Function Approximation Professors and Supervisors in Canada

Peter D. Lawrenceمشاهده پروفایل

استاد

University of British ColumbiaCanada

Professor Peter D. Lawrence holds a faculty position at the University of British Columbia (UBC) within the Department of Electrical & Computer Engineering, part of the Faculty of Applied Science. He has been a Professor since 1974 and has held visiting research roles at Chalmers University of Technology (1970-1972) and MIT (1972-1974). His educational background includes a B.A.Sc. from the University of Toronto (1965), M.Sc. from the University of Saskatchewan (1967), and Ph.D. from Case Western Reserve University (1970). He is a Professional Engineer (P.Eng.) and Fellow of the Canadian Academy of Engineering (FCAE). Research interests focus on improving human-machine interfaces, sensor technologies for control systems, and medical robotics. Key areas include teleoperation of heavy machinery, vision-based control, EEG-based brain interfaces, and functional approximation methods for complex systems. Collaborations span multiple disciplines at UBC, including Mechanical Engineering, Computer Science, Mining Engineering, and Forestry, with funding from NSERC and PRECARN/IRIS. Medical Robotics: Brain-computer interfaces and ultrasound-guided surgery. Autonomous Systems: Path planning and vision-based tracking for excavators and haul trucks. Sensing Technologies: Eye-tracking, joint-angle sensors, and slip detection for mobile robots. His teaching contributions include coordinating the Project Integrated Program (PIP) for ECE students and co-developing the interdisciplinary New Venture Design course with the Sauder School of Business. He leads the RCL Lab and has authored books on real-time microcomputer systems and contributed to IEEE publications. Awards include recognition as a Fellow of the Canadian Academy of Engineering.

پروفایل رسمی دانشگاه

Margarida Carvalhoمشاهده پروفایل

دانشیار

HEC MontréalCanada

Margarida Carvalho is an Associate Professor in the Department of Computer Science and Operations Research at Université de Montréal, where she holds the FRQ-IVADO Research Chair in Data Science for Combinatorial Game Theory. She is also an Associate Academic Member at Mila (Quebec AI Institute), contributing to their research in AI for Humanity. Her academic journey spans from Portugal to Canada, where she has established herself as a leading researcher at the intersection of operations research and game theory. Carvalho earned her bachelor's and master's degrees in mathematics from the Faculty of Sciences of the University of Porto (FCUP), followed by a PhD in Computer Science from the same institution in 2016. Her doctoral work, which focused on game theory applications for kidney exchange programs, earned her the prestigious 2018 EURO Doctoral Dissertation Award, making her the first Portuguese woman to receive this honor. After completing her PhD, she worked as an IVADO Postdoctoral Fellow at Polytechnique Montréal before joining Université de Montréal as an Assistant Professor in 2018. Her research focuses on combinatorial optimization and algorithmic game theory, with applications spanning healthcare (kidney exchange programs, hospital operations), sustainable development (electric vehicle infrastructure, urban planning), and education (school choice systems). She develops novel mathematical programming approaches to model and solve problems involving multiple decision-makers with potentially conflicting objectives. Her work bridges theoretical advances in optimization with practical implementations that address real-world challenges in resource allocation and decision-making under uncertainty. Notably, her research on fairness in kidney exchange programs has contributed to more equitable organ allocation policies. Her 15 most recent publications reveal a strong trend toward integrating game-theoretic concepts with practical optimization challenges, particularly in healthcare and sustainable infrastructure. She has pioneered approaches that balance utilitarian objectives with fairness considerations, developed novel formulations for bilevel and multilevel optimization problems, and created learning-based frameworks for complex decision environments. Her work consistently demonstrates how mathematical rigor can inform practical policy decisions in critical domains. 2018 EURO Doctoral Dissertation Award for her PhD thesis on game theory applications for kidney exchange programs Mathematical Programming 2024 Meritorious Service Award Teaching Excellence Award from Université de Montréal Supervised student Maria Bazotte receiving the Dupačová-Prékopa Best Student Paper Prize in Stochastic Programming Carvalho actively advises graduate students, with Marylou Fauchard (Master's) and William St-Arnaud (PhD) among her current advisees. Her research is supported by grants from Hydro-Québec, the Natural Sciences and Engineering Research Council of Canada (Discovery grant 2017-06054 and Collaborative Research and Development Grant CRDPJ 536757–19), and FRQ-IVADO. She serves as an associate editor for INFORMS Journal on Computing, OR Spectrum, and Dynamic Games and Applications, and is a founding board member and treasurer of the Bilevel Optimization Society. She teaches courses in Mathematical Programming, Operational Research Models, and Discrete Mathematics at Université de Montréal. Carvalho is affiliated with Mila (Quebec AI Institute), where she contributes to research initiatives focused on AI for Humanity, particularly in the areas of algorithmic fairness and sustainable development. Her FRQ-IVADO Research Chair supports her work on combinatorial game theory applications, and she collaborates with researchers across disciplines through the IVADO research community. She has been instrumental in establishing the Bilevel Optimization Society, creating a dedicated forum for researchers working on hierarchical decision-making problems.

