Find Academic Supervisors

• Formal Methods
• Relational Algebra
• Term Graph Rewriting
• +4 more

Professor Wolfram Kahl is a faculty member in the Department of Computing and Software at McMaster University . He has served as an instructor for courses such as Logical Reasoning for Computer Science , Formal Specification Techniques , and Certified Programming with Dependent Types , reflecting his expertise in formal methods and educational technologies. Research Interests : His work focuses on Relational algebraic approaches to software verification Term graph rewriting systems Haskell-based tool development for formal specification Category theory applications in computer science Interactive learning tools for discrete mathematics Parallel and distributed system modeling Publication Trends : Over the last decade, his publications have concentrated on formal verification techniques, graph transformation systems, and educational tool development. Notable works include mechanized relational algebraic proofs, parallel multicore verification frameworks, and contributions to Haskell module design for type class systems. Collaborations : Professor Kahl has collaborated extensively with researchers like Christopher Anand, Jacques Carette, and Alan Wassyng, and has contributed to major conferences including Relational and Algebraic Methods in Computer Science (RAMiCS) .

Dvira SegalView profile

Associate Professor

• Chemical Physics
• Quantum Dynamics
• Quantum Thermodynamics
• +3 more

Dvira Segal is an Associate Professor in the Department of Chemistry at the University of Toronto with a cross-appointment in the Department of Physics. They hold the Canada Research Chair in Theoretical Chemistry (2014-2024) and are a Distinguished Fellow at the Hebrew University of Jerusalem's Faculty of Sciences (2024-2026). Their research focuses on quantum dynamics, quantum thermodynamics, and nonequilibrium systems, particularly in nanoscale quantum devices and molecular-level electronics. Research interests include theoretical and computational studies of dissipative quantum systems, heat and charge transfer in molecular junctions, and quantum control strategies. Key areas involve spin dynamics in non-equilibrium systems, quantum thermal machines, and energy management at the nanoscale. Notable awards include the Canada Research Chair and Distinguished Fellowship. Segal leads the Centre for Quantum Information and Quantum Control and is part of the Chemical Physics Theory Group. Their work bridges quantum mechanics, statistical mechanics, and nanotechnology, with applications in quantum computing and energy science. Current and past students and postdocs include over 30 researchers, many now in academic and industry roles. Segal teaches courses on quantum mechanics, statistical mechanics, and physical chemistry, emphasizing graduate-level theoretical training.

Bogdan SimionView profile

Associate Professor

• Computer Science Education
• Distributed Processing
• Parallel Computing

Bogdan Simion is an Associate Professor (Teaching Stream) in the Department of Mathematical and Computational Sciences at the University of Toronto. His research focuses on Computer Science Education, Distributed Processing, and Parallel Computing. He can be reached at bogdan.simion@utoronto.ca or via his website https://www.cs.toronto.edu/~bogdan/ . His work emphasizes pedagogical approaches in computer science and advancing computational methodologies in distributed systems. No specific grants, awards, or advising activities are explicitly listed in the provided text.

Faith EllenView profile

Professor

• Distributed Computing
• Algorithms
• Computational Complexity
• +3 more

Professor Faith Ellen is a faculty member in the Department of Computer Science at the University of Toronto, where she holds the rank of Professor. Her research focuses on the theory of distributed computing, emphasizing lower bounds on computational complexity and the study of data structures in both distributed and sequential models. She has authored numerous publications in leading journals and conferences, contributing significantly to the fields of algorithms, computational complexity, and distributed systems. Her work often explores the interplay between model parameters and computational efficiency, with an emphasis on foundational theoretical insights. Her research interests include the design and analysis of concurrent data structures, synchronization algorithms, and the theoretical underpinnings of distributed systems. Key contributions include studies on adopt-commit objects, snapshot implementations, and non-blocking data structures. Ellen's work has been published in venues such as the Journal of the ACM , Distributed Computing , and Proceedings of PODC and SPAA . While her research has no explicitly listed awards, her prolific publication record underscores her academic impact. She has advised multiple collaborative projects but no specific student names are provided in the text. Ellen’s academic profile reflects a sustained engagement with core theoretical challenges in computer science, particularly in concurrency and distributed computing.

