جست‌وجوی استاد و پژوهشگر

Xiaoxiao Liمشاهده پروفایل

استادیار

• Federated Learning
• Medical Imaging Analysis
• AI Fairness and Trustworthiness
• +۷ مورد دیگر

Dr. Xiaoxiao Li is an Assistant Professor in the Electrical and Computer Engineering Department at the University of British Columbia (UBC), with joint appointments in Computer Science (Associate Member) and the School of Medicine at Yale University (Adjunct Assistant Professor). She is also a Canada CIFAR AI Chair and Canada Research Chair (Tier II) in Responsible AI. Her research focuses on enhancing trustworthiness, fairness, and efficiency in AI algorithms and foundation models, particularly in healthcare applications. Education: B.S. (Honors) in Zhejiang University (2015), Ph.D. in Biomedical Engineering from Yale University (2020), Postdoc at Princeton University (2020-2021). She leads the Trusted and Efficient AI (TEA) Lab at UBC, which develops algorithms for federated learning, medical imaging analysis, and interpretable AI systems. Research interests include federated learning, generative models, medical image analysis, AI fairness, and graph-based methods for neuroimaging. Recent projects include GMValuator (data valuation for generative models), FairMedFM (fairness benchmarking in medical AI), and FedTextGrad (textual gradient-based FL optimization). Grants: Canada Foundation for Innovation Grant (2023), UBC Green Lab Fund (2023), Vector Institute funding Teaching: Courses on machine learning, federated learning, and AI ethics at UBC Awards & Recognition: Best Paper Award at FL@FM WWW 2024, Editorial Board Member of Medical Image Analysis , multiple top-tier conference acceptances (NeurIPS, ICLR, CVPR, MICCAI). Lab & Teams: TEA Lab collaborates with industry and hospitals to translate AI research into clinical tools. Current projects address AI fairness in healthcare, federated learning for medical data, and multimodal medical analytics.

Laurent Mydlarskiمشاهده پروفایل

استاد

• Fluid Mechanics
• Turbulence
• Heat Transfer
• +۴ مورد دیگر

Laurent Mydlarski is a Professor in the Department of Mechanical Engineering at McGill University, affiliated with the Faculty of Engineering. His research focuses on experimental fluid mechanics, particularly turbulent flows and scalar mixing. He holds a Ph.D. from Cornell University and B.A.Sc. from the University of Waterloo. Research interests include turbulence statistics, scalar dispersion, differential diffusion, and industrial cooling applications such as hydroelectric generators and microelectronics. His work combines experimental methods like hot-wire anemometry, laser-induced fluorescence, and particle-tracking velocimetry. Key contributions include studies on multi-scalar mixing in jets, wall shear stress in turbulent flows, and thermal anemometry probe design. His Mydlarski Lab at McGill explores both fundamental fluid dynamics and practical engineering solutions. Recent publications (2023-2025) address multi-scalar mixing metrics, electronic cooling innovations, and drag reduction on porous cylinders. Collaborations with industry focus on applying fluid mechanics principles to real-world thermal management challenges.

Sharan Vaswaniمشاهده پروفایل

استادیار

• Reinforcement Learning
• Optimization
• Machine Learning
• +۳ مورد دیگر

Sharan Vaswani is an Assistant Professor in the School of Computing Science at Simon Fraser University (SFU). His research focuses on designing algorithms for sequential decision-making under uncertainty, stochastic optimization, and their interplay with machine learning generalization. He holds a PhD from the University of British Columbia (2019) and postdoctoral experiences at the University of Alberta and Mila. His academic journey includes MSc (UBC, 2015) and BTech (BITS Pilani, 2012) degrees. Education: PhD (UBC, 2019), MSc (UBC, 2015), BTech (BITS Pilani, 2012) Postdoctoral Work: University of Alberta (2020-2021), Mila (2019-2020) Teaching includes courses on Probability and Computing (CMPT 210), Optimization for Machine Learning (CMPT 409/981), and Theoretical Foundations of Reinforcement Learning (CMPT 419/983). His research group focuses on developing scalable optimization algorithms with theoretical guarantees. He advises multiple PhD and MSc students, contributing to areas like constrained MDPs, adaptive learning rates, and reinforcement learning theory. Research Highlights: Contributions to bandit algorithms, stochastic gradient methods, and reinforcement learning theory. Notable work includes global convergence analysis of policy gradients and variance-reduced optimization frameworks.