پروفایل رسمی دانشگاه

Deeksha Adilمشاهده پروفایل

استادیار

University of TorontoCanada

Deeksha Adil is a Junior Fellow at the Institute for Theoretical Studies in ETH Zurich since January 2023. Her research focuses on designing fast algorithms with provable guarantees for problems in optimization, machine learning, and theoretical computer science. In August 2026, she will transition to an Assistant Professor (Reader) position in the School of Technology and Computer Science at the Tata Institute of Fundamental Research in Mumbai. Ph.D. in Computer Science from University of Toronto (2022), supervised by Prof. Sushant Sachdeva BS-MS in Mathematics from Indian Institute of Science Education and Research, Pune (2017) Visiting Researcher at Simons Institute for Theory of Computation (2023) Research Associate at University of Michigan (2022) Visiting Student at Institute for Advanced Study, Princeton (2019) Dr. Adil's research program centers on algorithm design for optimization problems, particularly lp-norm regression and related challenges. She develops methods that leverage optimization theory and continuous analysis to create efficient algorithms with theoretical guarantees. Her work spans theoretical computer science, machine learning, and numerical analysis, with applications in network optimization and statistical learning. Her publication record shows consistent progress in developing faster algorithms for fundamental optimization problems. Recent work extends into non-convex optimization, covariate shift adaptation, and dynamic algorithms for linear algebra problems. She publishes regularly in top-tier venues including Journal of ACM, NeurIPS, ICALP, and SODA. At the University of Toronto, she served as Teaching Assistant for multiple advanced courses including Algorithm Design, Algorithmic Game Theory, Theory of Computation, and Numerical Analysis, demonstrating strong pedagogical skills alongside her research excellence.

پروفایل رسمی دانشگاه

Vladimir Vinogradovمشاهده پروفایل

استاد

University of TorontoCanada

Dr. Vladimir Vinogradov is a Professor of Mathematics at Ohio University and holds a status-only appointment at the University of Toronto, Scarborough (UTSC) within the Department of Statistical Sciences. He has affiliations with the Fields Institute as a Visiting Member and has previously served in administrative roles at Ohio University and UTSC. M.Sc. in Mathematics, Moscow State University Ph.D. in Probability and Statistics, Moscow State University Professor Vinogradov's research spans Probability Theory, Stochastic Processes, Mathematical Statistics, Financial Mathematics, and Mathematical Oncology. His work includes extensions of the Feller–Spitzer distribution, properties of Wright functions in probability, and stochastic models in tumor control probability. He has presented at numerous international conferences and contributed to the Fields Undergraduate Summer Research Program. Recent trends in his research include equi-/over-/underdispersion in probability distributions and connections between analysis and probability theory. He has also explored discontinuous superprocess approximations. Scientific Awards: NSERC International Research Fellowship BC Asia Pacific Scholars' Award Japan Society for the Promotion of Science Fellowship Professor Vinogradov has advised Actuarial Science and Mathematical Statistics majors and held leadership roles such as Chair of the Mathematics Promotion and Tenure Committee at Ohio University. He has served as an NSERC Canada external reviewer and graduate coordinator at the University of Northern British Columbia. He has co-organized international conferences, including the International Conference in Probability and Statistics 2022 and International Conference on Analysis, Applications, and Computations 2015 , and maintains an active research presence in probability and statistics.

