برای مسیر پژوهشیتان، استاد راهنما، استاد دانشگاه و پژوهشگر مناسب را پیدا کنید و با آنها ارتباط بگیرید.
نمایش ۱-۱۵ از ۲۵۲ استاد راهنما
اطلاعات پروفایلها از صفحات عمومی دانشگاهها گرفته شده و ممکن است بهروز نباشد.
James Bremer is a Professor in the Department of Mathematics and holds a cross-appointment in the Department of Computer and Mathematical Sciences at the University of Toronto's Scarborough campus. His research focuses on developing efficient numerical algorithms for solving elliptic boundary value problems, integral equations, and special function transforms.
Professor Keshav Dasgupta holds the position of Professor in Physics at McGill University since March 2021. His academic journey includes a MSc from the Indian Institute of Technology, Delhi and a PhD from the Tata Institute of Fundamental Research, Mumbai. Postdoctoral research followed at the Institute for Advanced Study (Princeton, USA) and Stanford University (USA). He transitioned to faculty roles at McGill starting as an Assistant Professor (2005-2010), then Associate Professor (2010-2021), and currently as a full Professor. His research interests span Superstring Theory (focusing on flux compactifications and gauge/gravity dualities), String Cosmology (exploring de Sitter spaces and primordial phenomena), Quantum Field Theories (confinement dynamics in thermal QCD), and Mathematics (non-Kähler manifolds and Lie group applications in string theory). He also investigates Knot Theories within M-theory frameworks. Recent work emphasizes de Sitter vacua in string theory, leveraging Glauber-Sudarshan states and confronting swampland conjectures. His publications address topics like holographic QCD, quantum gravity equations, and non-perturbative string solutions. He teaches advanced courses such as PHYS 562: Electromagnetic Theory (Winter 2023).
Justin Wan is a Professor in the Department of Computer Science at the University of Waterloo. His research focuses on scientific computing, medical image processing, computational finance, and machine learning. He holds a Ph.D. from UCLA (1998), an M.A. from UCLA (1995), and a B.Sc. from the Chinese University of Hong Kong (1992). Wan’s work bridges numerical methods, optimization, and deep learning, with applications in financial modeling, medical imaging, and fluid dynamics. His research interests include advanced techniques in scientific computing (e.g., multigrid methods), computer graphics simulation, and medical image enhancement (e.g., CT scan artifact reduction). He has pioneered applications of machine learning to computational finance, including option pricing and hedging using deep neural networks and GANs. His recent work explores denoising diffusion models and multi-agent systems for optimal execution in finance. Publications span topics like volatility surface computation, optimal mass transport for image registration, and parallel solvers for fluid dynamics. His methods address challenges in high-dimensional problems, robust numerical valuation, and scalable algorithms for large datasets. Wan collaborates across disciplines, integrating mathematical rigor with practical engineering solutions.
دانشیار
Ying MacNab is an Associate Professor in the Department of Statistics at the University of British Columbia (UBC), Vancouver Campus. She holds an additional affiliation as an Associate Member in the School of Population and Public Health (SPPH). Her research focuses on Bayesian hierarchical modeling, spatial epidemiology, and disease mapping with applications to public health surveillance and aging populations. She has contributed extensively to methodological advancements in Gaussian Markov random fields and spatiotemporal modeling frameworks. Her work bridges statistical theory and practical health challenges, including pandemic-related stress in older adults, opioid treatment outcomes, and infectious disease forecasting. MacNab has collaborated on projects involving mental health assessments (e.g., sleep dysfunction, anxiety/depression in iOAT patients) and has developed novel statistical tools for analyzing spatially and temporally correlated health data. Her research also addresses methodological gaps in coregionalized multivariate models and constrained Bayesian estimation. MacNab's publications reflect a multidisciplinary approach, integrating epidemiological theory with advanced computational methods. Recent trends in her work emphasize dynamic modeling of infection risks, mediation analysis in aging populations, and validation of psychometric scales for health-related stress. She has maintained an active research agenda since the early 2000s, with notable contributions to neonatal health outcomes, injury surveillance, and healthcare quality improvement.
