Find Academic Supervisors

• Robotics
• Artificial Intelligence
• Control Theory
• +7 more

Roya Firoozi is an Assistant Professor in the Department of Electrical and Computer Engineering at the University of Waterloo, Canada. She holds a Ph.D. in Control Theory from UC Berkeley (2021) and a Postdoctoral Research position at Stanford University's Multi-Robot Systems Lab (2024). Her research focuses on advancing safe robot autonomy in interactive environments through generative AI, distributed optimization, and game theory. Key areas include multi-agent systems, autonomous vehicles, and perception-driven control. Education: Ph.D. in Control Theory, UC Berkeley (2021) Bachelor's in Mechanical Engineering, UC Berkeley (2014) Postdoctoral Research, Stanford University (2024) Research Interests: Robotics-aided generative AI, multi-modal perception, interactive autonomy in multi-agent systems, game-theoretic optimization, and safe navigation in dynamic environments. Her work bridges theoretical foundations with real-world experiments in robotics and autonomous systems. Awards: NSF Postdoctoral Research Fellowship ASEE Fellowship (2021-2023) Rising Stars in Aerospace Engineering (2022) Outstanding Graduate Instructor Award (2021) Advising & Grants: Currently supervising graduate students (PhD/Master's) focused on robotics and control systems. Research is supported by grants from NSF and industry collaborations. She has pioneered algorithms for distributed multi-vehicle coordination and occlusion-aware navigation systems. Labs & Teams: Leads the Autonomous Systems Lab at Waterloo, focusing on robotics, AI, and control. Collaborates with Stanford's Multi-Robot Systems Lab and UC Berkeley's MPC Lab on projects involving autonomous vehicles and fault detection systems.

Mohamed KamelView profile

Professor

• Machine Learning
• Biometrics
• Robotics
• +7 more

Mohamed Kamel was a Professor in the Department of Electrical and Computer Engineering, co-founding director of the Centre for Pattern Analysis and Machine Intelligence (CPAMI). He contributed extensively to interdisciplinary research across machine learning, robotics, biometrics, and vehicular networks. His work integrated computational methods with ecological, medical, and engineering challenges. Research interests span machine learning applications, biometric systems, robotics, image analysis, and VANET (Vehicular Ad Hoc Network) technologies. Notable contributions include biometric identification systems, human-robot interaction frameworks, and environmental modeling using Maxent techniques. He also explored advanced battery systems and genetic diagnostics for psoriasis treatment. Publications reflect a focus on interdisciplinary topics: from underwater gesture recognition to protein sequence analysis, and from Li-ion battery cathodes to medical imaging algorithms. His work bridges theoretical advancements with practical implementations in safety systems, conservation biology, and clinical diagnostics. Awards and recognitions are not explicitly stated in available texts. His legacy includes foundational contributions to the CPAMI and mentorship through collaborative research projects.

Christophe DubachView profile

Associate Professor

• Data-Parallel Language Design
• Hardware Design Space Exploration
• Heterogeneous Computing
• +4 more

Christophe Dubach is an Associate Professor at McGill University, jointly appointed in the Department of Electrical and Computer Engineering (ECE) and the School of Computer Science (CS). He holds a Canada CIFAR AI Chair at Mila. Previously, he was a Reader (Associate Professor) at the University of Edinburgh until 2019. His research focuses on data-parallel language design, high-level code generation for parallel hardware (GPUs/FPGAs), hardware design space exploration, and applying machine learning to these areas. He has advised numerous PhD and MSc students on topics like FPGA-based high-level synthesis, neural networks, and compiler optimization. Education: PhD in Informatics (2009, University of Edinburgh), MSc in Computer Science (2005, EPFL, Switzerland). Teaching includes courses on compiler design, computer organization, and high-level synthesis. His projects include Lift (a functional IR for GPU code generation) and High-Level Synthesis of Neural Networks on FPGAs. Collaborators include Michel Steuwer, Lu Li, and others in the fields of compilers, machine learning, and parallel computing.