• Machine Learning
• Artificial Intelligence
• Data Science
• +۶ مورد دیگر

Chris J. Maddison is an Assistant Professor at the University of Toronto, holding joint appointments in the Department of Computer Science and the Department of Statistical Sciences. He is also a CIFAR AI Chair at the Vector Institute and a member of the ELLIS Society. Maddison earned his DPhil from the University of Oxford and previously worked as a Senior Research Scientist at Google DeepMind and a member at the Institute for Advanced Study. His research focuses on advancing machine learning methodologies, particularly in leveraging data’s natural structure for efficient learning, with applications in drug discovery, causal inference, and AI safety. Education: DPhil in Computer Science, University of Oxford His research interests span machine learning, AI safety, reinforcement learning, and the integration of logical reasoning into large language models. Maddison has contributed to foundational work on gradient estimation techniques and was a key member of the AlphaGo project. He actively explores how statistical structures in real-world data influence AI capabilities. Recent publications emphasize evaluating conversational agents, mitigating AI safety risks, and enhancing logical reasoning in LLMs. His work bridges theoretical advancements with practical applications, such as code generation and multi-agent systems. Awards: NeurIPS Best Paper Award (2014), Open Philanthropy AI Fellowship Maddison advises multiple PhD students and postdoctoral researchers, fostering collaborations across academia and industry. His former advisees now hold roles at institutions like OpenAI, Stanford, and Magic AI. He teaches advanced courses in machine learning and statistical methods, including CSC 2541 (Large Models) and STA 314 (Machine Learning). Maddison is affiliated with the Schwartz Reisman Institute for Technology and Society, extending his impact to societal implications of AI. His lab’s interdisciplinary approach combines algorithmic innovation with real-world problem-solving.

Margaret P. Chapmanمشاهده پروفایل

استادیار

• Risk-Averse Control Theory
• Stochastic Control Theory
• Safety Analysis for Dynamical Systems
• +۴ مورد دیگر

Margaret P. Chapman is an Assistant Professor in the Department of Electrical and Computer Engineering at the University of Toronto's Faculty of Applied Science and Engineering. She leads the DATA Lab (Decision Analysis for Trustworthy Autonomy), focusing on risk-averse and stochastic control theory with applications to environmental and human health. Education: B.S. and M.S. in Mechanical Engineering from Stanford University (2012, 2014), Ph.D. in Electrical Engineering and Computer Sciences from UC Berkeley (2020, advised by Claire Tomlin) Her research bridges robust and stochastic optimal control via risk measure theory, emphasizing safety-critical applications in healthcare and sustainable cities. Key challenges include scalable risk-sensitive control methods, integrating physics-based and data-driven models for safety analysis, and promoting technologies that enhance planetary and human well-being. Recent publications focus on risk-averse autonomous systems, CVaR-based safety analysis, and multi-time-scale modeling for cancer treatment. She has advised students in both graduate and undergraduate research roles, including NSERC awardees and thesis participants. Awards: US National Science Foundation Graduate Research Fellowship, Berkeley Fellowship, Terman Engineering Scholastic Award, Leon O. Chua Award She teaches courses like ECE 557 (Linear Control Theory) and ECE 1643 (Risk-Averse Control with Learning). Her invited talks span institutions such as MIT, Princeton, and Georgia Tech, highlighting risk-sensitive analysis and control for trustworthy autonomy.

