High-Performance Scientific Computing Professors and Supervisors in Canada | ApplySharif

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جست‌وجوی استاد و پژوهشگر

استاد راهنما، استاد دانشگاه یا پژوهشگر مناسب برای مسیر پژوهشی‌تان را پیدا کنید و با او ارتباط بگیرید.

نمایش ۱۶-۳۰ از ۱۹۱ استاد راهنما

اطلاعات این پروفایل‌ها از صفحات عمومی دانشگاه‌ها جمع‌آوری شده و ممکن است به‌روز نباشد.

Diane Guignardمشاهده پروفایل

استادیار

University of OttawaCanada

Diane Guignard is an Assistant Professor in the Department of Mathematics and Statistics at the University of Ottawa. Her research focuses on numerical analysis, partial differential equations, and computational methods with applications to mechanics and stochastic systems. She holds a position in a leading mathematics department and can be contacted at dguignar@uOttawa.ca . Her research interests include finite element methods, model reduction, uncertainty quantification, and optimal transport-based mesh adaptation. She explores nonlinear approximation theories for high-dimensional anisotropic functions and develops computational frameworks for thin structures and colloidal flow simulations. Her work bridges numerical analysis with practical engineering challenges, emphasizing adaptive algorithms and error estimation techniques. Her recent publications (2021-2024) highlight contributions to goal-oriented mesh adaptation, stochastic field approximations on surfaces, and large deformation analyses of prestrained plates. These studies emphasize interdisciplinary approaches combining mathematical rigor with computational innovation. Dr. Guignard has not been explicitly noted for awards in the provided materials. Her advising record is currently unspecified, though her research group likely engages in advanced numerical methods and computational mechanics projects.

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Yusuf Altintasمشاهده پروفایل

استاد

University of British ColumbiaCanada

Yusuf Altintas is a Professor in the Department of Mechanical Engineering at the University of British Columbia's Faculty of Applied Science, holding the NSERC–P&WC-Sandrik Coromant Industrial Research Chair and coordinating the Mechatronics Option. An internationally acclaimed scholar, he is a Fellow of 10 prestigious academies including the National Academy of Engineering (NAE), Royal Society of Canada (RSC), and ASME. His academic credentials include a Ph.D. from McMaster University, an Honorary Doctor of Engineering from the University of Stuttgart, and a Doctor of Technical Sciences from Budapest University of Technology and Economics. Professor Altintas's research pioneers the integration of physics-based modeling and data-driven approaches for machining systems. His work spans virtual high-performance machining simulation, machine tool dynamics, chatter stability prediction, and intelligent process control for CNC systems. Current projects focus on digital twin development for machining processes, spindle health diagnostics, ultrasonic vibration-assisted tooling, and adaptive damping systems for aerospace manufacturing applications. His methodologies bridge theoretical mechanics with industrial implementation in die/mold and aerospace sectors. Analysis of his 2022-2025 publications reveals dominant trends in physics-informed machine learning for spindle fault detection, topology-optimized tool design, and chatter avoidance in thin-walled component machining. Key thematic clusters include digital twin implementation (28% of recent work), dynamics modeling of multi-axis systems (35%), and intelligent monitoring algorithms (22%), with growing emphasis on anisotropic material machining and 3D printing process control. Georg Schlesinger Award (2016) NSERC Strategic Research Network in Virtual Machining Grant (2016) NSERC Synergy Award (2013) ASME Blackall Machine Tool and Gage Award (2013) Special Distinguished Scientist Award from Turkey's Scientific and Technical Research Council (2013) He directs the Manufacturing Automation Laboratory at UBC, leading an international research consortium on virtual machining systems supported by NSERC and industry partners including Sandvik Coromant and Pratt & Whitney Canada. His team develops real-time process monitoring frameworks and physics-based simulation tools that have been adopted in aerospace manufacturing for blade machining and die/mold production. The laboratory maintains advanced testbeds for five-axis machining dynamics, spindle health monitoring, and ultrasonic vibration-assisted tooling, serving as a hub for industry-academic collaboration in next-generation manufacturing technologies.

