Parallel Simulation Professors and Supervisors in Canada | ApplySharif

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Gul N. KhanView profile

Professor

Toronto Metropolitan UniversityCanada

Dr. Gul N. Khan is a Professor in the Department of Electrical, Computer and Biomedical Engineering at Toronto Metropolitan University (formerly Ryerson University). He has held academic positions at the University of Saskatchewan, Nanyang Technological University, RMIT University, and Quaid-i-Azam University. His career spans over three decades with a focus on embedded systems , network-on-chip (NoC) , and heterogeneous computing . Education: Ph.D. (Imperial College, 1989), M.Sc. (Syracuse University, 1982), B.Sc. (UET Lahore, 1979) Dr. Khan’s research interests include hardware-software co-design , CPU-GPU systems , fault-tolerant computing , and smart RFID systems . His work has led to over 125 refereed publications and three US patents. His recent publications highlight advancements in GPU auto-tuning , NoC synthesis , and digital time interpolators . Despite being listed in a Google Scholar block with unrelated public health topics, these appear to be errors, as his core expertise remains in computer engineering. Dr. Khan has supervised numerous graduate projects in embedded systems and SoC design . He served as Program Director for Computer Engineering from 2004–2015 and leads the Microsystems Research Lab at Toronto Metropolitan University.

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Yasser MohamedView profile

Professor

University of AlbertaCanada

Yasser Mohamed is a Professor in the Civil and Environmental Engineering Department at the University of Alberta . His academic and professional focus revolves around construction engineering, discrete-event simulation, and process optimization for industrial and tunneling operations. He has also explored knowledge engineering techniques and the application of TRIZ (Theory of Inventive Problem Solving) to construction processes. Email: yaly@ualberta.ca Location: 7-269 Donadeo Innovation Centre For Engineering, Edmonton, AB Courses Taught: CIV E 603 (Construction Informatics), CIV E 606 (Design and Analysis of Construction Operations) His research emphasizes modeling construction processes using discrete-event simulation to optimize performance and develop synthetic environments for construction operations. Recent publications, however, indicate a shift toward power systems, focusing on DC microgrids , grid-forming converters , and renewable energy integration . Scientific Awards: None explicitly mentioned in the provided data. Advising and Grants: No formal advisees listed. A co-applicant on a CRD grant (2007–present) for synthetic environments in construction simulation.

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Anuraag ShrivastavView profile

Professor

The University of WinnipegCanada

Dr. Anuraag Shrivastav is a Professor at the University of Winnipeg's Department of Biology, affiliated with the Richardson College for the Environment and Science Complex. His research focuses on cellular signaling mechanisms linked to cancer development, particularly studying biomarkers and therapeutic targets for colorectal, breast, and prostate cancers. His lab also explores renewable energy materials, including perovskite solar cells and nanomaterials like zinc oxide buckyballs. Research Interests: Dr. Shrivastav investigates oncogenic pathways involving N-myristoyltransferase (NMT), mTOR signaling, and STAT3/AP1 interactions. His work bridges basic science and clinical applications, developing diagnostic tools such as blood-based biomarker tests. Simultaneously, he pioneers advancements in energy materials through computational modeling and experimental synthesis of novel photovoltaic systems. Publications Trends: Over 2023-2025, his articles emphasize translational cancer research—identifying prognostic markers and therapeutic strategies—while maintaining a parallel focus on materials innovation for sustainable energy solutions. This dual focus reflects his interdisciplinary approach to scientific inquiry. Awards: No specific awards mentioned, though his extensive publication record indicates sustained research impact. He currently supervises graduate students and postdocs in both cancer biology and materials science domains. Lab/Team: His laboratory integrates molecular biology, computational modeling, and materials engineering, fostering cross-disciplinary collaboration. Active projects include NMT-based cancer diagnostics and next-generation solar cell development.

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Scott OrmistonView profile

Professor

University of ManitobaCanada

Scott Ormiston is a Professor in the Department of Mechanical Engineering at the University of Manitoba's Price Faculty of Engineering. He holds a B.Sc. (1981), M.A.Sc. (1984), and Ph.D. (1990) in Mechanical Engineering from the University of Manitoba and University of Waterloo. His research focuses on Computational Fluid Dynamics (CFD) , specializing in two-phase flow modeling, heat transfer, and numerical methods. Key areas include falling film condensation/absorption, turbulent flow in tube bundles, and CFD code development for industrial applications. His group develops in-house CFD codes and leverages commercial tools like ANSYS CFX and Fluent. Research emphasizes applications in power generation, refrigeration, and chemical processing industries. Recent work includes supercritical fluid instability analysis, phase change material thermal performance, and desalination systems. He actively seeks M.Sc. and Ph.D. students with strong fluid mechanics, heat transfer, and programming skills. Labs/Teams: His research group focuses on CFD software development and industrial collaboration. Ongoing projects involve modeling heat exchangers, aerothermal systems, and energy-efficient materials.

