جست‌وجوی استاد و پژوهشگر

Ghassan Hamarnehمشاهده پروفایل

استاد

• Medical Image Analysis
• Scientific computing
• Signal and image processing
• +۵ مورد دیگر

Ghassan Hamarneh is a Professor in the School of Computing Science at Simon Fraser University (SFU). His research focuses on medical image analysis, with expertise in super-resolution microscopy, explainable AI, and biomedical computing. He teaches courses in biomedical computing and scientific computing, emphasizing practical applications like signal processing and health informatics. Education: Ph.D. in Signal and Systems (Chalmers University, 2001), M.Sc. in Digital Communications (Chalmers, 1997), B.Sc. in Electrical Engineering (Jordan University, 1995). Research Interests include developing AI-driven tools for medical imaging, analyzing cellular structures using super-resolution techniques, and addressing ethical challenges in AI deployment. His work bridges computational methods with clinical applications, such as lesion segmentation, PET image analysis, and bias mitigation in medical algorithms. Recent publications highlight advancements in network analysis of molecular structures, debiasing AI models, and improving diagnostic accuracy through deep learning. His lab contributes to open-source software like SuperResNET and MCS-DETECT for super-resolution microscopy analysis. No scientific awards explicitly listed, but his extensive publication record reflects recognition in the field. Advising and grants information is not detailed in the provided texts. Active in teaching, including CMPT 340 (Biomedical Computing) and special research projects.

Martin Müllerمشاهده پروفایل

استاد

• Artificial Intelligence
• Game Theory
• Monte Carlo Tree Search
• +۷ مورد دیگر

Martin Müller is a Professor in the Department of Computing Science at the University of Alberta, where he conducts research in artificial intelligence, game theory, and heuristic search. He holds the Canada CIFAR AI Chair at Amii and is an Amii Fellow, underscoring his leadership in AI. His research group focuses on Monte Carlo tree search, reinforcement learning, combinatorial game theory, and automated planning, with applications in games such as Go, Hex, and NoGo. His research interests span Monte Carlo and exact methods in game-tree search , exploration in heuristic search and machine learning , and algorithms in combinatorial game theory . He has developed open-source software like MCGS (Minimax-based Combinatorial Game Solver) and contributes to game-playing systems such as Fuego for Go. His work bridges theoretical foundations with practical implementations in AI-driven game solvers. Recent publications show a strong trend in reinforcement learning , particularly in deep Q-learning, policy gradient methods, and anomaly detection in deep RL. His team also explores combinatorial game solving , sparse reward environments , and imperfect information games . The research integrates machine learning with classical AI techniques, emphasizing empirical validation and algorithmic innovation. Canada CIFAR AI Chair Amii Fellow Best student paper award at IEEE Conference on Games 2024 Best paper award at IEEE COG 2021 Outstanding paper award at AAAI-18 Faculty of Science Dissertation Award (2016) Dissertation Award from the Canadian Artificial Intelligence Association (2013) Müller has supervised numerous PhD and MSc students, including Hongming Zhang, Henry Du, and Timo Bertram, many of whose theses focus on game AI, reinforcement learning, and combinatorial optimization. He is funded by NSERC, Mitacs, and Compute Canada. His group collaborates on projects involving neural networks for game playing, SAT solving, and planning algorithms. He is currently on sabbatical but remains academically active, teaching a graduate course on combinatorial games in 2025 and hosting visiting researchers. His lab is involved in the development of MCGS, a solver for sum games, and contributes to open-source AI software. The team publishes regularly in top venues such as NeurIPS, ICML, AAAI, and IEEE Transactions on Games. Future work includes advancing combinatorial game solvers, improving deep RL robustness, and exploring generalization in game representations.