• Computer Science Education
• Computer Systems
• Object-Oriented Programming
• +5 more

Mark A. Holliday is a Professor of Computer Science at Western Carolina University (WCU), where he has served since 2004. He holds a Ph.D. in Computer Science from the University of Wisconsin–Madison (1986) and has extensive experience in academia, including roles as Interim Department Head (2007–2009) and Visiting Research Scientist at institutions like the University of Portsmouth (UK) and the University of Toronto. His primary affiliation is with the Department of Mathematics and Computer Science at WCU. Education Background: Ph.D. in Computer Science, University of Wisconsin–Madison (1986) Master's in Computer Science, University of Wisconsin–Madison (1982) B.A. in Mathematics and Economics, University of Virginia (1978) Research Interests: Holliday focuses on enhancing computer science education, particularly in teaching methodologies for programming concepts (e.g., memory diagrams for object-oriented programming), computer systems, and networking. His work includes developing educational tools like interactive animations for network concepts and memory diagram assessments. He also explores performance analysis of parallel architectures and distributed computing. Prior Research Areas: Included grid computing, web services, memory reference traces in parallel systems, and shared memory multiprocessors. His recent projects include the National Storm Surge Database and applications for public access to environmental data. Advising & Grants: While no specific students are listed, Holliday has led curriculum development initiatives and collaborated on projects funded by institutions like the National Oceanic and Atmospheric Administration (NOAA). He has served on the ABET Computing Accreditation Commission since 2023, contributing to accreditation standards. Labs/Teams: His work involves interdisciplinary collaboration, including contributions to the Distributed Systems Group (University of Portsmouth) and the development of the Storm Surge Viewer application.

Eugene ZimaView profile

Associate Professor

• Computer Algebra
• Algorithm Design
• Program Optimization
• +3 more

Eugene Zima is an Associate Professor in the Department of Physics and Computer Science at Wilfrid Laurier University, holding an adjunct appointment as Associate Professor at the University of Waterloo's Department of Computer Science. He also serves as an Editor for the ACM Communications in Computer Algebra. His research focuses on computer algebra, algorithm design, program optimization, closed-form summation, and modular arithmetic. Notable contributions include advancements in symbolic summation techniques, hypergeometric series computation, and parallel algorithm optimization. Zima has co-chaired major conferences like ISSAC and organized workshops such as the Waterloo Workshop on Computer Algebra. Awarded the Best Scientific Result Award at the 1994 PARCELLA Conference, Zima has published extensively in top-tier venues like ISSAC, SNC, and Springer's Lecture Notes in Computer Science. He has authored books on symbolic algorithms and contributed to open-source software projects like the JMCR engine for multidimensional recurrence computations. Zima's teaching spans computer science fundamentals, algorithms, operating systems, and computer networks, with courses taught since 2000. He actively promotes visual learning through initiatives like Visual OS and Visual Algorithms , enhancing educational resources for core computer science topics.

Yuanhao WeiView profile

Assistant Professor

• Parallel Algorithms
• Concurrent Data Structures
• Multicore Computing
• +3 more

Yuanhao Wei is an Assistant Professor in the Department of Computer Science at the University of British Columbia (UBC). Previously, he was a Postdoctoral Researcher at MIT's Computer Science and Artificial Intelligence Laboratory (CSAIL), working with Julian Shun. He earned his Ph.D. from Carnegie Mellon University under Guy Blelloch and completed his undergraduate studies at the University of Toronto, mentored by Faith Ellen and Toniann Pitassi. His research focuses on developing efficient parallel and concurrent algorithms for multicore systems, with a strong emphasis on both theoretical foundations and practical implementations. Education: Ph.D., Carnegie Mellon University (2023) Bachelor's Degree, University of Toronto Research interests include concurrent data structures, memory management (e.g., garbage collection), persistent algorithms, and efficient implementations of concurrency primitives such as atomic operations and locks. His work bridges the gap between theory and practice, aiming to create algorithms that are both theoretically sound and performant in real-world multicore environments. Awards: Best Paper Award at PPoPP’22 for Lock-free Locks: Revisited Principles of Distributed Computing Doctoral Dissertation Award (2023) He currently teaches CPSC 320: Intermediate Algorithm Design and Analysis and is actively recruiting self-motivated MSc/PhD students interested in parallel/concurrent algorithms. His research contributions span topics like multiversioning, concurrent memory reclamation, and efficient NVRAM data structures.