پروفایل رسمی دانشگاه

Yu-Ting Chenمشاهده پروفایل

دانشیار

University of VictoriaCanada

Probability theory
Stochastic partial differential equations
Interacting particle systems
+۳ مورد دیگر

Yu-Ting Chen is an Associate Professor in the Department of Mathematics and Statistics at the University of Victoria. He holds a PhD from the University of British Columbia. His research focuses on probability theory, stochastic partial differential equations, and interacting particle systems with applications to mathematical physics and evolutionary dynamics. He teaches advanced courses in calculus, partial differential equations, stochastic processes, and mathematical statistics. Dr. Chen's work bridges stochastic analysis and mathematical physics, particularly in systems like the delta-Bose gas and voter models. His recent studies include critical stochastic heat equations, many-body quantum systems, and spatial evolutionary games. He has published extensively in top journals such as Annals of Probability and Nature , contributing to stochastic mechanics and coexistence dynamics in spatial populations. Education: PhD, University of British Columbia Affiliations: University of Victoria, David Turpin Building A521 His teaching portfolio includes graduate-level courses on partial differential equations and stochastic modeling. He actively contributes to research in probability theory with a focus on interdisciplinary applications.

پروفایل رسمی دانشگاه

Julien Arinoمشاهده پروفایل

استاد

University of ManitobaCanada

Julien Arino is a Professor in the Department of Mathematics at the University of Manitoba , specializing in Mathematical Epidemiology and Mathematical Population Dynamics . He is an active member of the Mathematical Biology group and focuses on the role of movement in disease transmission, with applications to both human and animal health. Department of Mathematics Faculty of Science University of Manitoba His research spans: Metapopulation dynamics with explicit movement Multi-pathogen and multi-species transmission modeling Computational epidemiology with data science integration Evaluation of travel control measures for disease variants Recent work includes publications on: Cholera transmission in Chad Multi-species bovine tuberculosis dynamics Measles resurgence patterns Software tools for R compilation and data visualization He maintains a public YouTube channel with lecture videos on Mathematical Epidemiology and contributes open-source code for scientific computing in R and Python. His methodological contributions include novel approaches to plot formatting and computational efficiency in epidemic modeling.

پروفایل رسمی دانشگاه

Kirill Kopotunمشاهده پروفایل

استاد

University of ManitobaCanada

Kirill Kopotun is a Professor in the Department of Mathematics at the University of Manitoba , where he has held a faculty position since at least 1995. His research focuses on approximation theory , particularly in polynomial and spline approximation , moduli of smoothness , and convex/constrained approximation . He actively contributes to numerical analysis , linear algebra , and partial differential equations . Research Themes : Uniform/pointwise estimates for polynomial approximation, applications of Jacobi weights, shape-preserving approximation, k-monotone functions Collaborations : Extensive co-authorship with D. Leviatan , I. A. Shevchuk , and others in approximation theory. Publications span 2019–2015, emphasizing shape-preserving approximation , Jacobi-weighted approximation , and moduli of smoothness . His work appears in journals like Constructive Approximation , Journal of Approximation Theory , and Ukrainian Mathematical Journal . Contact : Office 422 Machray Hall, Kirill.Kopotun@umanitoba.ca