Dr. Joshua Brinkerhoff is an Associate Professor in Mechanical Engineering at the University of British Columbia Okanagan Campus. He serves as the Associate Director for Research & Industrial Partnerships in the School of Engineering and leads the UBC-Okanagan Computational Fluid Dynamics Laboratory. His research spans computational fluid dynamics, turbomachinery, multiphase flows, hydrogen safety, wind energy, and biofluid mechanics. He teaches courses in mechanics of materials, alternative energy systems, turbulence, computational fluid dynamics, and aircraft design. PhD, Aerospace Engineering (Carleton University, Ottawa, ON) BEng, Aerospace Engineering (Carleton University) Dr. Brinkerhoff’s research interests include: Computational Fluid Dynamics (CFD) for laminar-to-turbulent transition and instability analysis Wind energy systems and turbine aerodynamics Hydrogen storage and safety protocols for transportation Biofluid mechanics for respiratory diseases and aneurysm modeling Multiphase flows in industrial and environmental contexts His publications focus on CFD simulations for: Aerosol dispersion and mitigation in indoor environments Wind farm interactions and atmospheric gravity waves Cavitation and phase transitions in cryogenic and LNG systems Heat transfer optimization in industrial and thermal systems Instability dynamics in buoyancy-driven and swept flows Turbulent structures in fluidized beds and reactors Dr. Brinkerhoff has no listed scientific awards in the provided data but has extensive contributions to renewable energy, hydrogen safety, and medical fluid dynamics. His laboratory develops open-source tools like TOSCA for large-eddy simulations and investigates practical applications in urban air quality, dental aerosol control, and turbine wake modeling.
دانشیار
Piotr Zwiernik is an Associate Professor in the Department of Statistical Sciences at the University of Toronto's Faculty of Arts and Science, with a cross-appointment in the Department of Mathematics. Currently on leave from the University of Toronto, he is based in Barcelona following his return in July 2025. His academic journey includes a PhD in Statistics from the University of Warwick (2011), research positions at prestigious institutions including Mittag Leffler Institute, IPAM, TU Eindhoven, UC Berkeley, and the University of Genoa, and an Assistant Professorship at Universitat Pompeu Fabra in Barcelona (2016-2021). His research spans the intersection of statistics, mathematics, and computational methods, with particular emphasis on graphical models, covariance matrix estimation, convex analysis, tensors, and algebraic and combinatorial methods in statistics. Zwiernik's work demonstrates a consistent focus on high-dimensional statistics, mathematical statistics, and elegant theoretical frameworks that bridge abstract mathematics with practical statistical applications. His recent publications reveal a deepening exploration of tensor analysis, algebraic statistics, and the geometric properties of statistical models. Zwiernik serves as an associate editor for leading journals including Biometrika, Scandinavian Journal of Statistics, and Algebraic Statistics. His research program includes the development of the GOLAZO R package for asymmetric regularization of log-likelihood in Gaussian graphical models. As an academic leader, he has served as Associate Chair for Research in his department and actively participates in numerous international conferences and workshops, reflecting his significant standing in the statistical community. His recent publications show a strong trend toward algebraic and geometric approaches to statistical problems, with increasing focus on tensor methods, positivity constraints in statistical models, and the theoretical foundations of graphical models. The work demonstrates remarkable continuity in exploring the mathematical structures underlying statistical models while adapting to emerging challenges in high-dimensional data analysis. Zwiernik is committed to mathematical accessibility and education, guided by Federico Ardila's four axioms which emphasize equitable distribution of mathematical potential, joyful mathematical experiences, mathematics as a malleable tool, and treating every student with dignity and respect. He actively seeks PhD students with strong mathematical backgrounds for research at UPF or the Institute of Mathematics of UPC.
Samuel W.K. Wong is an Associate Professor in the Department of Statistics and Actuarial Science at the University of Waterloo. He holds a Ph.D. in Statistics from Harvard University (2013) under Prof. Samuel Kou. His research focuses on statistical methodology for complex data science challenges in protein structure modeling, dynamic systems inference, and reliability engineering of wood-based products. He has held academic positions at the University of Florida (2013–2018) and has been at Waterloo since 2018. His research interests include Bayesian computation, statistical inference for dynamic systems, and spatial-temporal data analysis. Notable contributions include the development of manifold-constrained Gaussian processes (MAGI package) and sequential Monte Carlo methods for protein folding studies. He has advised over 15 graduate students and researchers, many of whom are now in academic or industry roles worldwide. Wong has received teaching distinctions at Harvard and holds awards including the Nash Medal (2008) for academic excellence. His work bridges computational statistics with applications in bioinformatics, structural engineering, and environmental science. He has published extensively in top-tier journals like Journal of Computational and Graphical Statistics and Biometrics , and collaborates with wood scientists to improve real-time lumber quality assessment using laser imaging data. His teaching portfolio includes courses on probability theory, statistical inference, and spatial data analysis at both undergraduate and graduate levels. Beyond academia, he maintains an active passion for classical piano performance, having performed recitals combining music with his statistical research interests.