• Subsurface Hydrology
• Reactive Transport Modeling
• Geochemical Processes
• +4 more

Danyang Su is a Researcher at the University of British Columbia (UBC), affiliated with the Department of Earth, Ocean and Atmospheric Sciences within the Faculty of Science. He holds a Ph.D. in Hydraulic Structure Engineering (Zhejiang University, 2012) and a B.E. in Hydrology and Water Resources Engineering (Zhejiang University, 2006). His primary roles include Research Associate (2017–present) and Postdoctoral Fellow (2012–2017) at UBC. Research Focus: Dr. Su specializes in computational modeling of subsurface hydrological processes, reactive transport in porous media/fractured networks, and geochemical repository stability. His work emphasizes large-scale reactive transport modeling via the MIN3P code, including parallelization and unstructured grid implementations. Key contributions include enhancing MIN3P’s capabilities for complex domain simulations and long-term geochemical process understanding. Publications Overview: His recent work (2018–2024) spans topics like CO2 mineral trapping via root systems, ice sheet impact on sedimentary basins, and multibarrier system geochemistry. Articles frequently address environmental challenges such as GHG cycling in agricultural soils and permafrost waste rock management. Technical Contributions: He maintains MIN3P’s codebase, participates in reactive transport benchmarking, and mentors graduate students. His research integrates numerical methods with field applications, addressing climate change impacts and engineered system stability.

• Clinical Trial Process Improvement
• Stroke Research
• Cerebral Venous Thrombosis
• +4 more

Dr. Thalia Shoshana Field is an Associate Professor in the Department of Medicine - Neurology at the University of British Columbia's Faculty of Medicine, where she holds the Sauder Family/Heart and Stroke Foundation Professorship of Stroke Research. She serves as a Stroke Neurologist with the Vancouver Stroke Program based at Vancouver General Hospital, providing specialized care for patients with stroke and cerebrovascular diseases while leading important research initiatives. Dr. Field's research focuses on clinical trials and stroke in younger adults, with particular emphasis on cerebral venous thrombosis (CVT) and adults with congenital heart disease. Her work centers on clinical trial process improvement, including optimizing participant recruitment and retention, efficient resource utilization, and selecting patient-relevant outcomes. She actively investigates how technology can address clinical trial challenges, with specific interests in telemedicine for remote participant assessment and artificial intelligence applications for trial population enrichment, automated participant assessments, and identifying patient-centered outcomes. Analysis of her recent publications reveals a strong focus on cerebral venous thrombosis, stroke treatment protocols, and anticoagulation therapies. Her research spans from developing clinical guidelines to conducting randomized controlled trials examining novel treatment approaches for stroke and related conditions. The publications demonstrate her leadership in advancing evidence-based practices in stroke neurology and her commitment to improving clinical trial methodology. Dr. Field serves as Principal Investigator for the SECRET trial and parallel registry examining treatment strategies for CVT, and leads a national longitudinal study investigating brain health in children and adults with congenital heart disease. She is actively involved in multiple clinical trials including studies on colchicine for vascular event prevention, NA-1 for endovascular thrombectomy patients, TNK-tPA for minor ischemic stroke, and rivaroxaban for secondary stroke prevention. As part of the Vancouver Stroke Program, Dr. Field contributes to educating medical trainees at all levels, from medical students to fellows, who rotate through the VGH Stroke Service and Stroke Prevention Clinic. The program maintains an active research profile with a focus on investigator-initiated studies and participation in multicenter clinical trials for acute stroke and secondary prevention.

Kamyar KhodamoradiView profile

Assistant Professor

• theoretical computer science
• approximation algorithms
• combinatorial optimization

Kamyar Khodamoradi is an Assistant Professor in the Department of Computer Science at the University of Regina, Faculty of Science. His research focuses on theoretical computer science, particularly approximation algorithms and combinatorial optimization, where he designs algorithms with mathematically provable guarantees for intractable problems. Dr. Khodamoradi earned his Ph.D. from Simon Fraser University and completed postdoctoral appointments at the University of British Columbia and several European institutions. His research explores fundamental challenges in optimization across disciplines like computational geometry, graph theory, and resource allocation. Recent work demonstrates advanced techniques for clustering, scheduling, and network optimization problems. Publications emphasize algorithm design with theoretical rigor, frequently appearing in top-tier computer science venues.