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J.M. FloryanView profile

Professor

Western UniversityCanada

J.M. Floryan is a Professor in the Department of Mechanical and Materials Engineering at Western University. He holds a Ph.D. from Virginia Tech (1980) and completed postdoctoral work at Northwestern University (1981). His research focuses on fluid mechanics , particularly flow manipulation strategies using surface roughness, heating patterns, and boundary conditions, with applications across laminar-turbulent transition, microchannel transport, and turbulent structures. Education : M.Sc. (Warsaw Technical University, 1974), Ph.D. (Virginia Tech, 1980), Postdoctoral (Northwestern University, 1981) His research interests span hydrodynamic stability, spectral methods, immersed boundary conditions, natural/forced convection, and biomedical flows. Methodologies include spectral algorithms for moving boundary problems and stability analysis of spatially modulated systems. Recent work emphasizes flow control via grooves and heating , drag reduction, and energy-efficient fluid systems. The 15 most recent publications (2022-2020) explore thermally-induced streaks, wall vibration effects, wavenumber lock-in, droplet impact physics, and peristaltic pumping. These studies employ direct numerical simulations , spectral accuracy, and experimental validation, with keywords covering hydrodynamic stability , drag reduction , buoyancy convection , and microfluidics . Scientific accolades include Fellowships from the American Physical Society , ASME , and Canadian Academy of Engineering , alongside the Humboldt Research Prize and Canada Research Chair . He has held visiting appointments at institutions like Technion (Israel), Tokyo Metropolitan University (Japan), and Darmstadt Technical University (Germany). Teaching includes graduate courses on Hydrodynamics Stability , Computational Fluid Mechanics , and seminars on Heat Transfer . He advises PhD/Master’s students like Y. Wang and S. Shadman. Professional roles include President of the Canadian Society for Mechanical Engineering and leadership in international mechanics symposia.

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Francis DawsonView profile

Professor

University of TorontoCanada

Professor Francis Dawson is affiliated with the Department of Electrical and Computer Engineering at the University of Toronto , under the Faculty of Applied Science and Engineering . He has been active in teaching and research since 1988, focusing on energy systems, power converters, and electromagnetic modeling. PhD in Electrical Engineering (University of Toronto, 1988) MASc in Electrical Engineering (University of Toronto, 1985) BASc in Electrical Engineering (University of Toronto, 1982) BSc in Physics (University of Toronto, 1978) His research spans static power converters , energy storage systems , signal processing in power applications, and electromagnetic compatibility . Recent publications focus on fault current limiters , digital phase-locked loops , and plasma physics , with keywords indicating strong ties to Electrical Engineering and Materials Science . Scientific accolades include: IEEE Fellow Myron Zucker Student Design Project 1st Prize (2012) K. Yamamoto Best Student Paper Award (2007) F. Farahmand 3rd Best Paper Prize (2005) He has collaborated on industrial projects in power electronics and contributed to the Power Group at the University of Toronto.

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Pierre L'ÉcuyerView profile

Professor

University of MontrealCanada

Pierre L'Écuyer is a Full Professor in the Department of Computer Science and Operational Research at the Faculty of Arts and Sciences, University of Montreal. He holds a Canada Research Chair in Stochastic Simulation and Optimization and is a member of GERAD and CIRRELT research centers. His office is located at André-Aisenstadt building, room 3361, and he can be reached at 514 343-2143. Dr. L'Écuyer received his Baccalauréat in mathematics, Master's in operational research, and PhD in computer science with focus on operational research, all from the University of Montreal. His academic journey has led him to become one of the world's most influential researchers in stochastic simulation. His research focuses on stochastic systems modeling and simulation, with particular expertise in random number generation, quasi-Monte Carlo methods, simulation efficiency improvement, sensitivity analysis, and optimization. His work has practical applications in call centers, finance, communication systems, and revenue management. Analysis of his recent publications shows strong emphasis on randomized quasi-Monte Carlo methods, variance reduction techniques, and applications to complex optimization problems in service systems. Lifetime Professional Achievement Award from INFORMS (2020) Ranked among the most influential researchers worldwide by Mendeley/Elsevier (2019, 2020) ACM SIGSIM Distinguished Contribution Award (2016) Mercit Award from Canadian Operational Research Society (2014) Distinguished Service Award from INFORMS Simulation Society (2011) Urgel-Archambault Prize from ACFAS (2002) Dr. L'Écuyer has supervised numerous PhD and Master's students working on topics ranging from random number generation to call center optimization. His current research projects, funded by NSERC and other organizations, focus on fundamental tools for stochastic simulation, with applications extending through 2030. He leads the SIMUL research laboratory which has developed several important software tools including SSJ (Stochastic Simulation in Java), TestU01, and RNGStreams.

