جست‌وجوی استاد و پژوهشگر

Sheila McIlraithمشاهده پروفایل

استاد

• Artificial Intelligence
• AI Safety
• AI Alignment
• +۹ مورد دیگر

Sheila McIlraith is a Professor in the Department of Computer Science at the University of Toronto, holding the Canada CIFAR AI Chair at the Vector Institute and serving as Associate Director and Research Lead at the Schwartz Reisman Institute for Technology and Society. Her research spans AI safety, ethics, reinforcement learning, and knowledge representation, with a focus on human-compatible AI and long-term societal impacts. Her career includes six years as a Research Scientist at Stanford University and a year at Xerox PARC. McIlraith’s work has been recognized through ACM and AAAI fellowships, as well as prestigious awards like the SWSA 10-Year Award (2011) and the CAIAC Lifetime Achievement Award (2024). Research Interests: AI Safety and Alignment Human-Compatible AI Reinforcement Learning with Ethical Constraints Semantic Web Services Cognitive Robotics and Diagnostic Systems Probabilistic and Logical Reasoning Recent Contributions: Her work emphasizes ethical AI integration, such as the Embedded Ethics Education Initiative (E3I), and addresses challenges in long-term AI risks, multi-agent systems, and interpretable decision-making frameworks. Awards and Honors: ACM Fellow AAAI Fellow 2023 IJCAI-JAIR Best Paper Prize 2024 CAIAC Lifetime Achievement Award Labs and Teams: McIlraith leads initiatives at the Schwartz Reisman Institute and contributes to the Vector Institute, focusing on societal and ethical dimensions of AI technology.

Qingguo Liمشاهده پروفایل

استاد

• Biomechanical system design
• Energy harvesting
• Wearable sensors
• +۴ مورد دیگر

Qingguo Li is a Professor and Associate Head at the Department of Mechanical and Materials Engineering , Queen's University , and a member of the Ingenuity Labs Research Institute . He specializes in biomechanical system design, energy harvesting, wearable sensors, gait analysis, and load carriage systems. His research integrates robotics, biomedical engineering, and sensor technology to develop human-centric devices and mobility aids. Current Roles : Professor, Associate Head, Queen's University Research Institute : Ingenuity Labs Research Institute Lab : Bio-Mechatronics and Robotics Laboratory His work focuses on biomechanical energy harvesting , IMU-based motion analysis , and assistive device development . Key applications include stroke rehabilitation, gait monitoring, and wearable power generation systems. Articles span cable-driven robots , smart walkers , and 3D printing mechanisms , emphasizing human-robot interaction and dynamic modeling . The lab explores sensor calibration , adaptive control algorithms , and human movement optimization . Areas of impact include rehabilitation engineering , load carriage stability , wearable sensor accuracy , and assistive robotics . His team develops solutions for gait asymmetry detection , post-stroke mobility , and low-cost energy systems , leveraging machine learning and kinetic modeling .