Thomas K. Uchidaمشاهده پروفایل

دانشیار

• Biomechanics
• Dynamical Systems
• Musculoskeletal Modeling
• +۷ مورد دیگر

Thomas K. Uchida is an Associate Professor in the Department of Mechanical Engineering at the University of Ottawa, a position he has held since May 2024. Prior to this promotion, he served as an Assistant Professor at the same institution from October 2018 to May 2024. Before joining the University of Ottawa, Dr. Uchida was an Engineering Research Associate (April 2015-August 2018) and Simbios Distinguished Postdoctoral Fellow (July 2012-April 2015) in the Department of Bioengineering at Stanford University. Dr. Uchida's research focuses on the modeling and simulation of dynamic systems, with particular emphasis on human movement biomechanics. His work spans multiple areas including: Simulation-guided design of assistive devices for improving mobility Modelling musculotendon dynamics and energy expenditure Parameter identification and model reduction methods Impact and contact dynamics Development of computational tools for biomechanical analysis He is a co-author of the book "Biomechanics of Movement: The Science of Sports, Robotics, and Rehabilitation" published by MIT Press, and actively contributes to the development of OpenSim, an open-source software platform for modeling musculoskeletal systems and generating simulations of human and animal movement. His work on OpenSim was featured on the cover of PLoS Computational Biology. Dr. Uchida's recent publications demonstrate strong activity in biomechanics, robotics, and computational modeling. His work bridges engineering principles with biological applications, particularly in understanding human movement mechanics. Key trends include applying machine learning to gait analysis, developing enhanced spine models, analyzing human balance stability with time delays, and advancing musculoskeletal simulation techniques. As an academic advisor, Dr. Uchida currently supervises seven graduate students: Firas Baklouti (expected completion August 2025) Shahin Sharafi Kazem Alambeigi Jiawei Gao Yuzhen Yan Manuel Lucas De Oliveira Blake Scott Miller Dr. Uchida collaborates with research teams focused on biomechanics and movement science. His work with OpenSim places him within an international community of researchers developing computational tools for biomechanical analysis, connecting mechanical engineering with biomedical applications in sports, robotics, and rehabilitation.

Li Yangمشاهده پروفایل

استادیار

• Cybersecurity
• Machine Learning
• Deep Learning
• +۱۲ مورد دیگر

Li Yang is an Assistant Professor in the Department of Information Technology , part of the Faculty of Business and Information Technology at Ontario Tech University. His research focuses on applying AI and machine learning to cybersecurity, particularly in intrusion detection and anomaly detection for 5G/6G networks and IoT systems. He holds a PhD in Electrical and Computer Engineering from Western University (2022), and has held roles such as Vice Chair of IEEE Computer Society, London Section (2022–2023). Education: PhD in Electrical and Computer Engineering, Western University (2022) Master of Science, University of Guelph (2018) Bachelor of Engineering, Wuhan University of Science and Technology (2016) Research Interests: His work spans cybersecurity, machine learning, deep learning, AutoML, model optimization, network automation, IoT security, intrusion detection, and adversarial machine learning. He develops frameworks for concept drift adaptation and online learning to enhance cybersecurity measures, with a focus on trustworthy AI and defense strategies against adversarial attacks. Awards: Graduate Student Award for Excellence in Research (2022) Graduate Symposium Award for Best Presentation (2022) Mitacs Accelerate Fellowship (2021) OC2 Lab Industrial Research Excellence Award (2020) Ranked in Stanford/Elsevier's Top 2% Scientists (2024) Grants & Involvement: Li Yang has contributed to conferences like IEEE GlobeCom and IEEE CCECE, and authored patents such as 'Convenient primary-secondary barrels' (2009). His work has garnered thousands of citations and GitHub stars, emphasizing practical applications of AI in cybersecurity.