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Scott NoklebyView profile

Professor

Ontario Tech UniversityCanada

Dr. Scott Nokleby is an Associate Dean, Academic and Professor in the Department of Automotive and Mechatronics Engineering at the University of Ontario Institute of Technology. He holds a PhD in Mechanical Engineering from the University of Victoria (2003), and has over two decades of academic leadership and research experience. His primary roles include academic administration and advancing robotics and mechatronics research. Education: PhD (Mechanical Engineering, UVic, 2003), MASc (Mechanical Engineering, UVic, 1999), BEng (Mechanical Engineering with Co-op, UVic, 1997). Research interests focus on advanced robotics topics including parallel manipulators, mobile-manipulator systems, kinematic redundancy analysis, and autonomous systems. His work emphasizes practical applications such as radiation mapping robots, perching drones, and robotic hazard management. Notable contributions include optimal design methodologies for mechanisms and control systems for human-robot interaction. Publications reflect expertise in robotics systems, mechatronics, and nuclear engineering applications. Recent work explores multi-robot task allocation, autonomous navigation, and advanced control algorithms. Awards: Fellow of ASME (2022) Fellow of CSME (2016) CSME Best Paper Award (2014) UOIT Research Excellence Award (2008) CSME I.W. Smith Award (2007) Teaches graduate courses in advanced robotics, mobile robotic systems, and mechanism design. Active in academic administration, he bridges teaching innovation with engineering education through projects like tablet computing integration in design courses. Labs/Teams: Leads robotics research initiatives focused on autonomous systems development, with collaborations spanning nuclear safety, mining automation, and aerospace applications.

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Rook BridsonView profile

Adjunct Professor

University of British ColumbiaCanada

Rook Bridson is an Adjunct Professor in the Department of Computer Science at the University of British Columbia (UBC), affiliated with the Imager and SCL labs. He holds a PhD from Stanford University (2003) and degrees from the University of Waterloo (MMATH '99, BMATH '98). His research focuses on fluid simulation, physical effects in computer graphics, and computational physics, with applications in visual effects and animation. Bridson has contributed to industry through co-founding Exotic Matter AB, creators of the Naiad fluid simulation software acquired by Autodesk. He has been recognized with an Oscar Technical Achievement Award and multiple teaching awards at UBC. Education: PhD in Computer Science, Stanford University, 2003 Master of Mathematics (MMATH), University of Waterloo, 1999 Bachelor of Mathematics (BMATH), University of Waterloo, 1998 Research Interests: Bridson’s work spans fluid dynamics, computational physics, and their applications in film and animation. His research emphasizes realistic simulation techniques, including fluid animation with explicit surface meshes, particle-based methods, and numerical algorithms for handling complex physical phenomena. He has developed influential tools like Naiad, used in major films such as Avatar and Harry Potter . Industry Contributions: Co-founder of Exotic Matter AB (developer of Naiad) Senior Principal Research Scientist at Autodesk Contributions to studios like Weta Digital and Double Negative VFX Grants & Awards: Oscar Technical Achievement Award (Academy of Motion Picture Arts and Sciences) 2012 and 2008 CS Department Teaching Awards (UBC) Best Technical Paper Award (ACM SIGGRAPH/Eurographics Symposium on Computer Animation) Labs & Teams: Active in UBC’s Imager Lab and SCL Lab, focusing on fluid simulation, geometric modeling, and real-time animation techniques. Collaborates with industry partners on advancing visual effects technology.

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Steven RuuthView profile

Professor

Simon Fraser UniversityCanada

Steven Ruuth is a Professor in the Department of Mathematics at Simon Fraser University (SFU), within the Faculty of Science. He specializes in numerical methods for partial differential equations (PDEs), particularly those involving complex geometries and discontinuous solutions. His work bridges applied mathematics and computational science, focusing on interface dynamics, geometric numerical methods, and efficient algorithm development. Ph.D. in Applied Mathematics, University of British Columbia (1996) NSERC Postdoctoral Fellow and Visiting Assistant Professor at UCLA (1996–1999) His research emphasizes robust numerical techniques for evolving networks of interfaces, implicit-explicit time integration methods, and the closest point method for PDEs on manifolds. Applications span computational fluid dynamics, materials science, and computer vision. He has contributed to advancements in meshfree methods, RBF-FD discretizations, and parallel domain decomposition algorithms. Recipient of the Germund Dahlquist Prize (2011) and CAIMS Research Prize (2020) , he is also an editorial board member for SIAM Journal on Scientific Computing and Numerical Mathematics: Theory, Methods and Applications. Teaching responsibilities include courses such as MATH 260 (Introduction to Ordinary Differential Equations). His research group actively explores cutting-edge computational methods with applications to geometry processing and scientific computing challenges.