Yiyu Yaoمشاهده پروفایل

استاد

• Three-way Decisions
• Granular Computing
• Rough Sets
• +۸ مورد دیگر

Yiyu Yao is a Professor in the Department of Computer Science at the University of Regina, Faculty of Science. He holds a B.Eng. from Xi'an Jiaotong University and earned both his M.Sc. and Ph.D. from the University of Regina. His office is located in College West 308.6, and he can be reached at Yiyu.Yao@uregina.ca or by phone at (306) 585-5226. Dr. Yao's research spans multiple interconnected domains in intelligent systems. His primary focus is on three-way decisions, which serves as a unifying framework for his work in granular computing, rough sets, and decision-theoretic models. He has developed significant theoretical contributions to decision-theoretic rough sets (DTRS) and probabilistic rough sets, creating bridges between uncertainty management and practical decision-making applications. His work extends to web intelligence, information retrieval systems, and multiview data analysis, where he applies his theoretical frameworks to real-world problems in data science and artificial intelligence. Analysis of Dr. Yao's recent publications reveals a strong continuing focus on three-way decision theory, with increasing applications across diverse domains. His work demonstrates evolution from foundational theoretical contributions to sophisticated applications in multi-criteria decision making, conflict analysis, and explainable AI. The research shows integration of granular computing principles with modern machine learning techniques, particularly in handling uncertainty and developing interpretable models. Recent publications indicate growing interest in the intersection of three-way decisions with fuzzy sets, shadowed sets, and cognitive approaches to data analysis. Dr. Yao has mentored numerous graduate students and has hosted many visiting scholars, primarily from Chinese institutions including Nanjing University Posts and Telecommunication, Harbin Normal University, Shaanxi Normal University, and others. He serves as Area Editor on Rough Sets for the International Journal of Approximate Reasoning and as Associate Editor for Information Sciences. He is also Associate Editor-in-Chief for the Journal of Emerging Technologies in Web Intelligence and serves on multiple editorial boards including LNCS Transactions on Rough Sets and Web Intelligence and Agent Systems. He has organized significant conferences including the International Joint Conference on Rough Sets (IJCRS 2017) and served on the Steering Committee for the International Symposium on Fuzzy and Rough Sets.

Juergen Dingelمشاهده پروفایل

استاد

• Software Engineering
• Model-Driven Engineering
• Formal Methods
• +۷ مورد دیگر

Juergen Dingel is a Professor in the School of Computing at Queen's University, Canada. He joined the faculty in 2000 and holds a PhD in Computer Science from Carnegie Mellon University (1999). His research focuses on software modeling, model-driven engineering, formal methods, and formal verification, with applications in real-time systems and embedded systems. He leads the Modeling and Analysis in Software Engineering (MASE) research group. Education: PhD in Computer Science, Carnegie Mellon University (1999) M.Sc. in Pure and Applied Logic, Berlin University of Technology (1994) M.Sc. in Computer Science, Berlin University of Technology (1992) Research Interests: Model-driven engineering and transformation Formal specification and verification Automated testing and debugging Real-time and embedded systems Service composition and distributed systems His work emphasizes practical tools like Papyrus-RT and MDebugger , integrating formal methods into software development. Grants & Collaborations: Funded by NSERC, OCE, and industry partners (IBM, GM, Ericsson) Focus on automotive systems, IoT, and safety-critical applications Service: Editorial board member for SoSyM , STTT , and JOT Former chair of the MODELS Steering Committee (2016–2018) PC co-chair for MODELS 2014 and FMOODS/FORTE 2011 Labs & Teams: Leads the MASE group, which develops open-source tools for model-driven engineering. Collaborates with industry on automotive and IoT projects.

• Power Electronics
• Electrical Engineering
• Energy Conversion
• +۷ مورد دیگر

Kamal Al Haddad is a Lecturer in the Department of Electrical Engineering at École de technologie supérieure (ÉTS). He holds a Doctorate from INTP, Toulouse, and advanced degrees from UQTR. His research focuses on power electronics, renewable energy integration, and smart grid technologies. He leads the GREPCI research group, specializing in Power Electronics and Industrial Control. Education: B.Eng., M.Sc.A. (UQTR), Doctorate (INTP, Toulouse). Research interests span energy conversion, industrial electronics, power quality, and electromagnetic interference. He emphasizes sustainable energy solutions, electric traction systems, and high-efficiency power sources. His work includes developing advanced power electronic converters and grid stability solutions. Recent articles highlight advancements in modular converters for STATCOM, AI-driven fault detection in hydrogenerators, and renewable energy policy frameworks. He has received notable awards, including the 2014 IEEE Eugene Mittelmann Prize and Fellowships from IEEE and other institutions. Supervised over 60 students, including doctoral theses on topics like hydrogenerator diagnostics, EV charging systems, and renewable energy integration. His research also involves real-time simulation of power systems and FPGA-based implementations. Labs/Teams: GREPCI – Power Electronics and Industrial Control Research Group, leading projects on smart grids and energy efficiency.

