جست‌وجوی استاد و پژوهشگر

Wolfgang Stuerzlingerمشاهده پروفایل

استاد

• 3D User Interfaces
• Extended Reality
• Augmented Reality
• +۱۰ مورد دیگر

Wolfgang Stuerzlinger is a Professor and Director of the VVISE Lab at Simon Fraser University's School of Interactive Arts & Technology. His research focuses on 3D user interfaces, virtual/augmented reality (VR/AR), and human-computer interaction. He holds a Doctorate from the Vienna University of Technology and has held academic roles at York University and the University of North Carolina. **Education**: PhD (Dr. techn.) in Computer Science, Technical University of Vienna, Austria Dipl.-Ing. (Master's equivalent), Computer Science, Technical University of Vienna **Research Interests**: Specializes in VR/AR interaction techniques, 3D user interfaces, and visual/immersive analytics. Current projects address 3D interaction challenges, occasionally failing systems, and hardware/software innovations for extended reality. Notable contributions include studies on vergence-accommodation conflict, gaze-based text entry, and locomotion techniques in VR. **Awards**: Member of the IEEE VGTC Virtual Reality Academy (2023) and ACM SIGCHI Academy, recognizing leadership in VR and HCI research. **Grants & Labs**: Leads the VVISE Lab, collaborating on projects like RedirectedStepper and Depth3DSketch. Active in grant-funded research on immersive analytics and accessibility in XR navigation. **Teaching**: Teaches courses such as IAT 445 (Immersive Environments) and IAT 848 (Mediated Reality Systems).

David Clausiمشاهده پروفایل

استاد

• Computer Vision
• Image Processing
• Pattern Recognition
• +۲ مورد دیگر

David Clausi is a Professor in the Department of Systems Design Engineering at the University of Waterloo. His research focuses on computer vision, image processing, and pattern recognition, with applications in SAR imagery and biomedical imaging. He leads the Vision and Image Processing (VIP) Lab, which explores AI, transparent AI systems, and scalable solutions. He has supervised numerous graduate and undergraduate students, contributing to advancements in medical imaging, SAR sea ice classification, and sports analytics. Awards include Teaching Excellence recognitions and a Research Excellence Award. His work spans publications in remote sensing, medical imaging, and computer vision, with a focus on real-world applications like environmental monitoring and healthcare. Education: Ph.D., P.Eng. (details not explicitly provided, inferred from titles). Research Interests include automated image interpretation, SAR and biomedical imagery analysis, and AI ethics. His lab emphasizes scalable AI and transparent AI explanations. Recent articles address sea ice mapping, hockey analytics, and wildlife detection using advanced machine learning techniques. Awards highlight contributions to research and teaching excellence. Advising spans over 30 students, with notable theses in SAR segmentation, medical image analysis, and computer vision. The VIP Lab collaborates on projects like the AI4Arctic initiative and hockey analytics tools like PuckNet and GoalieNet.

Michael Nixonمشاهده پروفایل

دانشیار

• Artificial Intelligence
• Human-Computer Interaction
• Game Design
• +۵ مورد دیگر

Michael Nixon is an Associate Professor (Teaching Stream) and Associate Director at the Institute of Communication, Culture, Information & Technology (ICCIT) at the University of Toronto. His work bridges artificial intelligence, human-computer interaction, and game studies, focusing on procedural literacy and immersive technologies. PhD in Interactive Arts & Technology from Simon Fraser University MSc in Interactive Arts & Technology from Simon Fraser University BSc in Computer Science from Vancouver Island University Diploma in Digital Media Technology from Vancouver Island University Research Areas: Believable Characters: Cognitive modeling, procedural animation, and AI-driven social interactions in games and simulations Extended Reality: Designing immersive AR/VR experiences with touch-responsive interfaces Procedural Literacy: Integrating coding skills into humanities and social sciences education Teaching Focus: CCT111 Critical Coding CCT261 Information Architecture and Usability CCT483 Play, Performance, and Community in Digital Games

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

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.