پروفایل رسمی دانشگاه

Richard S. Suttonمشاهده پروفایل

استاد

University of AlbertaCanada

Richard S. Sutton is a pioneering researcher and Professor of Computing Science at the University of Alberta, where he serves as Chief Scientific Advisor for the Alberta Machine Intelligence Institute (Amii). He is also a Canada CIFAR AI Chair, Senior Fellow at CIFAR, and Research Scientist at Keen Technologies. Sutton is widely recognized as one of the founders of reinforcement learning, a field in which he continues to lead globally. His work has profoundly shaped modern AI research and applications. Education: B.A., Psychology, Stanford University (1978) M.S., Computer Science, University of Massachusetts (1980) Ph.D., Computer Science, University of Massachusetts (1984) Sutton's research focuses on identifying computational principles underlying intelligence and goal-directed behavior. He emphasizes learning from experience and extending reinforcement learning to create empirically grounded approaches to knowledge representation based on prediction. His work bridges artificial and natural intelligence, exploring how systems can predict and influence the world through learning, perception, action, and cognition. Sutton's research has produced foundational contributions including temporal-difference learning theory, actor-critic algorithms, the Dyna architecture, and Horde architecture. His recent publications reveal a continued focus on reinforcement learning fundamentals while expanding into broader AI applications. The articles show strong emphasis on theoretical foundations, convergence analysis, and practical implementations of reinforcement learning algorithms. There's also evidence of applying these techniques to real-world problems like pandemic forecasting, suggesting growing interest in practical societal applications of his theoretical work. Scientific Awards: 2024 Turing Award (with Andrew Barto) Fellow of the Royal Society (2021) Fellow of the Royal Society of Canada (2016) CAIAC Lifetime Achievement Award (2018) Outstanding Achievement in Research Award, UMass Amherst (2013) Sutton has mentored approximately 60 early-career researchers, including notable figures like David Silver (lead researcher behind AlphaGo), Doina Precup (DeepMind Montreal), Adam White (Amii Fellow), and Cam Linke (CEO of Amii). His Reinforcement Learning & Artificial Intelligence Lab at the University of Alberta has become a global hub for RL research. Sutton is currently collaborating with John Carmack through Keen Technologies to accelerate AGI development, documented in part through 'The Alberta Plan' which outlines his vision for creating long-lived computational agents. Sutton founded the Reinforcement Learning & Artificial Intelligence (RLAI) Lab at the University of Alberta and co-authored the seminal textbook 'Reinforcement Learning: An Introduction' with Andrew Barto. His work has been cited over 130,000 times and featured in major publications including Science, The Economist, New York Times, and Wall Street Journal. He is also known for his 'Tea Time Talks' tradition at Amii, fostering new ideas among researchers and students.

پروفایل رسمی دانشگاه

Claude Frassonمشاهده پروفایل

دانشیار

University of MontrealCanada

Claude Frasson is an Associate Professor in the Department of Computer Science and Operational Research at the Faculty of Arts and Sciences, University of Montreal. He serves as Director of the HERON laboratory (Higher Educational Research ON Emotional Intelligence and Privacy Protection) and was formerly Director of GRITI (Inter-university Research Group in Intelligent Tutors), which grouped seven universities in Quebec. Dr. Frasson earned his Doctorat d'État in Computer Science from the University of Nice, France in 1981. His academic journey spans over three decades with significant contributions to the field of intelligent systems for education and cognitive support. His primary research interests focus on the intersection of emotional intelligence, brain-computer interfaces, and virtual reality applications for education and healthcare. He investigates how emotional states affect learning processes and develops intelligent systems that can adapt to users' cognitive and emotional states. His work particularly emphasizes applications for Alzheimer's disease treatment, cognitive rehabilitation, and educational technology. His recent publications (2020) demonstrate a strong focus on virtual reality applications for Alzheimer's disease treatment, cognitive rehabilitation, and emotional regulation. These works combine intelligent tutoring systems with neuroscience principles to create therapeutic environments that respond to users' cognitive states in real-time. Throughout his career, Dr. Frasson has been actively involved in numerous international conferences, serving as keynote speaker at events such as GENEDIS, World Brain Disorder and Neuroscience Summit, and International Conference on Intelligent Tutoring Systems. Dr. Frasson has supervised over 80 graduate students since 1989, spanning both Master's and PhD programs. His students have conducted research in areas including emotional intelligence in learning systems, brain-computer interfaces, virtual reality applications, and intelligent tutoring systems. He has received substantial research funding from organizations including CRSNG, FRQSC, CRIAQ, and FCI. He directs the HERON laboratory, which focuses on emotional intelligence, privacy protection, and cognitive state monitoring in educational contexts. Previously, he led GRITI, an inter-university research group that produced approximately 70 graduate students and established international recognition in the field of intelligent tutoring systems.