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Kay C. Wieseمشاهده پروفایل

استاد

Simon Fraser UniversityCanada

Kay C. Wiese is a Professor and Software Systems Chair at the School of Computing Science, Simon Fraser University. His research focuses on computational intelligence and bioinformatics, particularly RNA secondary structure prediction and visualization. He leads the Bioinformatics Research Lab and has contributed to RNA design and gene finding. Wiese holds a PhD in Computer Science from the University of Regina (1999) and degrees in Computer Science and Mathematics from the Universität des Saarlandes (Germany). He has extensive editorial roles, including Associate Editor for the IEEE/ACM Transactions on Computational Biology and Bioinformatics, and has organized major conferences like the IEEE Symposium on Computational Intelligence in Bioinformatics. His teaching interests include Bioinformatics, Computational Biology, and Discrete Mathematics. Wiese has supervised numerous graduate students, including Boris Shabash, Wenbo Jiang, and Andrew Hendriks. His research group developed tools like jViz.RNA for RNA visualization and SARNA-Predict for structure prediction. His work bridges computational methods with biological applications, emphasizing algorithmic innovation and practical software solutions.

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Tianyu Guanمشاهده پروفایل

استادیار

York UniversityCanada

Tianyu Guan is an Assistant Professor in the Department of Mathematics and Statistics at York University, Faculty of Science. He previously served as an Assistant Professor at Brock University and joined York University in 2024. He holds a PhD in Statistics from Simon Fraser University (2020), an MSc in Actuarial Science from the same institution (2014), and a BSc in Statistics from Jilin University (2011). PhD in Statistics, Simon Fraser University, 2020 MSc in Actuarial Science, Simon Fraser University, 2014 BSc in Statistics, Jilin University, 2011 His research centers on sports analytics, functional data analysis, and nonparametric statistics, with strong applications in machine learning and data science. He applies statistical methodologies to understand sports performance, player behavior, and game dynamics. His work also extends to theoretical developments in sparse modeling and functional regression. The recent publications highlight a clear trend toward integrating advanced statistical techniques with real-world sports and entertainment data. His work combines functional data analysis, machine learning, and probabilistic modeling to extract insights from complex longitudinal and high-dimensional datasets. Topics span soccer, rugby, football, and movie reviews, demonstrating interdisciplinary reach. While no formal scientific awards are listed in the provided text, his publications in high-impact journals such as Annals of Applied Statistics and Statistics and Computing reflect strong academic recognition. Tianyu Guan actively advises multiple graduate students at both MSc and PhD levels, primarily at Brock and Simon Fraser Universities. His teaching portfolio includes advanced courses in nonparametric statistics, sampling theory, and experimental design at the undergraduate and graduate levels. He has not received external grant information in the provided text, but his research output suggests active engagement in funded or independent research projects. He leads methodological and applied research in sports analytics, often co-supervising students with colleagues across institutions. His lab or research group appears focused on developing and applying statistical tools for performance analysis and decision-making in sports, supported by computational implementations such as the R package ngr .