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Robert BergevinView profile

Professor

Laval UniversityCanada

Robert Bergevin is a Full Professor in the Department of Electrical Engineering and Computer Engineering at Laval University's Faculty of Science and Engineering, where he has been employed since 1990 and achieved full professor status in 2001. He is also a member of CeRVIM (Research Center in Robotics, Vision and Machine Intelligence) and actively participates in graduate recruitment. Dr. Bergevin's educational background includes: Ph.D. in Electrical Engineering from McGill University (1985-1990), with thesis titled "Primal Access Recognition of Visual Objects" under Professor Martin D. Levine M.Sc.A. in Biomedical Engineering from École Polytechnique de Montréal (1982-1984), with thesis on "Modeling and numerical simulation of a nuclear magnetic resonance imaging system" under Professor Robert Guardo B.Sc.A. in Electrical Engineering (Communications specialty) from École Polytechnique de Montréal (1978-1982) Professor Bergevin's research spans cognitive computer vision, pattern recognition, and information systems design methodologies. His work is guided by a unique methodology that progresses "from the general to the particular" to achieve "simply communicable and universally applicable understanding." He has been a researcher in cognitive computer vision since 1985, with particular focus on ontology, methodology, and categorization. His research interests include Ontology of Cognitive Digital Vision, Cognitive Digital Vision Development Methodology, Categorization in cognitive digital vision, Analysis and understanding of images and videos, and Understandable artificial intelligence. As a generalist, he is also a proponent of the science of global anticipatory design (R. Buckminster Fuller) and general semantics (Alfred Korzybski). Analysis of Professor Bergevin's recent publications reveals a strong focus on video anomaly detection, carried object detection, and human activity recognition. His work consistently applies cognitive principles to computer vision problems, often developing novel methodologies for segmentation, tracking, and recognition. The research shows progression from foundational work on image analysis to more complex spatio-temporal understanding of video content, with increasing integration of deep learning techniques in recent years while maintaining a focus on interpretable and cognitively-inspired approaches. Among his professional recognitions: Teaching Star, Faculty of Science and Engineering (2019, 2012) Professor Bergevin has supervised numerous graduate students throughout his career, including four PhD candidates and four Master's students in recent years. His current research includes the "Cyber-physical systems and materialized machine intelligence" project funded by Université Laval, École de technologie supérieure, and Fonds de recherche du Québec - Nature and technologies, running from 2019 to 2026. He was also the director of the bachelor's program in computer engineering from 2001 to 2010 and served as Area Editor for the journal Computer Vision and Image Understanding from 2002 to 2017. As a member of CeRVIM (Research Center in Robotics, Vision and Machine Intelligence), Professor Bergevin collaborates with researchers across multiple disciplines to advance the fields of robotics, computer vision, and machine intelligence. His work bridges theoretical foundations with practical applications, particularly in the analysis of human activities and object recognition in complex visual scenes.

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Athula Dayanart RajapakseView profile