پروفایل رسمی دانشگاه

Laurent Karim Bélandمشاهده پروفایل

دانشیار

Queen's University at KingstonCanada

Computational material science
Machine learning applied to materials science
atom-scale simulations
+۶ مورد دیگر

Laurent Karim Béland is an Associate Professor in Mechanical and Materials Engineering at Queen's University, Kingston, Ontario. His research employs computational methods including atom-scale simulations, machine learning, and physical chemistry to study materials for nuclear applications, bone fracture, and clay/cement systems. Education: PhD in Physics, Université de Montréal (2014) BSc in Physics, Université de Montréal (2008) Dr. Béland's expertise spans computational materials science with focus on machine learning for materials, atom-scale simulations, and physical chemistry. His work addresses nuclear materials (zirconium hydrides), biomaterials (bone fracture), and environmental materials (clay for waste containment). He develops multi-scale models from quantum mechanics to solid mechanics. Recent publications highlight trends in machine learning interatomic potentials, amorphous materials simulation, and radiation effects in nuclear materials. Key areas include high entropy alloys, zirconium alloys for nuclear cladding, and neural networks for electron microscopy analysis. Scientific Awards: No awards mentioned in the provided text. Dr. Béland leads the Béland Lab, advising graduate students in computational materials science. Specific student names and grant details were not provided. The Béland Lab collaborates with Queen's Reactor Materials Testing Laboratory and the Center for Advanced Computing (Compute Canada), utilizing supercomputing to model materials across length and time scales from nanometers to meters.

پروفایل رسمی دانشگاه

Serge Prudhommeمشاهده پروفایل

استاد

Polytechnic School of MontrealCanada

Serge Prudhomme is a Full Professor in the Department of Mathematics and Industrial Engineering at Polytechnique Montréal. With a background including a Dipl. Eng. from École Centrale de Lille, an M.Sc. from the University of Virginia, and a Ph.D. from UT Austin, he has established himself as a leading researcher in computational mathematics and mechanics. His primary research interests span scientific computing, finite element methods, error estimation and adaptive methods, multi-scale modeling, verification and validation of numerical calculations, uncertainty quantification, and model reduction. Professor Prudhomme's work demonstrates a consistent focus on developing mathematically rigorous computational frameworks with practical engineering applications. His recent publications show an expanding interest in integrating machine learning techniques with traditional numerical methods. Analysis of his recent publications reveals a strong trend toward interdisciplinary research that bridges computational mathematics with mechanics and increasingly machine learning. His work on peridynamics, proper generalized decomposition, and neural network applications to scientific computing represents cutting-edge developments in computational methods. The publications demonstrate a consistent focus on improving the accuracy, efficiency, and reliability of computational models. Professor Prudhomme has successfully supervised 10 graduate students to completion (4 doctoral theses and 6 master's theses), indicating a strong commitment to student mentorship. His supervision record spans topics including wave equation approximations, goal-oriented calibration, contact detection for ellipsoids, and PGD reduced-order modeling. His research is supported by active collaborations with institutions worldwide, as evidenced by his co-authorship patterns and conference participation across North America and Europe. The breadth of his work, from fundamental mathematical developments to practical engineering applications, positions him as a significant contributor to the field of computational science and engineering.