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Hassan Sajjadمشاهده پروفایل

دانشیار

Dalhousie UniversityCanada

Hassan Sajjad is an Associate Professor in the Faculty of Computer Science at Dalhousie University, Halifax, Canada. He is also the Director of HyperMatrix, a research group focused on AI and deep learning. His work centers on Natural Language Processing, Safe and Trustworthy AI, interpretability, and robustness of language models. PhD - University of Stuttgart, Germany (2012) Masters - National University of Computer and Emerging Sciences, Pakistan (2007) BSc - National University of Computer and Emerging Sciences, Pakistan (2005) Hassan Sajjad's research focuses on making AI systems more interpretable, robust, and safe. He investigates how deep learning models, particularly transformers, encode linguistic and conceptual knowledge. His work spans language generation , model editing , interpretability , and generalization . He develops tools like NeuroX and NxPlain to analyze neuron-level behavior in NLP models. His research also extends to crisis informatics and multilingual NLP, especially Arabic and Urdu. The recent publications show a strong trend in analyzing and interpreting deep NLP models, with a focus on neuron interpretation , latent concept discovery , and model robustness . His work appears in top-tier venues like NeurIPS, ICLR, ACL, and EMNLP, indicating high impact in the AI and NLP communities. There is a clear emphasis on developing practical tools and frameworks for model analysis. No formal scientific awards are mentioned in the provided text. Hassan Sajjad mentors students and has fellowship opportunities available. While specific grants are not listed, his extensive publication record and leadership roles suggest active research funding. He advises students in AI, NLP, and deep learning, and collaborates widely across institutions. He leads the HyperMatrix research group at Dalhousie University, which focuses on AI, deep learning, and NLP. The group develops tools for model interpretation and works on safe and trustworthy AI. Collaborations extend to institutions like MBZUAI, NYU Abu Dhabi, and various international research centers.

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Aaron Shugarمشاهده پروفایل

استاد

Queen's University at KingstonCanada

Aaron Shugar is a Professor and current Bader Chair in Art Conservation at Queen’s University. With a background in archaeometallurgy and conservation science, he specializes in non-destructive analysis techniques for cultural heritage, including X-ray fluorescence (XRF), Raman spectroscopy, and hyperspectral imaging. His work bridges art history, material degradation, and technological innovation. Honours H.B.A. in Anthropology and Law & Society from York University M.S. in Archaeological Materials from the University of Sheffield Ph.D. in Archaeometallurgy from University College London His research focuses on historic artist’s pigments , ancient metallurgy , and technical history of artifacts , with particular interest in degradation pathways and manufacturing processes. Recent publications highlight trends in AI integration with XRF analysis and preservation of modern materials in art conservation. Bader Chair in Art Conservation Mellon Foundation Professor in Conservation Science Aaron co-directed the Archaeometallurgy Laboratory at Lehigh University, served as a guest scientist at NIST, and remains a research associate at the Smithsonian Institution. He actively contributes to TEFAF’s Scientific Vetting Committee and acts as a forensic materials expert for the Court of Arbitration for Art.

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Gary Grewalمشاهده پروفایل

دانشیار

University of GuelphCanada

Gary Grewal is an Associate Professor at the School of Computer Science , University of Guelph. His research focuses on developing intelligent Computer-Aided Design (CAD) tools for Field Programmable Gate Arrays (FPGAs) , integrating classical optimization techniques with machine learning and deep learning to address challenges in placement and routing for heterogeneous devices. He has received the Michal Servit Award (2017, 2018) for outstanding FPGA research and the University of Guelph Faculty Association Distinguished Professor Award for Excellence in Teaching (2017) . Grewal has held NSERC Discovery Grants annually from 1999 to 2023. Co-founder of the Guelph FPGA CAD Group Key collaborator with institutions like Ryerson University , University of Toronto , and University of British Columbia His work extends to health technology through the IronTracker mobile app , developed with Andrew Hamilton-Wright and students (A. D'Angelo, J. Carter, F. Liu, R. Pattison) to manage Hereditary Hemochromatosis (HHC) . The app, available in four languages and adopted in 100+ countries, was recognized at Parliament Hill and the Ontario Legislature. Scientific Awards : Michal Servit Award (2018) Michal Servit Award (2017) Distinguished Professor Award for Teaching (2017) NSERC Discovery Grants (1999-2023) His recent publications highlight trends in machine learning for FPGA CAD , including reinforcement learning for partitioning, deep learning for congestion estimation, and adaptive algorithms for placement. Grewal remains active in teaching courses like Discrete Optimization (CIS*6070) and Digital Systems I (CIS*3120).