Philippe Dixonمشاهده پروفایل

استادیار

• Biomechanics
• Neuroscience
• Machine Learning
• +۳ مورد دیگر

Dr. Philippe Dixon is an Assistant Professor in the Department of Kinesiology & Physical Education at McGill University, with a division in Biomechanics and Neuroscience. He holds adjunct professor roles at the University of Montreal (School of Kinesiology and Physical Activity Sciences) and the University of Laval (Department of Kinesiology). His research focuses on human movement biomechanics using motion capture systems and wearable sensors, combined with machine learning for health and athletic performance optimization. He has expertise in gait analysis, muscle coactivation patterns in cerebral palsy, and predictive modeling of physiological states. Dr. Dixon earned a Post-doctoral fellowship in Public Health at Harvard University, a PhD in Engineering Science from the University of Oxford, and dual degrees in Biomechanics and Physics from McGill University. His education includes a Bachelor of Education in Mathematics and Physics (McGill), a Master of Science in Biomechanics (McGill), and a PhD in Engineering Science (Oxford). He has received grants such as the NSERC Discovery Grant (2022–2027) and the FRQSC AUDACE Grant (2022). His work emphasizes wearable sensor integration, with contributions to datasets like NACOB and tools like OpenOFM. He currently supervises Master’s and PhD students in biomechanics and machine learning applications. Key research themes include gait adaptations on uneven surfaces, machine learning for cough detection via smart garments, and musculoskeletal coordination in clinical populations. His articles span biomechanical modeling, wearable sensor validation, and neuro-musculoskeletal analysis, reflecting interdisciplinary innovation in human movement science.

Hassan Sajjadمشاهده پروفایل

دانشیار

• Natural Language Processing
• Deep Learning
• Safe and Trustworthy AI
• +۷ مورد دیگر

Hassan Sajjad is an Associate Professor in the Faculty of Computer Science at Dalhousie University, Halifax, Canada. He is also the Director of HyperMatrix, a research group focused on AI and deep learning. His work centers on Natural Language Processing, Safe and Trustworthy AI, interpretability, and robustness of language models. PhD - University of Stuttgart, Germany (2012) Masters - National University of Computer and Emerging Sciences, Pakistan (2007) BSc - National University of Computer and Emerging Sciences, Pakistan (2005) Hassan Sajjad's research focuses on making AI systems more interpretable, robust, and safe. He investigates how deep learning models, particularly transformers, encode linguistic and conceptual knowledge. His work spans language generation , model editing , interpretability , and generalization . He develops tools like NeuroX and NxPlain to analyze neuron-level behavior in NLP models. His research also extends to crisis informatics and multilingual NLP, especially Arabic and Urdu. The recent publications show a strong trend in analyzing and interpreting deep NLP models, with a focus on neuron interpretation , latent concept discovery , and model robustness . His work appears in top-tier venues like NeurIPS, ICLR, ACL, and EMNLP, indicating high impact in the AI and NLP communities. There is a clear emphasis on developing practical tools and frameworks for model analysis. No formal scientific awards are mentioned in the provided text. Hassan Sajjad mentors students and has fellowship opportunities available. While specific grants are not listed, his extensive publication record and leadership roles suggest active research funding. He advises students in AI, NLP, and deep learning, and collaborates widely across institutions. He leads the HyperMatrix research group at Dalhousie University, which focuses on AI, deep learning, and NLP. The group develops tools for model interpretation and works on safe and trustworthy AI. Collaborations extend to institutions like MBZUAI, NYU Abu Dhabi, and various international research centers.

Stephen Brownمشاهده پروفایل

استاد

• Field-Programmable Gate Arrays (FPGAs)
• Computer Architecture
• CAD Algorithms
• +۴ مورد دیگر

Stephen Brown is a Professor at the University of Toronto within the Department of Electrical and Computer Engineering under the Faculty of Applied Science and Engineering. He earned his B.A.Sc and M.A.Sc in Electrical Engineering from the University of Toronto and New Brunswick, respectively, and a Ph.D. in Electrical Engineering from the University of Toronto (1992). His career spans over two decades in academia and industry collaboration. Education : B.A.Sc, University of New Brunswick M.A.Sc, University of Toronto Ph.D, University of Toronto Professor Brown’s research focuses on field-programmable gate arrays (FPGAs) , CAD algorithms , and computer architecture , with applications in machine learning and high-level synthesis . He is a principal investigator in the LegUp project , an open-source high-level synthesis framework that bridges software and hardware design. His work also extends to optimizing FPGA interconnect delays, physical synthesis, and logic block architectures. Key trends in his publications include advancements in high-level synthesis tools, FPGA architecture evaluation, and timing-driven design methodologies. His contributions often intersect with design automation , resource sharing , and embedded systems . Scientific Awards : NSERC 1992 Doctoral Prize Hart Professorship for Innovation in Teaching (2017) Multiple teaching excellence awards Best Paper Award at ICCAD 1990 Best Paper Award nomination at Canadian Conference on VLSI (1989) As Director of the FPGA University Program at Intel Corporation, he leads industry-academia initiatives. His teaching portfolio includes courses like ECE253 (Digital Logic) and ECE1733F (Switching Theory).