پروفایل رسمی دانشگاه

Marc Laforestمشاهده پروفایل

دانشیار

Polytechnic School of MontrealCanada

Marc Laforest is an Associate Professor in the Department of Mathematics and Industrial Engineering at Polytechnique Montréal. He holds a B.Sc. from UQAM and M.Sc. and Ph.D. degrees from SUNY at Stony Brook. His academic career at Polytechnique Montréal spans multiple decades, during which he has established himself as a researcher in computational mathematics and numerical analysis. Professor Laforest's research interests focus on continuum mechanics, hyperbolic conservation laws, kinetic equations, numerical analysis, multi-scale analysis, a posteriori error estimation, and mesh adaptivity . His work bridges theoretical mathematics with practical applications, particularly in wave propagation and gas mechanics. Recent publications demonstrate increasing integration of machine learning techniques with traditional numerical methods, reflecting evolving trends in computational science. Analysis of his 15 most recent publications reveals a strong trajectory in computational mathematics with growing emphasis on machine learning applications. His research spans pure mathematical theory to practical engineering applications, particularly in superconductivity modeling. The consistent publication rate (2 papers in 2025, 2 in 2024, 2 in 2023, and 5 in 2022) indicates active research programs and likely multiple ongoing grants. Professor Laforest has supervised 5 doctoral students to completion (most recent in 2024) and 9 master's students (most recent in 2023). His students' thesis topics reflect the breadth of his research interests, ranging from wave equation approximation to contact detection methods and superconductivity modeling. This extensive supervision record demonstrates his commitment to graduate education and mentorship. His teaching responsibilities include courses in differential equations, analysis, and numerical analysis , connecting his research expertise with classroom instruction. While specific grant information isn't provided in the available materials, his consistent publication record across multiple high-impact journals suggests successful grant acquisition and management of research funding.

پروفایل رسمی دانشگاه

Yves Grosdidierمشاهده پروفایل

مدرس

University of SherbrookeCanada

Dr. Yves Grosdidier is a Lecturer in the Department of Physics at the University of Sherbrooke's Faculty of Science, where he has taught theoretical physics courses since 2002. His academic journey includes doctoral research at Université de Montréal (1995-2000) and a postdoctoral position as Support Astronomer at Instituto de Astrofísica de Canarias (2000-2001). His research focuses on stellar mass-loss phenomena in Wolf-Rayet stars and planetary nebulae, with particular expertise in turbulent outflows, wind inhomogeneities, and dust formation in massive binary systems. Complementing this astrophysical work, he investigates radiative transfer in multifractal cloud structures , analyzing scaling properties across spatial scales from kilometers to meters using satellite data and Monte Carlo simulations. Analysis of his 59 publications (1997-2009) reveals three dominant research threads: (1) High-resolution studies of Wolf-Rayet nebulae like M1-67 using HST and CFHT data, (2) Characterization of wind fluctuations in [WC]-type planetary nebula nuclei through spectroscopic monitoring, and (3) Development of multifractal models for radiative transfer in optically thick clouds. His most influential works establish scaling laws in astrophysical turbulence and provide evidence for episodic dust formation in long-period WR+O binaries. 5,340 publication reads and 301 citations reflect community engagement with his work Extensive collaboration with international teams including NASA, ESO, and Canadian observatories Methodological contributions to structure function analysis and multifractal modeling As a teaching-focused academic, Dr. Grosdidier integrates his research expertise into theoretical physics instruction while maintaining active research collaborations. His work bridges observational astrophysics and theoretical fluid dynamics, with particular attention to scaling phenomena across multiple orders of magnitude.

پروفایل رسمی دانشگاه