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David Del Rey Fernándezمشاهده پروفایل

استادیار

University of WaterlooCanada

Numerical Simulation of PDEs
Summation-by-Parts Methods
Continuous and Discontinuous Galerkin Schemes
+۲ مورد دیگر

David Del Rey Fernández is Assistant Professor and Pratt & Whitney Canada Chair in Industrial Artificial Intelligence in the Department of Applied Mathematics at University of Waterloo. His research develops efficient numerical algorithms for solving partial differential equations on high-performance systems. He holds a PhD from University of Toronto and previously worked at NASA Langley Research Center. Research focuses on robust numerical methods, mesh adaptation, and machine learning acceleration. His work includes entropy-stable schemes, summation-by-parts methods, and discretizations for compressible flows. Recent publications address Lyapunov-consistent discretizations and scalable reduced-order modeling.

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Albert Yoonمشاهده پروفایل

استاد

University of TorontoCanada

Albert Yoon is a Professor and holds the Michael J. Trebilcock Chair in Law and Economics at the University of Toronto Faculty of Law. He previously served as Associate Dean (Research & Curriculum) from 2018–2020 and has held academic positions at Northwestern University. His research focuses on labor markets in legal professions, legal ethics, and applications of AI to law. He co-founded Blue J, an AI startup aiding tax and legal professionals. Education: BA from Yale, JD and PhD (Political Science) from Stanford. Professional experience includes clerkship at the U.S. Court of Appeals for the Sixth Circuit. Fellowships include the Pierre Elliott Trudeau Fellowship (2022), Princeton University, and Robert Wood Johnson Foundation. Research interests span legal economics, judicial behavior, and technology's impact on law. Notable awards include the Ronald H. Coase Prize and American Law Institute membership. His work bridges empirical legal studies and computational methods, addressing topics like Supreme Court clerkships, AI-driven legal predictions, and tax law analytics. Publications include over 47 scholarly articles in top journals like Chicago Law Review , Stanford Law Review , and Journal of Law & Economics . Recent work examines AI in legal practice, gender disparities in M&A legal teams, and judicial decision-making dynamics.

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Ksenia Dolgalevaمشاهده پروفایل

دانشیار

University of OttawaCanada

Ksenia Dolgaleva is an Associate Professor at the University of Ottawa's School of Electrical Engineering and Computer Science, and holds a Canada Research Chair in Integrated Photonics (Tier 2). She joined the university in July 2013 as part of the Canada Excellence Research Chair team led by Professor Robert Boyd. Education: She earned a Diploma in Physics from Moscow State University (Russia) and a Ph.D. in Optics from the University of Rochester (USA, 2009). She completed a postdoctoral fellowship at the University of Toronto (2009–2013), supported by a Mitacs Elevate fellowship (2011–2012). Research Interests: Her work focuses on integrated photonics, nonlinear optics, THz technology, and advanced optical materials. Key areas include artificial photonic structures, nonlinear responses in materials like InP, GaN, and quartz, and plasmonic metamaterials for sensing and frequency conversion. Publications & Trends: Over 20 peer-reviewed articles highlight her contributions to nonlinear photonics platforms, THz spectroscopy, and material characterization. Recent work emphasizes phase-matched processes, ultra-high-Q resonances, and THz plasmonic metasurfaces. Scientific Awards: Outstanding undergraduate thesis award (Russian Physical Society, 2009) Outstanding student presentation (OSA Frontiers in Optics, 2008) Advising & Grants: Trained 8 students during her Ph.D. and postdoc. Currently building her research group to explore optics and photonics. Labs/Teams: Active in the Canada Excellence Research Chair team and collaborates on projects involving III-V semiconductors and THz technologies.