• Machine Learning
• Artificial Intelligence
• Computer Science
• +۵ مورد دیگر

Gordon Geoffrey J. is a Professor at Carnegie Mellon University, specializing in Machine Learning , Artificial Intelligence , and Computer Science . His research spans Reinforcement Learning , Domain Adaptation , Graph-based Planning , and Relational Learning , with significant contributions to Adversarial Learning , Deep Learning Dynamics , and Mobile Sensor Networks . He has pioneered algorithms like ARA* and Anytime Dynamic A* for efficient planning under constraints. Key research areas: Transfer Learning , Bayesian Knowledge Tracing , Multi-agent Systems , Database Optimization His work has been cited thousands of times across foundational topics in AI and robotics, demonstrating sustained impact in algorithm design and applications to complex systems.

Angel Xuan Changمشاهده پروفایل

دانشیار

• Natural language processing
• 3D scene understanding
• Multimodal grounding of language
• +۵ مورد دیگر

Angel Xuan Chang is an Associate Professor at Simon Fraser University's School of Computing Science, where she leads research at the intersection of natural language processing, computer vision, and 3D scene understanding. She holds the prestigious Canada CIFAR AI Chair position and is affiliated with multiple research groups including 3DLG, GrUVi, SFU NatLang, SFU AI/ML, and VINCI. PhD in Computer Science, Stanford University MSc in Computer Science, Stanford University M.Eng in Electrical Engineering and Computer Science, MIT BSc in Computer Science and Engineering, MIT Professor Chang's research primarily focuses on connecting language to 3D representations of shapes and scenes, with particular emphasis on grounding language for embodied agents in indoor environments. Her work spans natural language processing and understanding, linking natural language with visual and 3D representations, multimodal grounding of language, embodied AI, and machine learning applications for biodiversity monitoring through the BIOSCAN project. She has developed methods for synthesizing 3D scenes and shapes from natural language and created various datasets for 3D scene understanding. Her recent publications reveal a strong trend toward integrating language understanding with 3D scene generation and manipulation, with increasing focus on practical applications in embodied AI and biodiversity monitoring. The research shows progression from foundational work on text-to-3D scene generation to more sophisticated approaches for evaluating semantic coherence in generated scenes and developing efficient methods for zero-shot scene modeling. Canada CIFAR AI Chair TUM-IAS Hans Fischer Fellow (2018-2022) Best paper award at 3DV 2025 for 'An Object is Worth 64x64 Pixels: Generating 3D Object via Image Diffusion' Professor Chang actively advises numerous graduate students who appear as first authors on her publications, indicating a strong mentoring program. Her research is supported through multiple channels including the CIFAR AI Chair position and likely various research grants supporting her BIOSCAN-related work and 3D scene understanding projects. She has been involved in organizing multiple workshops at major conferences including ICML, CVPR, and ICLR. Her research is conducted through several interconnected groups: 3DLG (3D Language and Graphics), GrUVi (Graphics, Vision, and Interaction), SFU NatLang (Natural Language Processing), SFU AI/ML, and VINCI. These groups work collaboratively on problems spanning language grounding, 3D scene understanding, embodied AI, and biodiversity applications, creating a rich interdisciplinary research environment.

• Wireless Communications
• MIMO Systems
• Signal Processing
• +۳ مورد دیگر

Oussama Damen is a Professor at the University of Waterloo's Department of Electrical and Computer Engineering. His research focuses on advanced wireless communication systems, particularly in MIMO (Multiple-Input Multiple-Output) systems, signal processing, and machine learning applications in telecommunications. He is actively involved in developing innovative solutions for beamforming, hybrid precoding, and distributed decoding in massive MIMO and millimeter-wave networks. Damen's work also extends to optical fiber communication, federated learning in wireless systems, and optimization of resource allocation in next-generation networks like 5G/6G. His research interests include wireless communication theory, antenna system design, channel modeling, and algorithm development for improving spectral and energy efficiency. He has contributed extensively to the theoretical foundations of MIMO detection, lattice reduction techniques, and statistical signal processing methods. Notable trends in his publications emphasize bridging theoretical performance limits with practical implementations, particularly in scenarios involving channel impairments, limited backhaul capacity, and multi-core fiber transmission. His work often addresses fairness and optimization in distributed systems, including federated learning frameworks and hybrid beamforming architectures. No scientific awards or grants are explicitly mentioned in the provided information. Damen has advised no listed students, and no specific lab affiliations are noted.