دانشیار
Lena Simine is an Associate Professor in the Department of Chemistry at McGill University, affiliated with the Faculty of Science. She holds a B.Sc. (2009) and Ph.D. (2015) from the University of Toronto, followed by a postdoctoral fellowship at Rice University (2015–2019). Her laboratory is located in P&P 118A, focusing on developing computational approaches for modeling molecular phenomena in theoretical chemistry and chemical physics. Her research interests center on computational materials design, quantum dynamics, and the application of machine learning to chemistry. Specific areas include simulating amorphous materials, aptamer design, and quantum systems modeling. She teaches CHEM 365 (Statistical Thermodynamics) and CHEM 593 (Statistical Mechanics and Machine Learning for Chemistry). Her work explores interdisciplinary frontiers, such as path-integral simulations, GFlowNets for molecular design, and the physical principles underlying deep learning in materials science. Recent studies highlight innovations like DeltaGzip for binding affinity prediction and the MAP protocol for 3D disordered matter simulations. Her lab’s contributions span computational methods, material innovation, and quantum phenomena, with a focus on advancing both theoretical frameworks and practical applications in chemistry and materials science.
استاد مدعو
Axel Moehrenschlager is an Adjunct Professor in the Department of Biological Sciences at the University of Calgary's Faculty of Science. He concurrently serves as Director of Conservation & Science at the Calgary Zoological Society and holds adjunct roles at Clemson University (Adjunct Associate Professor) and New Zealand's University of Canterbury (Erskine Fellow). His expertise spans conservation translocations, species reintroduction, and community-based conservation, with a focus on leveraging science for global biodiversity preservation. Moehrenschlager earned a B.Sc. from the University of Alberta (1993) and a D.Phil. in Zoology from Oxford University (2001). He chairs the IUCN's Conservation Translocation Specialist Group, driving policy integration of the IUCN Reintroduction Guidelines. He advises the UN Equator Prize and the St. Andrews Prize for the Environment, emphasizing sustainable conservation practices. His research integrates ecological, social, and policy dimensions, addressing challenges like sylvatic plague in prairie dogs, amphibian reintroductions, and swift fox genetics. Publications highlight innovative methodologies in conservation translocations, climate change impacts, and community-led conservation. His work bridges academic research with practical applications, aiming to harmonize biodiversity conservation with human well-being. Moehrenschlager has received the Erskine Fellowship (University of Canterbury) and contributed to over 30 peer-reviewed articles and book chapters. His leadership at the Calgary Zoo's Centre for Conservation Research fosters international collaborations, focusing on Africa and Madagascar. Current projects explore translocation success metrics and the ethical dimensions of conservation actions.
استادیار
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.
دانشیار
Franz Huber is an Associate Professor in the Department of Philosophy at the University of Toronto (St. George campus). He holds an MA from the University of Salzburg, Austria, and a PhD from the University of Erfurt, Germany. His primary research areas include Formal Epistemology, Philosophy of Science, Philosophical Logic, and Metaphysics. Huber’s work focuses on belief revision, counterfactual reasoning, and means-end rationality, often employing formal methods to address philosophical problems. His publications include influential books like A Logical Introduction to Probability and Induction (2018) and Belief and Counterfactuals (2021), as well as numerous peer-reviewed articles in journals such as Synthese and Journal of Philosophical Logic . His research explores the foundations of epistemology and metaphysics, particularly through ranking theory and its applications to belief change, counterfactuals, and causal reasoning. Huber has also contributed to the philosophy of science, examining confirmation theory and the logic of scientific reasoning. His recent work emphasizes a means-end approach to philosophical problems, integrating instrumental rationality with normative theory. Huber’s publications demonstrate a commitment to bridging formal methods with traditional philosophical inquiry. His academic contributions include editing volumes on belief revision and co-authoring works on ranking theory. He maintains an active research agenda, reflected in his latest book Causality, Counterfactuals, and Belief (2025). His office is located in the Jackman Humanities Building, and he is affiliated with the University of Toronto’s philosophy faculty and research initiatives such as the Balzan Research Project on Styles of Reasoning.