Simon Yangمشاهده پروفایل

استاد

• Artificial Intelligence
• Robotics
• Control Systems
• +۷ مورد دیگر

Simon Yang is a Professor in the School of Engineering at the University of Guelph, part of the College of Engineering and Physical Sciences. His research focuses on artificial intelligence, robotics, sensors, control systems, and bio-inspired intelligence. He has contributed to advanced robotics applications, including mobile robot navigation, underwater vehicle control, and agricultural automation. Dr. Yang holds editorial roles for journals such as the International Journal of Robotics and Automation and IEEE Transactions on Cybernetics . His work bridges theoretical advancements with practical implementations in areas like sensor networks, machine learning, and multi-agent systems. Recent projects include developing robust control frameworks for autonomous systems, digital twin applications, and bio-inspired neural network algorithms. His research emphasizes real-world challenges in robotics, environmental monitoring, and precision agriculture, with a focus on integrating AI-driven solutions for enhanced decision-making and system reliability. Professional contributions include advisory roles in multiple journals and conference committees, reflecting his leadership in the field.

Xujie Siمشاهده پروفایل

استادیار

• Artificial Intelligence
• Machine Learning
• Software Engineering
• +۵ مورد دیگر

Xujie Si is an Assistant Professor in the Department of Computer Science at the University of Toronto. He is also a faculty affiliate at the Vector Institute and an affiliate member at Mila - Quebec AI Institute, holding a Canada CIFAR AI Chair. Previously, he served as an Assistant Professor at McGill University's School of Computer Science. Education: Ph.D., Computer and Information Science, University of Pennsylvania (advised by Mayur Naik) M.S., Computer Science, Vanderbilt University B.E. (with Honors), Nankai University Research Focus: His work bridges AI and program reasoning, emphasizing the integration of statistical and logical methods. Key areas include: Static analysis and verification using deep learning/reinforcement learning Neuro-symbolic systems for urban simulation (e.g., LogiCity) Automated theorem proving via LLMs and symbolic reasoning Program repair and compiler fuzzing Recent Article Trends: Recent work focuses on synergizing LLMs with symbolic reasoning (e.g., Olympiad inequality proving), advancing SAT solving with graph neural networks, and applying neuro-symbolic methods to Euclidean geometry formalization. Awards: Canada CIFAR AI Chair (2023) Lab/Teams: Leads research teams exploring program analysis, neuro-symbolic AI, and formal verification at the University of Toronto and Vector Institute.

Howard Schwartzمشاهده پروفایل

استاد

• Machine Learning
• Adaptive Control
• Multi-Agent Machine Learning
• +۳ مورد دیگر

Professor Howard Schwartz is a distinguished academic in Electrical and Computer Engineering at Carleton University , with a career spanning academia, industry, and robotics research. He earned his BEng in Civil Engineering from McGill University (1982), followed by MSc (1984) and PhD (1986) in Aerospace and Mechanical Engineering from MIT. His academic leadership includes serving as Department Chairman (2009-2013) and authoring the seminal text Multi-Agent Machine Learning: A Reinforcement Approach (Wiley, 2014). As an Associate Editor for IEEE Transactions on Cybernetics , he shapes discourse in cybernetic systems and machine learning. Research Focus Professor Schwartz's research bridges theoretical and applied domains: Machine learning for robotics and autonomous systems Adaptive control theory and multi-agent systems Reinforcement learning algorithms with fuzzy logic integration Real-time video analytics and GPS receiver development Nonlinear control systems for UAVs and wind energy optimization Industry Experience His career includes impactful industry engagements: Early development of high-performance GPS receivers at Canadian Marconi Co. (1982-1984) Sabbatical at March Networks (2001-2002) for video analytics Software quality control at CMC Electronics (2008) Software development for TV set-top boxes at Espial Inc. (2014-2015)

Brian Rossمشاهده پروفایل

استاد

• Artificial Intelligence
• Computational Intelligence
• Genetic Programming
• +۱۰ مورد دیگر

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).

