Parallel and Distributed Systems Professors and Supervisors in Canada

Sylvain PerronView profile

Professor

HEC MontréalCanada

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.

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Christopher SwartzView profile

Professor

McMaster UniversityCanada

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.

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Shikharesh MajumdarView profile

Professor

Carleton UniversityCanada

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.

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John C. BowmanView profile

Professor

University of AlbertaCanada

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.

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Wojciech GolabView profile

Professor

University of WaterlooCanada

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.

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Anwar HasanView profile

Professor

University of WaterlooCanada

Anwar Hasan is a Professor in the Department of Electrical and Computer Engineering at the University of Waterloo. He specializes in cryptography, post-quantum cryptography, and secure computation, with a focus on algorithm optimization, cryptographic hardware implementation, and energy efficiency. His work spans theoretical foundations and applied systems, including blockchain technology, fault-tolerant cryptosystems, and secure multiparty computation. His research interests emphasize developing quantum-resistant cryptographic protocols, improving efficiency in cryptographic algorithms (e.g., EdDSA signature verification, CSIDH optimization), and designing secure systems against adversarial attacks (e.g., poisoning in federated learning, covert adversary models). He also explores energy consumption analysis of cryptographic algorithms, particularly in the context of NIST PQC standards and elliptic curve-based systems. Recent work includes contributions to threshold cryptography (isogeny-based schemes), decentralized oracle networks (CountChain), and privacy-preserving blockchain applications (AdChain). His publications frequently address hardware-software co-design for cryptographic primitives, emphasizing both theoretical advancements and practical implementation challenges. Dr. Hasan’s research also extends to applied areas such as fraud detection systems, digital rights management using blockchain, and energy efficiency in embedded cryptographic systems. He collaborates on projects involving secure communication in cloud computing and resilient key management for mobile applications.

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Tamer ÖzsuView profile

Professor

University of WaterlooCanada

Tamer Özsu is a University Professor at the David R. Cheriton School of Computer Science, University of Waterloo, where he previously served as Director (2007-2010) and currently holds the role of Associate Dean of Research. He is also a Distinguished Visiting Professor at Tsinghua University. Professor Özsu's research spans distributed data management, graph/RDF systems, and database fundamentals. His current work focuses on: Distributed graph processing algorithms SPARQL query optimization for RDF systems Streaming graph analytics Indexing techniques for modern hardware Distributed database architectures He has authored the seminal textbook Principles of Distributed Database Systems and co-edited the Encyclopedia of Database Systems . As founding Editor-in-Chief of ACM Books, he has shaped computing literature. His recent publications demonstrate continued innovation in graph partitioning, streaming graph algorithms, and scalable RDF processing. Özsu maintains active research leadership in database systems with over 30 years of influential contributions.

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William MelekView profile

Professor

University of WaterlooCanada

William Melek is a Professor in the Department of Mechanical and Mechatronics Engineering at the University of Waterloo. He serves as Director of the Laboratory of Computational Intelligence and Automation and RoboHub. His research focuses on intelligent systems, modular robotics, autonomous vehicles, and bioinformatics. He holds a B.A.Sc. in Electrical & Computer Engineering (Zagazig University, 1994), M.Sc. and Ph.D. in Mechanical Engineering (University of Toronto, 1998/2002), and a Post-Doctorate in Computer Science (Ryerson University, 2004). Education: Bachelor's: B.A.Sc in Electrical & Computer Engineering, Zagazig University (1994) Master's: Mechanical Engineering, University of Toronto (1998) Doctorate: Mechanical Engineering, University of Toronto (2002) Post-Doctorate: Computer Science, Ryerson University (2004) His research interests span artificial intelligence, intelligent control, mechatronics, automotive design, and computational intelligence. Recent work includes autonomous vehicle navigation, predictive maintenance in manufacturing, and bioinformatics-driven clinical decision support systems. He teaches courses such as ME 360 (Control Systems), ME 780 (Mechatronics), and MTE 100 (Intro to Mechatronics). Articles from 2023–2025 highlight advancements in autonomous driving decision-making, modular robotics, and 5G-enabled teleoperation systems. His lab develops algorithms for urban autonomous systems and industrial automation. He currently oversees graduate student applications in mechatronics and robotics. Labs/Teams: Laboratory of Computational Intelligence and Automation, RoboHub.

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Peter WestView profile

Assistant Professor

University of British ColumbiaCanada

Peter West is an incoming Assistant Professor at the University of British Columbia (UBC) Computer Science Department, specializing in Natural Language Processing (NLP) and AI. His research focuses on understanding the capabilities and limitations of large language models (LLMs) and generative AI systems, emphasizing their divergence from human intuition and alignment challenges. He holds a PhD from the University of Washington (2024), supervised by Yejin Choi, and a BSc (Honours Computer Science) from UBC (2017). His work has been recognized with awards including Best Method Paper at NAACL 2022 and Outstanding Paper awards at ACL 2023 and EMNLP 2023. He conducted internships at the Allen Institute for AI and Microsoft Research’s NLP group. Research interests include analyzing LLM behavior through a natural sciences lens, exploring model capabilities versus human expectations, and developing decoding algorithms to infuse models with algorithmic logic. His recent publications address generative AI paradoxes, constrained text generation, and symbolic knowledge distillation. He serves on panels for NeurIPS workshops and is beginning a postdoc at Stanford with Chris Potts. His research group at UBC seeks students interested in generative AI’s analytical frontiers.

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Jean MahseredjianView profile

Professor

Polytechnic School of MontrealCanada

Jean Mahseredjian is a Full Professor in the Department of Electrical Engineering at Polytechnique Montréal. He holds the NSERC/Hydro-Québec/RTE/EDF/OPAL-RT Industrial Research Chair in Multiscale Time Simulation of Transients in Large-Scale Electrical Networks and is a member of the Institute for Data Valorization (IVADO). His research bridges electrical engineering, numerical methods, and renewable energy integration. Research interests include: Advanced numerical simulation of power system transients Modeling and validation of wind farms and HVDC systems Electromagnetic compatibility and circuit theory Real-time simulation and parallel computing for large-scale grids His recent publications (2025-2026) demonstrate strong emphases on: EMTP simulation advancements for circuit breakers, cables, and converters Renewable integration challenges (wind/PV parks and battery systems) High-performance computing methods for power system analysis Awards and honors: IEEE Fellow (2013) for contributions to power system transients simulation Supervision and collaboration: Mentored 68 graduate students (31 PhD, 37 Master's) Leads industry-funded research with Hydro-Québec, RTE, EDF, and OPAL-RT Develops open-source simulation tools (e.g., Julia-based platforms)

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