Sid Felsمشاهده پروفایل

استاد

• Human Computer Interaction
• 3D displays
• Biomechanical modeling
• +۷ مورد دیگر

Sid Fels is a Professor in the Department of Electrical and Computer Engineering at the University of British Columbia (UBC), affiliated with the Faculty of Applied Science. He leads the Human Communication Technologies Laboratory and was the director of the Media and Graphics Interdisciplinary Centre (MAGIC) from 2001 to 2012. His academic rank is Professor, and he is actively involved in faculty roles without any indication of retirement or former status. Education: BASc in Electrical Engineering (University of Waterloo, 1988), MSc and PhD in Computer Science (University of Toronto, 1990 and 1994 respectively). He has been at UBC since 1998 and was recognized as a Distinguished University Scholar in 2004. Research Interests: Focuses on human-computer interaction, biomechanical modeling, neural networks, medical applications, speech synthesis, and interactive arts. His lab explores topics like 3D displays, surgical simulation, and vocal tract modeling. Key projects include mandibular reconstruction simulations and biomechanical modeling for speech production. Publications: Over 150 articles, including recent works on mandibular reconstruction efficacy, AI in education, and vocal tract acoustics. Notable articles from 2025 include studies on surgical planning and chatbot-mediated critical thinking. Awards: Distinguished University Scholar (UBC, 2004). His work bridges computer science, engineering, and biomedical applications, with a strong focus on interdisciplinary projects. Students: Supervised over 20 graduate students, including those in biomechanical modeling, medical imaging, and human-computer interaction. Notable advisees include authors of theses on video-based learning tools and surgical simulation systems. Labs/Teams: Leads the Human Communication Technologies Lab and co-founded MAGIC. Collaborates with clinical partners on projects like mandibular reconstruction and sleep apnea modeling.

Robert Andersenمشاهده پروفایل

استاد

• Political Economy
• Social Stratification
• Social Statistics
• +۳ مورد دیگر

Robert Andersen is a Professor in the Department of Sociology at Western University, cross-appointed to the Department of Statistical and Actuarial Sciences and the Department of Political Science. He previously held the Distinguished Professor of Social Science and Chair position at the University of Toronto and has served at McMaster University, the University of Oxford, and Brock University. Ph.D., McMaster University MA, University of Western Ontario BA, University of Western Ontario His research focuses on political economy , social stratification , and social statistics , particularly how economic inequality interacts with national economic conditions to influence democratic support , civic engagement , and redistributive attitudes . He has published extensively on class politics, tolerance, and statistical methods. Recent publications analyze topics such as methodological bias , brain health metrics , cross-national inequality , and political behavior . His work spans disciplines including political science, sociology, and quantitative methodology. Andersen has secured over $1,000,000 in research grants and authored 1 book. His methodological contributions include robust regression techniques and effect displays for statistical models.

• 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.

Dinesh K. Paiمشاهده پروفایل

استاد

• Sensorimotor Computation
• Computer Graphics
• Robotics
• +۵ مورد دیگر

Dinesh K. Pai is a Professor and Tier 1 Canada Research Chair in the Department of Computer Science at the University of British Columbia, where he directs the Sensorimotor Systems Laboratory. He holds cross-appointments at UBC’s Institute for Computing, Information and Cognitive Systems (ICICS), Institute of Applied Mathematics, Graduate Program in Neuroscience, ICORD International Collaboration on Repair Discoveries, and Brain Research Centre. His educational background includes a Ph.D. from Cornell University and a B.Tech. from the Indian Institute of Technology, Madras. His research spans sensorimotor computation with applications in computer graphics, robotics, biomechanics, haptics, and neurobiology. Recent work focuses on human body modeling, garment simulation, soft tissue dynamics, and friction measurement, leveraging machine learning and physics-based simulation techniques. Pai’s publication trends reveal a strong interdisciplinary focus on human-centered computational models, with increasing integration of machine learning for 3D pattern adjustment, body shape generation, and cloth simulation. His work bridges computer graphics with biomechanics and neuroscience through sensorimotor computation. UBC’s 2012 Killam Research Prize CHCCS 2020 Achievement Award Tier 1 Canada Research Chair His research has been supported by significant grants including NSERC Discovery and Idea-to-Innovation grants, a Major Thematic Grant from the Peter Wall Institute for Advanced Studies, and a Human Frontier Science Program grant for studying CNS modularity. As leader of ICICS’ Human Sensorimotor Systems theme, he directs a multidisciplinary team exploring computational models of human sensorimotor function. The Sensorimotor Systems Laboratory operates state-of-the-art facilities featuring a Vicon MX motion capture system, LMI HDI body scanner, eye trackers, skin measurement instruments, EMG systems, haptic interfaces, and robotic platforms for advancing human-computer interaction research.