Maryam Mehri DehnaviView profile

Associate Professor

• High-Performance Computing
• Cloud Computing
• Machine Learning
• +3 more

Maryam Mehri Dehnavi is an Associate Professor of Computer Science at the University of Toronto and a Principal Research Scientist at NVIDIA. She holds the Canada Research Chair in Parallel and Distributed Computing. Her research group, ParaMathics, focuses on scalable numerical methods, high-performance libraries, and compilers for cloud and parallel computing platforms. Her research interests span cloud computing, machine learning, numerical analysis, and compiler optimization. She has been recognized with awards including the Ontario Early Researcher Award (2021) and an NSF CRII grant. She has advised numerous students, many of whom have achieved notable academic and industry roles. Teaching: Applied Parallel Computing, Parallel Computing, Cloud Computing Leadership Roles: General Chair of PPOPP 2023, Keynote Speaker at SIAM PP 2024 Labs/Teams: ParaMathics Research Group Her work bridges theoretical advances in numerical methods with practical applications in high-performance computing, emphasizing efficiency and scalability across diverse architectures.

Nandita VijaykumarView profile

Assistant Professor

• Computer Architecture
• Compilers
• Systems
• +5 more

Nandita Vijaykumar is an Assistant Professor in the Computer Science Department at the University of Toronto and the Department of Computer and Mathematical Sciences at the University of Toronto Scarborough. She leads the embARC Research Group, affiliated with the Vector Institute and Robotics Institute. Her work focuses on computer architecture, compilers, and systems, with a particular emphasis on the interplay between programming models, systems, and architectures. She holds a Ph.D. and M.S. from Carnegie Mellon University (2019), advised by Prof. Onur Mutlu and Prof. Phil Gibbons. She has held roles at Intel Labs, AMD, Microsoft, and Nvidia, and was a visiting student at ETH Zurich's Systems Group. Her research bridges computer architecture/systems with robotics, computer vision, and deep learning. Key projects include optimizing GPU algorithms, differential privacy in hardware, and 3D neural graphics. She mentors over 30 graduate and undergraduate students across Ph.D., MSc, and internship programs, fostering innovation in distributed systems and machine learning infrastructure. Nandita’s recent publications address challenges in differentiable rendering, distributed robotics, and energy-efficient computing. Her work on frameworks like DPWatch and DiffusionRenderer highlights her contributions to privacy-aware hardware and neural rendering. Collaborations with industry and academic institutions underscore her commitment to practical, cross-disciplinary solutions.

Mark C. JeffreyView profile

Assistant Professor

• Computer Architecture
• Parallel Computing
• Computer Systems
• +3 more

Mark C. Jeffrey is an Assistant Professor in the Edward S. Rogers Sr. Department of Electrical and Computer Engineering at the University of Toronto, cross-appointed to the Department of Computer Science. His research focuses on computer architecture and systems, aiming to simplify parallel programming for software engineers through novel compilers and large-scale multicore architectures. Previously, he was a Research Scientist at Facebook AI Research and earned his PhD from MIT under Daniel Sanchez. He holds a MASc and BASc from the University of Toronto. Education: PhD in Computer Science, MIT (2019) MASc in Computer Engineering, University of Toronto (2011) BASc in Engineering Science, University of Toronto (2009) Research interests include parallel computing, compilers, multicore architectures, and energy-efficient systems. His work bridges hardware-software co-design to enable scalable parallelism in modern systems. Notable awards include the George M. Sprowls PhD Thesis Award (2nd place, 2019), Best Paper at NoCArc 2023, and a Best Paper Award at ARC 2010. He teaches advanced courses in computer architecture and parallel programming (ECE1755, ECE552). His GitHub repository bf-intersect explores Bloom filter configurations for address-set disambiguation, tied to his SPAA 2011 and MASc thesis work.

• 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.

• 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.

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).

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.