Shaun Lui is Professor and Head of Mathematics at the University of Manitoba's Faculty of Science. His research develops advanced numerical methods for partial differential equations with applications in fluid dynamics and electromagnetics. Education includes B.Sc./M.Sc. from University of Toronto and Ph.D. from Caltech. Research focuses on spectral collocation methods in space-time, domain decomposition, and finite volume schemes. Recent work establishes spectral accuracy for Stokes flows and matrix singularity bounds. Supervises graduate students in numerical PDE projects.
Ronald D. Haynes is a Full Professor and Chair of Scientific Computing Graduate Programs in the Department of Mathematics and Statistics at Memorial University of Newfoundland. He leads research in numerical methods for PDEs and industrial-scale optimization problems. His work develops advanced domain decomposition techniques, adaptive mesh methods, and parallel computing approaches for solving complex physical systems. Applications include modeling pitting corrosion of materials, predicting rock strength for drilling optimization, and simulating multiphase fluid flows in porous media. Recent publications demonstrate innovations in mesh adaptation, parallel algorithms, and machine learning applications for industrial problems. Collaborative projects have addressed reservoir simulation, drill bit analysis, and corrosion prediction through integrated computational approaches. Professor Haynes has received the President's Award for Outstanding Research (2018) and Dean of Science Distinguished Teaching Award (2017). He serves as Co-editor-in-chief of the CAIMS Mathematics in Science and Industry Journal and was President-Elect of the Canadian Applied and Industrial Mathematics Society (2023-2025). He maintains active doctoral supervision with current research groups focusing on domain decomposition methods, closest point algorithms, and optimization techniques. Industry partnerships include projects with ExxonMobil and Global Maritime addressing drilling optimization and mooring design challenges.
دانشیار
Dr. Min Sun is an Associate Professor and Director of the Undergraduate Program in the Department of Civil Engineering at the University of Victoria (UVic). He holds a PhD from the University of Toronto. His research focuses on structural engineering and steel structures, particularly in the areas of steel connections, seismic resilience, and numerical modeling. Dr. Sun has extensive academic and professional experience, including roles as Assistant Professor at UVic (2016–2022), Lecturer at the University of Toronto, and structural design roles in industry. His research interests emphasize the performance of steel structures under extreme loads, including earthquake engineering and material behavior. Recent work includes studies on stress concentration factors in steel connections, thermal integrity of piles, and wood-frame building reliability under lateral loads. He actively contributes to professional organizations, such as serving as Vice President (Western Region) for the Canadian Society for Civil Engineering (2018–2020). Dr. Sun teaches courses including Advanced Structural Analysis (CIVE 421) and Solid Mechanics (CIVE 220) at UVic. He currently supervises graduate students in structural steel design and construction. His publications span experimental and numerical analyses, with a focus on improving design standards for steel and wood structures. Labs/Teams: Affiliated with UVic's Engineering and Computer Science faculty and the IESVIC (Institute for Energy Systems and Sustainability at UVic), though specific lab names are not explicitly stated in the text.
Yves Bourgault is a Full Professor in the Department of Mathematics and Statistics at the University of Ottawa. He holds a MSc and PhD from Laval University. His research focuses on computational fluid dynamics, numerical methods, finite element techniques, and continuum mechanics modeling, with applications in cardiac electrophysiology and ecological systems. Dr. Bourgault has supervised several graduate students, including Edward Boey (co-supervised), Sana Keita, Saint-Cyr Koyagurebo-Ime, and Kak Choon Loy. His work integrates advanced numerical techniques to address complex problems in biomedical engineering, environmental science, and mathematical physics. Key methodologies include finite element methods, deferred correction schemes, and anisotropic mesh adaptation. His research group is part of the Applied Mathematics division at the University of Ottawa, emphasizing interdisciplinary applications. Recent work explores climate change impacts on ecological systems, cardiac tissue modeling using high-resolution MRI data, and robust numerical methods for reaction-diffusion equations. Publications span topics such as bidomain models for cardiac electrophysiology, fluid-structure interaction in heart mechanics, and mathematical modeling of fuel cells. His contributions bridge theoretical numerical analysis with real-world biomedical and environmental challenges.