• Data Mining
• Machine Learning
• Natural Language Processing (NLP)
• +5 more

Aijun An is a Professor in the Department of Electrical Engineering & Computer Science at York University's Lassonde School of Engineering. She holds a Ph.D. from the University of Regina (1997) and has extensive academic experience, including postdoctoral work and roles at the University of Waterloo before joining York in 2001. Her research focuses on data mining, machine learning, and NLP, with contributions to pattern mining, parallel deep learning, sentiment analysis, and generative AI. Key projects include outbreak detection systems using social media, ethical AI in humanitarian crises, and adaptive data stream mining. Dr. An has led numerous grants from NSERC, SSHRC, and other agencies, including initiatives like the SMART-ART space exploration project and One Health Modelling for emerging infections. She actively supervises graduate students in both master’s and doctoral programs. Teaching responsibilities include courses on data mining, big data systems, and database management. Recent courses include EECS 4412/6412 (Data Mining), EECS 4415 (Big Data Systems), and EECS 2031 (Software Tools).

Ilias S. KotsireasView profile

Professor

• Computer Algebra
• Combinatorics
• Optimization Algorithms
• +6 more

Dr. Ilias S. Kotsireas is a Professor in the Department of Computer Science and Physics at Wilfrid Laurier University (WLU), Canada. He serves as Director of the CARGO (Computer Algebra Research Group) lab and Editor-in-Chief of the Journal of Algebraic Combinatorics . His research focuses on computational mathematics, combinatorics, optimization algorithms, and symbolic computation. Kotsireas is a Fellow of the Institute of Combinatorics and its Applications (ICA, 2004) and has held leadership roles in organizations like ACM SIGSAM and the ACA Working Group. He maintains adjunct appointments at the Athena Research Center (Greece) and the University of Waterloo, and is a Principal Scientist at ORCCA (Ontario Research Centre for Computer Algebra). His work integrates computer algebra systems with SAT solvers to address complex combinatorial problems, such as Hadamard matrices and Golay pairs. Recent research explores neurosymbolic frameworks, blockchain economics, and machine learning applications in algebraic geometry. Education: Advanced degrees in Mathematics/Computer Science (not explicitly listed, inferred from roles). Research Interests: Computer algebra systems, combinatorial search, algorithm portfolios, cryptographic protocols, and computational neuroscience applications. His contributions span theoretical advancements (e.g., Legendre pair algorithms) and practical tools (e.g., Maple-based software). Kotsireas collaborates internationally, organizing conferences like ACA and publishing in venues such as Journal of Symbolic Computation and SpringerPlus . His lab, CARGO, emphasizes supercomputing and interdisciplinary projects. Awards: Fellow of the Institute of Combinatorics and its Applications (ICA), 2004 Labs & Teams: Leads the CARGO lab and affiliated with ORCCA, focusing on computational algebra and high-performance computing.

• Numerical Analysis
• Scientific Computing
• Domain Decomposition Methods
• +4 more

Conor McCoid is a Research Fellow in Mathematics at McMaster University, specializing in numerical analysis, scientific computing, and computational geometry. His research focuses on domain decomposition methods, spectral collocation techniques, and algorithm development for computational problems such as phase field models and geometric intersections. He holds a PhD from the University of Geneva, where he worked on domain decomposition under Martin Gander, and a Master's from Simon Fraser University in spectral collocation methods with Manfred Trummer. His postdoctoral work at McMaster involves accelerating phase field models for fractures and optimizing Schwarz methods. McCoid's research interests include developing robust algorithms for geometric intersections (e.g., triangles and tetrahedra), improving spectral collocation methods for boundary value problems, and prototyping machine learning algorithms for music analysis (e.g., Mozart classification). His GitHub repository includes code for tetrahedral intersections, adaptive Schwarz methods, and music data processing using tools like music21 and scikit-learn. His publications explore numerical methods for multigrid cycles, nonlinear solvers, and geometric algorithms, often emphasizing robustness and efficiency in computational contexts. Collaborations include work with Blaise Bourdin (McMaster), Felix Kwok (Université Laval), and Martin Gander (Geneva).