Jean-François Godboutمشاهده پروفایل

استاد

• Data Science
• AI Safety
• Generative AI
• +۶ مورد دیگر

Jean-François Godbout is a Professor in the Department of Political Science at the Université de Montréal and an Associate Academic Member of Mila - the Quebec AI Institute. He directs the undergraduate program in Big Data Analytics in Social Sciences and Humanities at UdeM and conducts interdisciplinary research through the Complex Data Lab. Affiliated with IVADO (AI Consortium) Member of CÉRIUM (International Research Centre) and CECD (Democratic Citizenship Centre) His research focuses on: Data Science applications in political institutions AI Safety and generative AI's impact on political attitudes Misinformation Mitigation through large language models Comparative Political Development in Canadian and Lower Canada contexts Legislative Institutions and voting records analysis Political Polarization in online societies Recent publications analyze social media disinformation, AI persuasion on harmful topics, and education-focused text simplification. His articles frequently combine graph mining , machine learning , and political science methodologies. Scientific collaborations include: Mila researchers (Andreea Musulan, Maximilian Puelma Touzel) IVADO data science initiatives McGill University interdisciplinary projects He supervises students in: Political science (Julien Robin, Matthew Taylor) Artificial Intelligence (Kellin Pelrine, Camille Thibault) Computational social science applications

Christian Seilerمشاهده پروفایل

استادیار

• Climate Change
• Terrestrial Carbon Cycle
• Earth System Modeling
• +۳ مورد دیگر

Dr. Christian Seiler is an Assistant Professor at Queen's University, holding cross-appointments in the School of Environmental Studies and the School of Computing. His research focuses on the role of the land surface in the global climate system, particularly the terrestrial carbon cycle's response to anthropogenic CO₂ emissions. He specializes in numerical modeling, global earth observations, and evaluating climate models like the Canadian Earth System Model (CanESM). His work addresses critical questions about forest dieback, model biases, and carbon dynamics under future climate scenarios. Education: Ph.D. Earth System Science, Wageningen University (2009-2014) M.Sc. Earth System Science, Wageningen University (2006-2008) B.Sc. Forest and Environment, University of Eberswalde (2001-2003) Research Interests: Dr. Seiler’s research integrates climate modeling, biogeochemical cycles, and earth system dynamics to understand tipping points in the land carbon sink. He leverages machine learning and dynamical systems theory to advance model evaluation and climate projections. His work supports initiatives like the Canadian Land Surface Scheme Including Biogeochemical Cycles (CLASSIC) and the CanESM. Publications Trends: His articles emphasize model benchmarking, sensitivity analysis, and climate forcing biases. Recent work explores nitrogen cycle disconnects in terrestrial models and the impacts of dynamic land cover changes. His contributions span climate extremes, decadal predictions, and earth observation applications. Awards/Grants: 2023 Environment Canada Grant (CAD 70,000, co-applicant) 2022 Queen's University Research Initiation Grant (CAD 50,000) 2018 NSERC Discovery Grant (CAD 162,500) Advising & Teaching: He supervises PhD students like Florence Kayode and Raj Deepak. Courses include Climate Change Science and Policy , Land-Atmosphere Interactions , and The Climate System . His teaching integrates R programming, dynamical systems theory, and climate model analysis. Labs/Teams: Collaborates with Environment and Climate Change Canada, the Pacific Climate Impacts Consortium, and international modeling teams to advance earth system science and climate policy.

