical Society of Canada, and numerous editorial and advisory roles. Reid collaborates globally, delivering invited lectures at institutions like Harvard, Stanford, and the Royal Society. Reid's academic output spans over 30 years, with influential books like Theory of the Design of Experiments (with D.R. Cox) and Applied Asymptotics . Her recent focus includes replicability in data science, foundations of statistical inference, and bridging gaps between theoretical and applied statistics. She actively promotes methodological rigor and interdisciplinary collaboration through initiatives like the Canadian Statistical Sciences Institute.

Brian RossView profile

Professor

• Artificial Intelligence
• Computational Intelligence
• Genetic Programming
• +10 more

Brian Ross is a Professor at the Department of Computer Science, Brock University. He holds a BSc (Hon) from the University of Manitoba, MSc from the University of British Columbia, and PhD in Artificial Intelligence from the University of Edinburgh. His research focuses on evolutionary computation , particularly genetic programming , with applications in computational biology , evolutionary design , and computational aesthetics . Education: BSc Computer Science (Hon), University of Manitoba MSc Computer Science, University of British Columbia PhD Artificial Intelligence, University of Edinburgh Research Interests: Computational Intelligence: genetic programming, evolutionary algorithms, multi-objective optimization Evolutionary Design: computational aesthetics, 2D/3D modeling, energy-efficient architecture Applications: computational biology, stochastic process algebra, GPU-based image analysis Article Trends: His recent work combines deep learning with evolutionary algorithms to enhance diversity in agent behavior and design optimization. Earlier studies focused on stochastic modeling of biological networks and procedural texture generation . Collaborations span computational aesthetics, passive solar architecture, and non-photorealistic rendering techniques. Students & Collaborations: He has supervised diverse projects including evolved intelligent agents , passive solar building design , and evolutionary art systems . Notable students include Marshall Joseph (2023), Tyler Cowan (2021), and Sheikh Faishal Basher (2021).

• Dependence Modeling
• Copula Models
• Multivariate Analysis
• +7 more

Harry Joe is a Professor in the Department of Statistics at the University of British Columbia (Vancouver Campus). His primary research focuses on dependence modeling, copula theory, multivariate analysis, and applications in biostatistics, finance, and psychometrics. He has advised students including Xiaoting Li, Xinyao Fan, and Pavel Krupskiy. Research Interests: - Advanced copula constructions (e.g., vine copulas) - Extreme value theory and tail dependence - Applications in financial risk, biomedical research, and educational measurement - Multivariate time series analysis and non-Gaussian models Publications highlight contributions to copula-based classification methods (2024), factor copula models (2015), and dynamic dependence modeling (2020). His work bridges theoretical developments with practical applications across disciplines. Teaching and advising emphasize methodological innovation. Current research explores high-dimensional dependence structures and computational methods for complex data. No lab/team affiliations explicitly noted in provided materials.