Peter Lewisمشاهده پروفایل

دانشیار

• Artificial Intelligence
• Socio-Technical Systems
• Self-Awareness
• +۷ مورد دیگر

Peter Lewis is an Associate Dean (Research and Graduate Studies) and Associate Professor at Ontario Tech University's Faculty of Business and Information Technology, holding a Canada Research Chair in Trustworthy Artificial Intelligence. He specializes in socio-technical systems, self-aware computing, and AI ethics. His work bridges foundational AI research with practical applications in industry, emphasizing trust, collective action, and socially intelligent systems. Education PhD in Computer Science, University of Birmingham, UK MSc in Natural Computation, University of Birmingham, UK BSc in Computer Science, University of Leicester, UK PGCert in Learning and Teaching Higher Education, Aston University, UK Research Interests His research focuses on trustworthy AI, self-aware systems, and the societal implications of AI. Key areas include explainable AI for accessibility, ethical AI governance, and modeling social institutions. He co-edited Self-Aware Computing Systems and serves on editorial boards for IEEE journals. Publications & Awards Over 75 peer-reviewed papers in journals/conferences Winner of the 2020 Aston Achievement Award for Business Engagement Best Paper Award at IEEE SASO 2018 Labs & Collaborations He leads research in the Business Analytics & AI group, collaborating on projects with industry partners. His work addresses real-world challenges in AI ethics, sustainability, and human-AI collaboration.

Quentin Cappartمشاهده پروفایل

دانشیار

• Artificial Intelligence
• Combinatorial Optimization
• Constraint Programming
• +۵ مورد دیگر

Quentin Cappart is an Associate Professor at Polytechnique Montréal since 2020, specializing in artificial intelligence , combinatorial optimization , and constraint programming . His work integrates machine learning and operations research to solve complex industrial problems, particularly in transportation , logistics , and healthcare . Affiliations: Co-founder and Head of the Combinatorial Optimization and Reasoning in Artificial Intelligence Laboratory (CORAIL) Interuniversity Research Centre on Enterprise Networks, Logistics and Transportation (CIRRELT) IVADO researcher Affiliate member of MILA Researcher at IMC2 institute Research focus: Hybrid algorithms combining logical reasoning , machine learning , and operations research to enhance decision-making tools. His group emphasizes accessibility for non-experts and small companies. Scientific Awards: 2025: Early Career Engineer Award (Ordre des ingénieurs du Québec) 2025: Early Career Professor Award (Polytechnique Montréal) 2025: Prix Méritas (Polytechnique Montréal) 2024: Early Career Researcher Award (ACP) 2024: Best Paper Award (ACP Machine Learning track) 2023: Winning approach for Citylearn challenge (NeurIPS) Teaching: Courses include INF8175: Artificial intelligence - methods and algorithms and INF6102: Local search and metaheuristics . He also teaches at ACP Summer/Winter Schools.

Yan Yanمشاهده پروفایل

استادیار

• Bioinformatics
• Machine Learning
• Big Data Analysis
• +۷ مورد دیگر

Yan Yan is an Assistant Professor at the School of Computer Science, University of Guelph. His research focuses on bioinformatics, multi-omics analysis, and AI-driven computational models for understanding biological processes. His lab develops algorithms for genome-wide association studies, single-cell RNA-seq analysis, and de novo peptide sequencing, with applications in plant genomics and digital agriculture. Education : Not explicitly stated, but his publications suggest advanced training in bioinformatics and computational biology. Research Interests : Bioinformatics, Machine Learning, Big Data Analysis, Statistical Association, and AI applications in agriculture. His work integrates artificial intelligence with omics data to uncover disease mechanisms and improve agricultural practices. Recent trends in his publications emphasize robotics, structural health monitoring, and environmental sensing using hybrid neural networks and deep learning techniques. Grants & Awards : No awards explicitly listed, but his prolific publication record indicates sustained research activity. Advising & Labs : Advises students like B S Puliparambil and J Tomal. His lab actively collaborates on projects involving AI in agriculture, robotics, and structural engineering. Current initiatives include digital twins for human-robot collaboration and precision livestock monitoring systems. Labs/Teams : Leads a computational biology and AI lab focusing on multi-omics integration and smart agriculture solutions.