M. Tariq Iqbalمشاهده پروفایل

استاد

• Hybrid Power Systems
• Renewable Energy Systems
• Consumer Electronics
• +۱ مورد دیگر

M. Tariq Iqbal is a Professor in the Department of Electrical and Computer Engineering at Memorial University of Newfoundland. He holds a B.Sc. from UET Lahore, M.Sc. from QAU Islamabad, and PhD from Imperial College London. His research develops renewable energy solutions including hybrid power systems, solar applications, and IoT-based monitoring. Projects focus on off-grid communities, industrial applications, and energy-efficient electronics. Specific interests include microgrid design, solar water pumping, and power consumption analysis. Recent publications emphasize techno-economic modeling of microgrids, IoT-enabled SCADA systems, and energy efficiency in computing. Work demonstrates increasing focus on practical implementations in remote locations. No scientific awards are documented. Iqbal advises graduate students on projects across 20+ countries. Current research includes solar-powered oil pumps, electric vehicle charging, and community microgrids. He directs multiple projects through the faculty's engineering design initiative.

Miguel F. Anjosمشاهده پروفایل

استاد

• Mathematical Optimization
• Smart Grids
• Operations Research
• +۳ مورد دیگر

Miguel F. Anjos is Professor and Chair of Operational Research at the School of Mathematics, University of Edinburgh , and holds the NSERC-Hydro-Québec-Schneider Electric Industrial Research Chair on Optimization for Smart Grids at Polytechnique Montréal. He received his B.Sc. (1992), M.S. (1994), and Ph.D. (2001) from McGill, Stanford, and Waterloo respectively. Research Theme Head of Data and Decisions at Edinburgh Founding Director of Trottier Institute for Energy Editor-in-Chief of Optimization and Engineering Research Interests: His work bridges mathematical optimization with smart grid applications , focusing on conic optimization, optimal power flow, demand response, and facility layout. He applies these techniques to energy storage, electric transportation, and industrial systems. Scientific Awards: Méritas Teaching Award (2012) Humboldt Research Fellowship (2009) Queen Elizabeth II Diamond Jubilee Medal (2013) Elected Fellow of EUROPT and Canadian Academy of Engineering Academic Service: Served on Mathematical Optimization Society Council, SIAM Activity Group on Optimization, INFORMS Optimization Society Vice-Chair, and Mitacs Research Review Committee. Hosts benchmark datasets: QAPLIB, FLPLIB, Jones Benchmark.

Jason Heinمشاهده پروفایل

دانشیار

• Mechanistic Organic Chemistry
• Automated Reaction Analysis
• Self-Driving Laboratories
• +۷ مورد دیگر

Jason Hein is an Associate Professor in the Department of Chemistry at the University of British Columbia's Faculty of Science. His research focuses on the development of automated reaction analysis technology and self-driving laboratories that integrate robotics with synthetic organic chemistry. Dr. Hein leads the Hein Lab, which pioneers innovative solutions for mechanistic organic chemistry, catalytic reaction mechanisms, and chemical manufacturing processes. His research interests center on creating modular robotic tools and integrated analytical hardware for automated reaction profiling, with applications in pharmaceutical manufacturing, battery materials processing, and sustainable chemistry. The lab's work combines advanced robotics, artificial intelligence, and process analytical technology to develop self-optimizing chemical systems that accelerate discovery and improve manufacturing efficiency. Analysis of Hein's recent publications reveals a strong focus on AI-driven laboratory automation, with particular emphasis on crystallization optimization for battery materials, computer vision for process monitoring, and interoperable software systems for self-driving laboratories. His work bridges fundamental mechanistic understanding with practical industrial applications, particularly in lithium extraction from waste brines and pharmaceutical process development. NSERC Postdoctoral Fellowship Dr. Hein's research program includes significant grant funding supporting the development of self-driving laboratory technologies and their application to challenging chemical problems. His lab actively collaborates with industry partners in pharmaceuticals and clean energy sectors to translate fundamental insights into deployable technologies. Current projects focus on battery-grade lithium carbonate production, continuous manufacturing processes, and AI-optimized chemical synthesis. The Hein Lab operates as a multidisciplinary research environment combining expertise in organic chemistry, robotics engineering, computer science, and data analytics to create the next generation of autonomous chemical discovery systems.