Chih-Hung Chenمشاهده پروفایل

استاد

• Biomedical Technologies
• Microelectronics & VLSI
• Digital & Smart Systems
• +۵ مورد دیگر

Dr. Chih-Hung (James) Chen is a Professor in the Department of Electrical & Computer Engineering at McMaster University. His research focuses on noise-related issues in semiconductor devices, low-noise circuit design for medical and communication applications, and thermal noise characterization in nano-scale transistors. He holds senior member status in IEEE and is a licensed Professional Engineer in Ontario. Education: Ph.D., McMaster University, 2002 M.A.Sc., Simon Fraser University, 1997 B.Sc., National Central University, Taiwan, 1991 Research interests include biomedical technologies, microelectronics & VLSI, and digital/smart systems. He has collaborated with companies like Sony Corporation, United Microelectronics Corporation, and Focus Microwaves. His work is supported by grants from the Canada Foundation for Innovation (CFI), NSERC, and the Ontario Innovation Trust (OIT). Notable achievements include serving on the International Advisory Committee of the International Conference on Noise and Fluctuations (2015) and as an editor for the Journal of Low Power Electronics and Applications since 2022. Teaching includes courses like Analysis and Design of RF ICs for Communications and Electronic Devices and Circuits 2. His research lab focuses on advancing noise measurement techniques and designing ultra-low-power analog circuits for emerging applications.

Cristina Rosellمشاهده پروفایل

استاد

• Design and development of innovative grain-based foods
• Quality assessment of cereals and other grains
• Starch physical properties in foods
• +۷ مورد دیگر

Dr. Cristina Rosell is a Professor and Head of the Department of Food and Human Nutritional Sciences at the Faculty of Agricultural and Food Sciences, University of Manitoba. Her research focuses on grain-based food innovation, starch properties, and sustainable bakery processes. Education: PhD and BSc in Pharmacy from Universidad Complutense de Madrid, Spain Dr. Rosell specializes in cereal science, enzymatic food modification, and gluten-free product development. Her current projects include optimizing bakery processes with computational tools, tracing rice supply chains via blockchain, and integrating bioactives into gluten-free foods. Her work bridges food chemistry, nutritional science, and industrial bakery technology. Her recent research trends emphasize gluten-free bread using legume and root starches, starch-plant compound interactions , and sustainable food processing via microwave-assisted extraction, high-pressure treatments, and fermentation. Key subfields include dough rheology, polyphenol bioactivity, and techno-functional ingredient development. Dr. Rosell’s teaching includes graduate seminars in food science (HNSC 7130). She supervises graduate students in breadmaking, starch hydrolysis, and functional food design. Her lab explores grain quality, enzymatic treatments, and bakery product optimization through interdisciplinary approaches combining chemistry, nutrition, and process engineering.

Pascale Bironمشاهده پروفایل

استاد

• Hydro-geomorphology
• River Restoration
• Floodplain Management
• +۳ مورد دیگر

Pascale Biron is a Professor in the Department of Geography, Urban Planning and Environment at Concordia University, Montreal. She holds a Ph.D. in Geography from Université de Montréal (1995) and has been with Concordia since 1998. Her research focuses on river dynamics, stream restoration for fish habitat, flood modeling, and climate change impacts. She specializes in hydrogeomorphology, river management in agricultural watersheds, and numerical modeling of fluvial processes. Research Interests: Her work includes river restoration strategies, flood risk assessment using LiDAR technology, and the 'river freedom' concept promoting ecosystem resilience. She collaborates closely with government agencies to translate research into practical river management policies. Professional Affiliations: Canadian Geomorphology Research Group, Canadian Association of Geographers, American Geophysical Union, GRIL (Limnology Research Group), and RIISQ (Quebec Flood Risk Network). Publications & Research: Recent studies address global salmonid biomass patterns, fluvial hazard detection via machine learning, and large-scale flood modeling. She supervises 19 graduate students in Ph.D./M.Sc. programs in Geography and Environmental Studies, focusing on topics like river confluence hydraulics and agricultural stream restoration. Grants & Funding: Active projects include river dynamics in fish habitats, flood modeling for road infrastructure vulnerability, and computational fluid dynamics simulations of river flows. She also leads research on societal dimensions of river restoration and policy frameworks for flood resilience.