• Workflow Verification
• Ontology and Logic Reasoning
• Healthcare Information Systems
• +۳ مورد دیگر

Wendy MacCaull is an Adjunct Professor in the Department of Computing and Software at McMaster University's Faculty of Engineering. Her scholarly activity focuses on formal methods for healthcare workflow verification, ontology reasoning, and temporal logic applications in process mining. Role: Adjunct Professor Institution: McMaster University Department: Computing and Software Research Interests: Workflow Verification, Ontology Reasoning, Healthcare Process Mining Her work bridges formal logic and healthcare IT , with publications on compensable workflows, timed BDI CTL logic verification, and ontology merging for clinical systems. She has contributed to Journal of Symbolic Logic , Lecture Notes in Computer Science , and IEEE Transactions on Knowledge and Data Engineering . Recent trends include contextual process mining (2024) and multi-context reasoning architectures (2023). Earlier works (2008-2012) explored non-classical logics, residuated logic models, and Kripke semantics.

Shakil M. Khanمشاهده پروفایل

استادیار

• Knowledge Representation
• Causal Reasoning
• Multi-Agent Systems
• +۳ مورد دیگر

Dr. Shakil M. Khan is the SaskPower Assistant Professor in Artificial Intelligence at the University of Regina's Department of Computer Science. His primary research develops formal models for causal reasoning in artificial intelligence, focusing on knowledge representation and rational agent systems. He holds a Ph.D. from York University and leads the PRACToR lab. Education: Ph.D. Computer Science, York University (2018) B.Sc. Computer Science, University of Windsor Dr. Khan's research program develops logical frameworks for actual causation in nondeterministic domains, with applications to explainable AI and multi-agent systems. His work combines situation calculus with game-theoretic approaches to model causal relationships and responsibility attribution. Current projects investigate abstraction techniques for concurrent game structures and root cause analysis in hybrid dynamic systems. His publications demonstrate strong theoretical foundations in logic-based AI, with recent articles exploring the semantics of causality, knowledge representation, and agent modeling. Research keywords frequently include situation calculus, formal verification, and multi-agent systems. Dr. Khan supervises graduate students working on causal reasoning projects and has received NSERC Discovery funding (2022-2027). He serves on program committees for leading AI conferences including AAAI, IJCAI, and KR. Dr. Khan teaches courses in discrete structures, artificial intelligence, and knowledge representation.

Stephen L. Smithمشاهده پروفایل

استاد

• Robotics
• Artificial Intelligence
• Control Systems
• +۳ مورد دیگر

Stephen L. Smith is a Professor and Tier 2 Canada Research Chair in the Department of Electrical and Computer Engineering at the University of Waterloo. He serves as Co-Director of the Waterloo Artificial Intelligence Institute, leading research in robotics and AI. His work focuses on autonomous systems, multi-robot coordination, and human-robot interaction, with applications in navigation, control systems, and collaborative task planning. Smith's research explores challenges such as path planning in uncertain environments, persistent monitoring, and adaptive decision-making. His contributions include algorithms for robot navigation in ice-covered waters, real-time replanning under constraints, and optimizing multi-agent systems for efficiency and safety. Key scientific awards include the Tier 2 Canada Research Chair designation. His research also addresses human-centric aspects, such as studying user preferences in collaborative tasks and developing frameworks for learning user intent through interaction.