Shane Dawsonمشاهده پروفایل

استاد

• Learning Analytics
• Educational Data Mining
• Curriculum Development
• +۲ مورد دیگر

Shane Dawson is the Executive Dean of UniSA Education Futures and Professor of Learning Analytics at the University of South Australia. His work bridges social network analysis and learner interaction data to enhance teaching quality and educational outcomes. Affiliation : University of South Australia Research Focus : Learning Analytics, Curriculum Mapping, K-12 Decision-Making Systems, and AI in Education Recent Research Trends : Shane’s 2025 publications emphasize generative AI for curriculum analytics, ethical considerations in K-12 dashboards, and longitudinal graduate attribute monitoring. His articles often integrate psychometric models, social network tools, and open-source software like OVAL and SNAPP . Advising and Collaboration : As a co-developer of key learning analytics tools and a supervisor for research students, he collaborates globally with institutions such as Johns Hopkins University and Shahid Beheshti University of Medical Sciences. Labs and Teams : Shane leads UniSA’s Teaching Innovation Unit and is a founding member of the Society for Learning Analytics Research , driving institutional and international initiatives in educational technology.

Kelly Lyonsمشاهده پروفایل

استاد

• Service Science
• Data Science
• Social Media
• +۴ مورد دیگر

Professor Kelly Lyons is a Professor at the Faculty of Information at the University of Toronto, cross-appointed to the Department of Computer Science. Her research focuses on service science, knowledge mobilization, social media, and data-driven innovation. Prior to academia, she held roles at IBM Toronto Lab's Centre for Advanced Studies. She has secured extensive funding from NSERC, IBM, and industry partnerships, and has advised numerous graduate students. Her work bridges interdisciplinary collaboration, emphasizing AI governance, digital economy impacts, and fostering Women in Technology initiatives. Research interests include the application of social platforms in service systems, data science for knowledge translation, and the societal implications of AI. Key projects involve analyzing gender dynamics in user reviews, open-source software structures, and pandemic-driven innovation trends. Her grants span data science, healthcare analytics, and smart city technologies. Publications span empirical studies on GitHub collaboration, AI governance frameworks, and biomedical knowledge systems. She received the Best Paper Award for 'The Effect of Collaborative Games on Group Work' (2015). Her teaching emphasizes service systems design and project management, with a focus on practical, interdisciplinary learning. Active in scholarly service, she chairs the Consortium for Software Engineering Research and serves on ACM-W's Executive Council. Collaborations include cross-institutional projects with University College London and UCL on AI governance frameworks. Her work extends to promoting STEM education and diversity in tech.

Alexandra Fedorovaمشاهده پروفایل

استاد

• Systems Software
• Memory and Storage Management
• Accelerator-Centric Computing
• +۴ مورد دیگر

Dr. Alexandra Fedorova is a Professor in the Department of Electrical and Computer Engineering at the University of British Columbia (UBC), with an Associate Member role in the Computer Science department. She leads the Systopia systems research group, focusing on system software design, memory/storage management, and accelerator-centric computing. Her work emphasizes performance optimization, energy efficiency, and hardware-software co-design. She holds a PhD from Harvard University (2006), where she researched operating system scheduling under Margo Seltzer. Prior to UBC, she was an Associate Professor at Simon Fraser University (2006–2015). Fedorova is a recipient of the Alfred P. Sloan Research Fellowship and the Anita Borg Early Career Award. She consults for MongoDB's storage engine team and collaborates with industry on storage and performance challenges. Her research spans tools like Non-sequitur for program trace visualization, studies on storage-class memory (e.g., Optane), and frameworks for GPU acceleration. Recent efforts include Sunstone (spatial accelerator scheduling) and ExtMem (application-aware memory management). Her work bridges low-level systems with high-performance computing needs. Key contributions include optimizing NUMA systems, improving storage engine performance, and exploring processing-in-memory architectures. Fedorova’s projects often involve open-source collaboration, reflected in her GitHub repositories such as vividperf and perf-logging , which support performance analysis tools.