Andrew Warfieldمشاهده پروفایل

استاد مدعو

• Computer Systems Software
• Virtualization
• Distributed Storage
• +۳ مورد دیگر

Andrew Warfield is an Adjunct Professor in the Department of Computer Science at the University of British Columbia (UBC) and a Senior Principal Engineer at Amazon. His research focuses on computer systems software, including virtualization, distributed storage, security, and high-availability systems. He previously held roles as Associate Professor at UBC, CTO at Coho Data, and Technical Director at Citrix Systems. His work has led to projects like Remus (high-availability replication), Tralfamadore (execution analysis), and secure hypervisor development with Xen. Warfield's education includes a PhD from the University of Cambridge's Computer Laboratory, where he researched I/O device virtualization under Steven Hand. He has held visiting roles at Intel Research Cambridge and internships at AT&T Research and Nortel Networks. Research Grants: Supported by Intel Research, NSERC, Network Appliance, and the Communications Security Establishment. Professional Activities: Technical Advisory Board Member at Teradici, and involvement in program committees for conferences like HotOS, EuroSys, and FAST. His research emphasizes practical systems, aiming to bridge the gap between theoretical computer science and real-world applications. Notable contributions include innovations in storage for virtualized environments, secure hypervisor architectures, and disaster-tolerant systems like SecondSite. Warfield is affiliated with UBC's Department of Computer Science and maintains active collaboration with industry partners. Though currently not actively recruiting students, his prior mentorship has influenced many in systems research.

• Human-Computer Interaction
• Virtual Reality
• 3D User Interfaces
• +۷ مورد دیگر

Dr. Robert J. Teather is an Associate Professor and the Director of the School of Information Technology at Carleton University in Ottawa, Canada. He previously served as an interim Director of the School of Information Technology during the 2022-23 academic year. His academic journey includes a PhD in Computer Science from York University (2013) and a postdoctoral fellowship at McMaster University (2015). Dr. Teather's educational background includes: PhD in Computer Science from York University (2013) Master's Thesis: "Comparing 2D and 3D Direct Manipulation Interfaces" from York University (2008), which was awarded the Joseph Liu Thesis Award Dr. Teather's research broadly falls under the field of human-computer interaction, with specialization in 3D user interfaces, virtual reality, and user interfaces for computer games. His work establishes methods for direct comparison of 2D and 3D interfaces for conceptually equivalent tasks, such as selection and manipulation interfaces. He investigates factors influencing human performance in VR, including stereo 3D graphics, haptic feedback, and head-tracking. His research also evaluates novel user interfaces like tilt control or touchscreens, and examines human performance with game input devices in complex tasks involving navigation, selection, and manipulation of objects in game environments. His research has been published extensively in top venues including IEEE VR, ACM SUI, and Graphics Interface. Among his notable scientific achievements are: NSERC Postgraduate Scholarship during his PhD studies Ontario Graduate Scholarship during his PhD studies Best Paper Honourable Mention at the ACM Symposium on Applied Perception 2020 Best Demo Award for SUI 2017 Joseph Liu Thesis Award (2008) Dr. Teather actively supervises graduate students at Carleton University, currently overseeing multiple PhD and Master's students in the areas of human-computer interaction and interactive digital media. His research is supported by NSERC and the Canada Foundation for Innovation, providing funding for his students and laboratory equipment. His students have produced research spanning VR as a persuasive tool to improve vaccine confidence, selection performance using smartphones in VR, and text entry methods in virtual reality environments. Dr. Teather leads a well-equipped CFI-supported lab focused on virtual and augmented reality research. His team works collaboratively on projects related to interactive virtual reality systems, computer game user interfaces, and input devices for 3D interaction. The lab environment fosters interdisciplinary research with opportunities for students to work on cutting-edge VR/AR technologies and contribute to the growing field of spatial computing.