Yossiri AdulyasakView profile

Professor

• Supply Chain Analytics
• Optimization under Uncertainty
• Large-Scale Optimization
• +4 more

Yossiri Adulyasak is a Full Professor in the Department of Logistics and Operations Management at HEC Montréal, holding the Canada Research Chair in Supply Chain Analytics. He is also an Adjunct Professor at the University of Montreal's Department of Computer Science and Operations Research and a scientific advisor at IVADO Labs. His research focuses on supply chain optimization, data-driven decision-making, and robust optimization under uncertainty, with applications in retail, transportation, and humanitarian logistics. Education: PhD in Operations and Logistics Management (HEC Montréal), Master of Civil Engineering in Transportation Engineering (Chulalongkorn University). Research Interests : Supply chain analytics, large-scale optimization algorithms, stochastic programming, machine learning integration, and resilient supply chain design. His work emphasizes practical applications in logistics networks, inventory management, and dynamic resource allocation under uncertainty. Recent Articles : Focus on AI-driven retail analytics, robust optimization for lot-sizing and facility location, and humanitarian logistics. Key topics include pandemic response strategies, electric vehicle routing, and drone delivery systems. Awards : Recipient of the 2021 Prix de recherche Chenelière Éducation/Gaëtan Morin for excellence in scientific publications. Supervision : Directed 3 PhD dissertations and over 40 master’s and project supervisions, collaborating with companies like Bombardier, Pratt & Whitney Canada, and JDA. Active in industrial partnerships for supply chain optimization solutions. Lab/Teams : Member of GERAD (Group for Research in Decision Analysis) and IVADO Labs, contributing to cross-disciplinary AI and operations research initiatives.

Sylvain PerronView profile

Professor

• Mathematical Programming
• Global Optimization
• Combinatorial Optimization
• +4 more

Sylvain Perron is a Full Professor at HEC Montréal's Department of Decision Sciences, affiliated with GERAD (Group for Research in Decision Analysis) and IVADO (Institute for Data Valorization). He specializes in mathematical programming, global optimization, and network analysis. His work focuses on developing algorithms for complex systems, including column generation, clustering, and quadratic programming applications. Education: Ph.D. in Engineering Mathematics (École Polytechnique Montréal, 2004) M.Sc. in Modeling and Decision (HEC Montréal, 1998) B.A.A. in Business Administration (HEC Montréal, 1995) Research Interests: Perron's research bridges theoretical and applied optimization, addressing challenges in logistics, telecommunications, and data science. Key areas include community detection in networks, vehicle routing optimization, and energy planning models. Publications: Recent work spans dynamic network analysis, heuristic algorithms for dispersion problems, and energy management systems. His contributions highlight interdisciplinary applications of optimization techniques. Awards: Recipient of multiple grants and awards, including the Bourgoin Grant (2000-2002) and Excellence Awards from HEC Montréal. His research is supported by funding from MITACS, CRSNG, and FQRNT. Grants: Projects include trajectory optimization for aviation, supply chain management, and data mining collaborations. Notable funding includes a $1.028M CRIAQ grant for air trajectory research and a GERAD-sponsored column generation project. Supervision: Advised over 20 graduate students, focusing on optimization, network analysis, and algorithm development. Current and past students include leaders in logistics, telecommunications, and academic research. Labs/Teams: Active in GERAD's optimization group and IVADO's data science initiatives, contributing to collaborative research networks across Quebec and internationally.

Jean-François PlanteView profile

Professor

• Statistical Inference
• Statistical Learning
• Sports Analytics
• +3 more

Jean-François Plante is a Professor at the Department of Decision Sciences, HEC Montréal. He holds roles such as Member of the Group for Research in Decision Analysis (GERAD), Professorship in Pedagogical Innovation in Gamification for Data Science, and Associate Director of Pôle sports Education. His research focuses on Statistical Inference, Statistical Learning, Sports Analytics, Applied Artificial Intelligence, and Dependence Models (copulas). He actively supervises Master’s theses and projects, contributing to over 40 academic works in the last five years. Education: B.Sc. in Mathematics from Université Laval, M.Sc. and Ph.D. in Statistics from University of British Columbia. Research interests emphasize statistical methodologies applied to diverse fields including sports analytics, environmental modeling, and machine learning optimization. His recent publications explore topics like spatial interpolation in mining, classification tree capabilities, and migrant labor dynamics in professional sports. He has advised numerous students on projects ranging from fraud detection to AI-enhanced chatbots for autism support. Plante teaches courses such as Statistical Learning and Multidimensional Analysis, reflecting his expertise in statistical education and pedagogical innovation. His work bridges academia and industry through collaborations with organizations like CDPQ and Mouvement Desjardins.