Professor

University of ManitobaCanada

Dr. Athula Rajapakse is a Professor in the Department of Electrical and Computer Engineering at the Price Faculty of Engineering, University of Manitoba, Canada. He leads the Intelligent Power Grid Laboratory and holds Fellow status with Engineers Canada, alongside senior membership in IEEE. His research addresses critical challenges in modern power systems, particularly renewable energy integration and grid resilience. His academic foundation includes: B.Sc. (Eng) First Class Honours in Electrical Engineering from the University of Moratuwa, Sri Lanka (1990) M.Eng. in Energy Technology from the Asian Institute of Technology, Bangkok, Thailand (1993) Ph.D. in Quantum Engineering and Systems Science from the University of Tokyo, Japan (1998) Dr. Rajapakse's research centers on power system protection in evolving grid environments, with specialized expertise in transient-based protection schemes , HVDC grid security , and machine learning applications for fault detection. His work bridges theoretical innovation and industry implementation through synchrophasor technology and wide-area monitoring systems, addressing vulnerabilities introduced by inverter-based renewable resources. Analysis of his 2023-2025 publications reveals three dominant thrusts: (1) Machine learning-driven voltage stability prediction using real-time monitoring, (2) Novel protection architectures for HVDC grids and microgrids requiring sub-cycle fault clearance, and (3) Standardized electro-thermal designs for scalable DC circuit breakers in medium-voltage networks. These reflect the industry's urgent need for adaptive protection in grids with high renewable penetration. His recognition includes: Fellow of Engineers, Canada Dr. Rajapakse serves as Associate Editor for the International Journal of Electric Power and Energy Systems (Elsevier) and Technology and Economics of Smart Grids and Sustainable Energy (Springer), while convening the Cigre/IEEE Joint Working Group C4/C2.62 on synchrophasor applications. Though not accepting new graduate students currently, his editorial leadership and working group contributions significantly shape power systems protection standards. His research has secured industry partnerships focused on real-world grid modernization. The Intelligent Power Grid Laboratory under his direction develops hardware-in-the-loop validation platforms for protection schemes, collaborating with Manitoba Hydro and international grid operators to test solutions for renewable-rich systems and remote microgrids.

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Martin KarstenView profile

Professor

University of WaterlooCanada

Martin Karsten is a Professor at the David R. Cheriton School of Computer Science, University of Waterloo, where he leads research in software systems and networking. His work focuses on finding simple approaches to building robust and efficient systems infrastructure. He teaches courses including Real-time Programming, Operating Systems, Distributed Systems, and Computer Networks. His research explores fundamental patterns for software infrastructure design and redesigning systems using modular building blocks. Primary interests include system-level runtime systems (OS kernels, hypervisors), performance optimization through simplicity, and structural commonalities across software layers. Key research areas encompass system software, network architecture, network services, and network software. Karsten holds a Diplom-Wirtschaftsinformatiker from Universität Mannheim and Dr.-Ing. from TU Darmstadt. His career includes positions as Assistant Professor (2002-2007), Associate Professor (2007-2020), and Professor (2021-present) at Waterloo, with administrative roles including Associate Director of the School since 2020. He maintains active collaborations through sabbaticals at SAP and visiting positions at TU Kaiserslautern. He currently advises multiple graduate students in the MMath program and has contributed to open-source projects like libfibre (user-level threading) and KOS (experimental OS kernel). His professional service includes roles in academic integrity initiatives and conference organization.

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Larry Yueli ZhangView profile

Assistant Professor

York UniversityCanada

Larry Yueli Zhang is an Assistant Professor, Teaching Stream in the Department of Electrical Engineering & Computer Science at York University's Lassonde School of Engineering. He holds a PhD from the University of Toronto's Department of Computer Science and previously served as teaching faculty at the University of Toronto Mississauga's Department of Mathematical and Computational Sciences. His research focuses on social network analysis, information networks, and computing education. He is actively involved in curriculum development for York University's Markham Campus, including the Computer Science for Software Development (CSSD) and Digital Technologies (DT) programs. His teaching spans undergraduate courses in data structures, algorithms, computer organization, and distributed systems. Notable projects include SlideChat (an interactive slide-based discussion platform), Velocity (a course management tool), and OkShell (a human-friendly command-line interface). He has received awards such as the Alexander Graham Bell Canada Graduate Scholarship (2013) and Outstanding Master's Thesis from Tsinghua University. Larry's research emphasizes improving computing education through innovative pedagogical tools and analyzing student learning behaviors. His work includes studies on gamified quizzes, online exam strategies, and breakout room utilization in large courses.

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Olena SyrotkinaView profile

Professor

University of WindsorCanada

Dr. Olena Syrotkina is a Professor at the University of Windsor's School of Computer Science. She holds a Ph.D. in Mathematical Simulation from Ukrainian State University of Science and Technologies and an M.Sc. in Computer Science from Dnipro University of Technology. Her research focuses on SCADA diagnostics, big data analytics, discrete mathematics, and machine learning algorithms. Research Interests: Her work spans computational methods for large-scale data analysis, including machine learning applications in industrial systems and theoretical frameworks for data optimization. Primary areas include: SCADA system reliability and diagnostics Big data processing and resource optimization Machine learning-driven predictive modeling Research Trends: Her recent publications emphasize mathematical optimization in big data contexts, with themes like quantum computing applications, fault detection in complex systems, and e-commerce analytics. Over 80% of her 2020-2024 publications involve scalable algorithms for industrial data. Honors & Advising: No awards are listed. She currently mentors graduate students but no named advisees were specified. Laboratory & Teams: Leads research in computational methods at UWindsor, collaborating with industrial partners on SCADA and data infrastructure projects.

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