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Dongyang Liمشاهده پروفایل

استاد

University of AlbertaCanada

Dongyang Li is a Professor in the Department of Chemical and Materials Engineering at the University of Alberta. He also serves as an Adjunct Professor in Biomedical Engineering at the same institution and holds adjunct positions at several international universities. B.Sc. in Solid Mechanics, University of Science and Technology China M.Sc. in Condensed Matter Physics, Sun Yat-sen University PhD in Materials Physics, University of Science and Technology Beijing PhD in Metallurgical Engineering, McGill University Dr. Li's research focuses on the electronic origin of material properties , material design , and computational materials science , with applications in surface science , wear resistance , and corrosion mitigation . His work integrates experimental and computational approaches to develop high-performance industrial materials and functional/bio-materials, as reflected in his keywords: Materials Physics, Surfaces & Interfaces, Wear, Corrosion, Tribo-materials, Nanostructured Materials, and Computational Materials Science. With over 500 scientific publications and 460+ refereed journal articles, Dr. Li has made significant contributions to wear and tribology through invited handbook chapters (Elsevier, Springer, ASM International). His accolades include the Metal Physics Award (MetSoc-CIM, 2024), Research Excellence Award (CIM, 2024), and the 2020 Distinguished Materials Scientist Award . Member of editorial boards for 18 technical journals Research team applies novel methodologies to material design and modification

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Julian Lowmanمشاهده پروفایل

استاد

University of TorontoCanada

Julian Lowman is a Professor at the University of Toronto Scarborough (UTSC), specializing in planetary interiors, mantle convection, and computational fluid dynamics. His research focuses on understanding the thermal and structural evolution of planetary mantles, core-mantle interactions, and high-performance numerical modeling techniques. He holds a Ph.D. from York University (1996) and contributes to advancing geodynamic simulations for terrestrial planets and moons. Key research interests include the mechanics of mantle convection, the role of viscosity and compositional variations, and the application of high-performance computing to model planetary processes. He has explored topics such as stagnant-lid convection, plate tectonic dynamics, and the thermal evolution of planetary cores and mantles. His work bridges computational methods with geophysical observations to address questions in Earth science and planetary science. Lowman’s publications span over three decades, addressing topics from Mercury’s mantle dynamics to exoplanet tectonics. His methodologies include advanced numerical models that simulate 2D and 3D convection patterns, fluid dynamics under Arrhenius viscosity regimes, and the influence of curvature on planetary interiors. He collaborates on projects involving mantle plumes, supercontinent cycles, and the interplay between surface tectonics and deep mantle structure. Despite his extensive contributions, no specific scientific awards or grants are explicitly listed in the provided texts. He advises no named students in the available data but likely contributes to graduate training at UTSC. His research aligns with interdisciplinary themes in computational geosciences and planetary evolution.

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Marc Parizeauمشاهده پروفایل

استاد

Laval UniversityCanada

Marc Parizeau is a Professor at Université Laval, affiliated with the Faculty of Science and Engineering and the Department of Electrical and Computer Engineering. His office is located at PLT-1138-B, and he can be reached at (418) 656-2131 ext. 407912 or via email at marc.parizeau@gel.ulaval.ca. Academic Background: Ph.D., École Polytechnique de Montréal, 1992 M.Sc.A., École Polytechnique de Montréal, 1987 B.Eng., École Polytechnique de Montréal, 1984 His research focuses on Pattern Recognition, Evolutionary Computation, Neural Networks, 2D and 3D Computer Vision, and parallel and distributed systems . He integrates these areas to develop scalable computational models and tools for intelligent systems. His work bridges theoretical AI with practical software engineering for high-performance environments. His teaching includes courses such as Programmation parallèle et distribuée (GIF-4104) , Réseaux de neurones (GIF-21410) , and various algorithm and programming courses in Python and engineering. His recent publications reflect a strong trend in distributed evolutionary algorithms and concurrent programming frameworks, particularly using Python-based tools like DEAP and SCOOP, emphasizing scalability and real-world deployment. Scientific Leadership and Software Contributions: Director, Calcul Québec Creator, Distributed Evolutionary Algorithms in Python (DEAP) Creator, Scalable Concurrent Operations in Python (SCOOP) Contributor, Portable Agile Classes in C++ (PACC) Contributor, Open Beagle Marc Parizeau has supervised multiple collaborative projects and students, though specific names are not listed. He has secured research support through leadership roles and software development. His work is supported by institutional and provincial computing infrastructure initiatives. Conference Involvement: Organizing Committee, High Performance Computing Symposium (HPCS'08) International Workshop on Frontiers in Handwriting Recognition (IWFHR'02) International Conference on Pattern Recognition (ICPR'02) Vision Interface (VI'99) Conférence Internationale sur l'Écrit et le Document (CIFED'98) He leads the Computer Vision and Systems Laboratory at Université Laval, a research group focused on intelligent systems, machine learning, and high-performance computing applications in vision and optimization.

