Find Academic Supervisors

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.

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.

Christopher SwartzView profile

Professor

• Process Systems Engineering
• Operations Optimization
• Dynamic Optimization
• +5 more

Christopher Swartz is a Professor in the Department of Chemical Engineering at McMaster University and Director of the McMaster Advanced Control Consortium (MACC). He specializes in process systems engineering, with a focus on operations optimization, dynamic modeling, and supply chain optimization. His work integrates advanced control strategies with real-time optimization to enhance industrial process efficiency under dynamic conditions. Education: B.Sc. (Eng.) Chemical Engineering, University of Cape Town Ph.D. Chemical Engineering, University of Wisconsin Professional Licenses: P.Eng. (Ontario), MAIChE, MCSChE Research Interests: Dynamic optimization of transient processes (e.g., electric arc furnaces, air separation units) Integration of design and control for plant operability Supply chain optimization under uncertainty Economic model predictive control and real-time optimization Large-scale dynamic optimization under uncertainty using parallel computing Teaching: Teaches CHEM ENG 3P04 (Process Control), covering transient process behavior, automatic control theory, and computer process control. MACC Leadership: Directs MACC, a research consortium advancing process automation through industry-academia collaboration. MACC focuses on cutting-edge control technologies, data analytics, and sustainable design.

Shikharesh MajumdarView profile

Professor

• Parallel and Distributed Systems
• Performance Modelling
• Cloud Computing
• +5 more

Dr. Shikharesh Majumdar is the Chancellor’s Professor and Director of the Real-Time and Distributed Systems Research Centre at Carleton University's Department of Systems and Computer Engineering. He holds a Ph.D. from the University of Saskatchewan. His research focuses on parallel and distributed systems, performance modeling, cloud computing, and IoT. He teaches undergraduate and graduate courses in operating systems and distributed systems engineering. His work includes pioneering contributions to resource management on clouds and grids, energy-efficient systems, and middleware for distributed computing. He has supervised numerous graduate and undergraduate students, many of whom have received awards such as the University Senate Medal and NSERC scholarships. Dr. Majumdar has authored over 100 peer-reviewed publications and delivered keynotes at major conferences like IEEE Cloud and ACM ICDCN. His research addresses challenges in big data analytics, sensor-based systems, and edge computing. He actively contributes to industry through collaborations with organizations like TELUS and Nortel. His current projects include optimizing resource allocation for MapReduce jobs, enhancing IoT systems, and developing frameworks for complex event processing. The Real-Time and Distributed Systems Research Centre under his leadership explores cutting-edge solutions for distributed computing challenges.

John C. BowmanView profile

Professor

• Statistical closures in turbulence
• Spectral Reduction
• 3D vector graphics
• +10 more

John C. Bowman is a Professor in the Department of Mathematical and Statistical Sciences at the University of Alberta. He has maintained an active teaching career with courses spanning undergraduate to graduate levels, including Honours Calculus (Math 117/118), Real Variables (Math 417), Coding Theory (Math 422), Linear Algebra II (Math 225), and Statistical Theories of Turbulence (Math 655). His office is located in CAB 521, and he has consistently provided regular office hours for student consultation across multiple courses. Dr. Bowman earned his BS in Engineering from the University of Alberta, followed by an MA and PhD from Princeton University. His educational background has informed his diverse research trajectory spanning mathematical physics, computational mathematics, and scientific visualization. His teaching philosophy emphasizes deep conceptual understanding, as evidenced by his development of comprehensive hyperlinked 3D lecture notes for Honours Calculus. His primary research focuses on statistical theories of turbulence, particularly his development of Spectral Reduction as a reduced statistical description of turbulence. His work shows remarkable agreement with full numerical simulations, even in flows containing long-lived coherent structures. Additional research interests include 3D vector graphics (notably the Asymptote language he developed), implicit dealiasing of convolutions, exponential integrators, and exactly conservative integrators for numerical simulations. He has been involved with the University of Alberta's Geophysical Fluid Dynamics Research Group, where his work bridges theoretical mathematics with practical computational applications. Dr. Bowman's publication record demonstrates consistent contributions to computational mathematics and fluid dynamics over several decades. His recent work (2015-2024) shows continued innovation in computational methods for turbulence simulation, with particular attention to dealiasing techniques, efficient convolution algorithms, and novel approaches to spectral methods. His research has practical applications in assessing dissipation mechanisms in large-eddy simulations and modeling high-Reynolds number turbulence. As an educator, Dr. Bowman has developed comprehensive teaching materials and maintains an active engagement with students through office hours and detailed course websites. His commitment to mathematical education extends to high school outreach through contributions to the "Pi in the Sky" mathematics magazine. His work on the Asymptote vector graphics language has created a valuable tool for mathematical visualization and scientific publishing that integrates seamlessly with TeX.

• Algorithms
• Big data analytics
• Blockchain
• +12 more

Dr. Wojciech Golab is a Professor in the Department of Electrical and Computer Engineering at the University of Waterloo. His research focuses on distributed systems, concurrency control, and persistent memory algorithms. He has received recognition for his work on shared memory algorithms from ACM Computing Reviews. His academic career includes teaching courses like ECE 252, ECE 356, and ECE 454 in the past five years. Education: PhD in Computer Science, University of Toronto (2010) M.Math in Computer Science, University of Waterloo (2004) BSc in Computer Science, University of Toronto (2002) Dr. Golab's research interests span algorithms, blockchain, distributed systems, fault tolerance, and scalable storage solutions. His work emphasizes practical applications of theoretical computer science, such as optimizing transaction processing and ensuring consistency in distributed environments. His recent publications explore topics like detectable synchronization objects for persistent memory, blockchain sharding protocols, and efficient mutual exclusion algorithms under system failures. These contributions highlight advancements in both theoretical foundations and real-world distributed system design. Awards: ACM Computing Reviews 'Notable Computing Items' recognition (2012) Dr. Golab has collaborated with industry partners like Hewlett-Packard Labs, Google, and Cisco to advance research in distributed computing and cloud systems. He currently oversees graduate studies in his department and welcomes applications from prospective students.