Ralph Evinsمشاهده پروفایل

دانشیار

• Building Energy Simulation
• Energy System Optimization
• Machine Learning Applications in Energy
• +۳ مورد دیگر

Dr. Ralph Evins is an Associate Professor and Director of the Graduate Program in the Department of Civil Engineering at the University of Victoria. He holds affiliations with the Urban Energy Systems laboratory at Empa and ETH Zurich in Switzerland. His expertise spans building energy simulation, energy system optimization, and machine intelligence applications in sustainable design. Evins holds an MEng from Imperial College London and an EngD from the University of Bristol. His research focuses on computational problem-solving in energy systems, including surrogate modeling, optimization algorithms, and machine learning. He develops tools like the Holistic Urban Energy Simulation (HUES) platform and BESOS software framework to bridge building, district, and city-scale energy analysis. His work emphasizes holistic systems thinking, integrating energy hubs, thermal modeling, and digital twin technologies. Recent articles explore surrogate model refinement, inverse modeling for building characterization, and decarbonization strategies. He collaborates with industry to translate academic innovations into practical solutions. Evins advises students in energy systems and leads projects on net-zero building design, retrofit prioritization, and smart grid integration. His research addresses challenges in climate adaptation, energy efficiency, and sustainable urban development through interdisciplinary approaches.

Abhijit Sarkarمشاهده پروفایل

استاد

• Dynamics
• Probabilistic Mechanics
• Uncertainty Quantification
• +۵ مورد دیگر

Abhijit Sarkar is a Professor in the Department of Civil and Environmental Engineering at Carleton University, Ottawa. His work centers on computational dynamics and probabilistic modeling, with office MC 3076 in the Minto Centre for Advanced Studies in Engineering and contact details including phone (613) 520-2600 x6320 and email abhijit_sarkar@carleton.ca . Education: D.Phil. from University of Oxford M.Sc. from Indian Institute of Science (IISc) B.E. from Calcutta University Professional Engineer (P.Eng.) designation His research drives innovation in uncertainty quantification for complex engineering systems. Core interests include dynamics of nonlinear structures, probabilistic mechanics for stochastic finite element methods, and Bayesian inference frameworks for parameter estimation. He pioneers scalable high-performance computing solvers for large-scale systems and sparse learning algorithms to address overfitting in statistical modeling. Recent publications (2022-2024) reveal three dominant trends: (1) Bayesian model calibration for stochastic compartmental systems applied to epidemiology and aerospace, (2) domain decomposition techniques for scalable uncertainty quantification in stochastic PDEs, and (3) sparse learning methods for nonlinear aerodynamic encoding. Key applications span wind turbine vibration analysis, flutter margin prediction, MEMS resonator optimization, and geospatial pandemic modeling. Scientific awards: No awards, fellowships, or medals listed in the source material Graduate supervision includes 6 current students (Ajay Kumar, John Clarabut, Nastaran Dabiran, Sakhi Mittal, Michael Pantano, Brandon Robinson) and 18 graduated students across 17 years (2006-2023). His research leverages high-performance computing for projects in structural dynamics, aeroelasticity, and computational epidemiology, frequently co-supervised with Dominique Poirel and Chris Pettit. Notable grants focus on wind tunnel validation for nonlinear systems and pandemic spread modeling. Based in the Minto Centre for Advanced Studies in Engineering, his computational mechanics group develops algorithms for stochastic dynamics using Carleton University's high-performance computing infrastructure. Collaborations span aerospace engineering (flutter analysis), civil infrastructure (seismic wave propagation), and public health (Covid-19 modeling).