James Staffordمشاهده پروفایل

استاد

• Data Science
• Asymptotics
• Biostatistics
• +۵ مورد دیگر

James Stafford is a Professor at the University of Toronto , affiliated with the Department of Statistical Sciences . His office is located at Sidney Smith Hall, Room 6019, Toronto, Ontario, Canada (M5S 3G3). Research Interests : Data Science, Asymptotics, Biostatistics, Computer Algebra, Smoothing, Survey Methodology, Symbolic Computation, Longitudinal Data Analysis. His work focuses on computational statistics, symbolic algorithms for likelihood adjustments, and applications in survey data visualization and biostatistical modeling. Recent Publications : His research spans symbolic computation in statistics (2000), local-EM algorithms (2010), and recursive moment calculations in risk theory (2003). Articles address topics like bootstrap methods, interval-censored data analysis, and priority queue theory, reflecting interdisciplinary applications in statistics, computer science, and actuarial science. Awards : Distinguished Service Award, Statistical Society of Canada (SSC) Contact : Email: jamie.stafford@utoronto.ca , stafford@utstat.toronto.edu Phone: +1 416-978-5709 Fax: +1 416-978-5133

• Computer Vision
• Computer Graphics
• Computational Photography
• +۶ مورد دیگر

Alexei Efros is a Professor of Electrical Engineering and Computer Science at UC Berkeley , affiliated with the Berkeley Artificial Intelligence Research Lab (BAIR) . Previously, he was a faculty member at the Robotics Institute, Carnegie Mellon University , and a postdoc at Oxford University with Andrew Zisserman. His work spans data-driven computer vision , self-supervised learning , and applications to computer graphics , computational photography , and human-AI interaction . Research Themes : Self-supervised visual learning 3D scene understanding Vision-language multimodal systems Teaching : CS 180/280A: Intro to Computer Vision CS 280: Graduate Computer Vision CS 294-192: Visual Scene Understanding Recent Publication Trends : Focus on diffusion models and self-guidance 3D perception and rendering Interpretability of vision-language models Temporal and sequential learning Scientific Collaborations : Extensive partnerships with institutions like MIT, CMU, Stanford, and NVIDIA Mentorship of PhD students now at TTIC, OpenAI, Anthropic, and academia Labs & Teams : BAIR Lab (UC Berkeley) Collaborations with Adobe Research, Google, and NVIDIA

Brian Fristenskyمشاهده پروفایل

دانشیار

• Bioinformatics
• Plant Biotechnology
• Plant Genomics
• +۷ مورد دیگر

Brian Fristensky is an Associate Professor in the Department of Plant Science at the University of Manitoba, Faculty of Agricultural and Food Sciences. His research and teaching focus on plant biotechnology, bioinformatics, and plant genomics, particularly in the context of plant-microbe interactions and defense mechanisms. Institution: University of Manitoba School: Faculty of Agricultural and Food Sciences Department: Department of Plant Science Position: Associate Professor Office: 330 Agriculture Building, Fort Garry Campus Contact: 204-474-6852 / 204-474-6085; brian.fristensky@umanitoba.ca; frist@cc.umanitoba.ca Education A.B., Cornell University, 1980 Ph.D., Washington State University, 1987 Research Interests Dr. Fristensky's research centers on bioinformatics and plant biotechnology, with a strong emphasis on molecular biology of plant defense mechanisms. His work spans computational biology, genomics, and genetic engineering, particularly in developing tools for bioinformatics analysis and understanding plant responses to pathogens. He leads the Fristensky Lab, which develops the BIRCH bioinformatics system and BioLegato interface. Recent Research Trends His recent publications (2021–2023) show continued innovation in bioinformatics software development, including the BioLegato interface and educational tools for data science. Earlier work (1999–2006) focused on transgenic approaches to enhance disease resistance in canola using pea defense genes, and the functional analysis of PR10 proteins in stress tolerance. His research integrates computational and experimental biology, with applications in crop improvement and sustainable agriculture. Scientific Awards No awards explicitly mentioned in the provided texts. Advising and Grants Dr. Fristensky advises undergraduate and graduate students, offering opportunities for thesis work and summer research in his lab. He supports students in the Plant Biotechnology Program and teaches core courses in biotechnology, cytogenetics, and bioinformatics. He assists researchers in writing the bioinformatics components of grant proposals, indicating active grant-related mentoring and collaboration. Labs and Teams He leads the Fristensky Lab (Bio Information Technologies Laboratory), which develops the BIRCH (Biological Research Computer Hierarchy) system—a comprehensive desktop bioinformatics platform. The lab focuses on software tools for genome assembly, transcriptomics, systems biology, database management, and data pipelines. The BioLegato interface serves as a programmable GUI for launching and chaining bioinformatics programs. The lab also engages in training and collaborative research, particularly in biofuels and data science education.