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Ayesha Aliمشاهده پروفایل

استاد

University of GuelphCanada

Ayesha Ali is a Professor of Statistics and Director of the Master of Data Science program at the University of Guelph. She holds a PhD in Statistics from the University of Washington (2002) and has expertise in statistical methods for complex high-dimensional systems, including ecological networks, causal inference, and bioinformatics. Her research integrates graphical Markov models, machine learning, and statistical computing to address challenges in plant-pollinator networks, livestock genetics, and disease risk modeling. Education: B.Sc. Honours in Statistics and Actuarial Science, University of Western Ontario (1996) M.Sc. in Statistics, University of Toronto (1998) Ph.D. in Statistics, University of Washington (2002) Research Interests: Graphical Markov models and ecological networks Causal inference and longitudinal data analysis Machine learning and high-dimensional predictive modeling Statistical methods for livestock genetics and animal health Computational statistics and bioinformatics Articles Trends: Her recent work spans interdisciplinary applications, including veterinary oncology biomarker discovery, remote sensing for agricultural suitability, and pipeline development for cross-species transcriptomics. She emphasizes graphical structure exploitation in regression and predictive modeling, with contributions to both theoretical and applied statistical methodologies. Awards: Canadian Journal of Statistics Award (2020) for groundbreaking work on doubly sparse regression NSERC Discovery Grant (2018) NSERC Collaborative Research and Development Grant (2015) Advising & Grants: She has supervised numerous graduate and undergraduate students on projects ranging from plant-pollinator network analysis to bioinformatics. Her grants include NSERC-funded research on milk fatty acid genetics and statistical methods for clustered data. Labs/Teams: Involved in the Bioinformatics program at the University of Guelph, contributing to interdisciplinary research collaborations in ecology and animal science.

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Kenneth R. Jacksonمشاهده پروفایل

استاد

University of TorontoCanada

Kenneth R. Jackson is a Full Professor of Computer Science at the University of Toronto, where he has been active since 1981. He retired in 2020 but continues research in numerical computation, computational finance, and scientific computing. Born in Montreal and raised in Toronto, Jackson earned all his degrees from the University of Toronto: BSc (1973), MSc (1974), and PhD (1978). He held roles as Gibbs Instructor and Visiting Assistant Professor at Yale University before returning to Toronto. Recognized as an NSERC University Research Fellow, he also served as Associate Chair for Graduate Studies (2002–2005) and President of the Canadian Applied and Industrial Mathematics Society (2001–2003). His research focuses on numerical methods for ODEs, parallel computation, validated solutions, and applications in finance, medical imaging, and climate modeling. He has advised over 30 graduate students, many of whom contributed to groundbreaking work in computational finance and scientific computing. Jackson organized the 2001–02 Thematic Year on Numerical Challenges in Science at the Fields Institute and currently advises YetiWare. He teaches advanced courses in numerical methods, optimization, and computational finance, and has published extensively on topics ranging from high-dimensional ODE systems to GPU-accelerated financial modeling.

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