• Computational Arts
• Generative Systems
• Immersive Environments
• +۵ مورد دیگر

Graham Wakefield is an Associate Professor in the Department of Computational Arts at York University's School of the Arts, Media, Performance and Design (AMPD). He is also a Canada Research Chair in Computational Worldmaking and leads the Alice Lab, focusing on biomorphic intelligence and immersive mixed realities. His research spans generative systems, artificial life, and creative software development, with notable collaborations on the 'Artificial Nature' series and contributions to the AlloSphere project. Wakefield holds a Ph.D. in Media Arts and Technology from UC Santa Barbara and has taught and researched globally, including at institutions like KAIST and Sogang University in South Korea. Education: Ph.D., Media Arts & Technology, University of California, Santa Barbara (2007–2012) M.Sc., Media Arts & Technology, UC Santa Barbara (2004–2007) M.Mus., Composition, Goldsmiths College, University of London (2002–2004) B.A. (Hons), Philosophy, University of Warwick (1994–1997) Research Interests: Wakefield’s work emphasizes open-ended computational systems applied to immersive environments and generative art. His projects explore bio-inspired systems, generative audiovisual composition, and interactive installations. Key themes include real-time systems, modular synthesis, and the intersection of art and AI. The Artificial Nature series exemplifies his focus on evolving ecosystems and user interaction. Publications & Awards: His work has been exhibited at SIGGRAPH, ISEA, and NIME. Notable awards include the VIDA 16.0 competition and a Best Paper at NIME 2013. Recent publications address modular synthesis, live coding, and immersive environments. Grants & Service: He has secured grants from the Canada Foundation for Innovation, SSHRC (Canada Research Chair), and others. Wakefield actively chairs committees for AI initiatives, faculty searches, and interdisciplinary research units like the Centre for Artificial Intelligence & Society. Labs & Collaborations: The Alice Lab at York University develops tools like COSM and gibber , and collaborates on projects such as the Synaptic Time Tunnel for SIGGRAPH 2023. Wakefield co-authored Generating Sound & Organizing Time and contributes to Max/MSP/Jitter through Cycling '74.

Julien Arinoمشاهده پروفایل

استاد

• Mathematical Epidemiology
• Mathematical Population Dynamics
• Data Science
• +۳ مورد دیگر

Julien Arino is a Professor in the Department of Mathematics at the University of Manitoba , specializing in Mathematical Epidemiology and Mathematical Population Dynamics . He is an active member of the Mathematical Biology group and focuses on the role of movement in disease transmission, with applications to both human and animal health. Department of Mathematics Faculty of Science University of Manitoba His research spans: Metapopulation dynamics with explicit movement Multi-pathogen and multi-species transmission modeling Computational epidemiology with data science integration Evaluation of travel control measures for disease variants Recent work includes publications on: Cholera transmission in Chad Multi-species bovine tuberculosis dynamics Measles resurgence patterns Software tools for R compilation and data visualization He maintains a public YouTube channel with lecture videos on Mathematical Epidemiology and contributes open-source code for scientific computing in R and Python. His methodological contributions include novel approaches to plot formatting and computational efficiency in epidemic modeling.

Robert Gensمشاهده پروفایل

پژوهشگر

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

Robert Gens is a researcher at the University of Washington , affiliated with the College of Engineering and the Department of Computer Science and Engineering . His work focuses on advancing machine learning architectures, particularly Sum-Product Networks (SPNs) , with applications in computer vision and deep learning. Education: S.B. in Electrical Engineering and Computer Science from MIT (2009) , Ph.D. in Computer Science and Engineering from the University of Washington ( 2016 ). His research integrates insights from neuroscience, graphics, and mathematics to develop algorithms capable of modeling infinite visual data as stable concepts. Key contributions include structural learning, discriminative training, and computational efficiency in SPNs. Notable publications span NIPS , ICML , and ICLR venues, with a focus on SPN optimization and compositional modeling. Trends in his work emphasize tractable probabilistic models , neural network efficiency , and cross-disciplinary algorithm design . Awards include the Google PhD Fellowship in Deep Learning and an NIPS 2012 Outstanding Student Paper Award . Current research involves Deep Symmetry Networks at the Seattle Laboratory of Robotics.