جستجوی استاد راهنمای دانشگاهی

Laura Sanità is an Associate Professor in the Department of Computing Sciences at Bocconi University, Milan, Italy. Previously, she held positions at TU Eindhoven (2020–2022) and the University of Waterloo, Canada, where she was an Assistant Professor (2012–2017) and later Associate Professor (2017–2020). She earned a Bachelor’s and Master’s in Management Engineering from Università di Roma Tor Vergata (2003–2005), followed by a PhD in Operations Research from Università Sapienza di Roma (2009). Her postdoctoral work (2009–2011) was at EPFL’s Discrete Optimization Group. Her research focuses on Combinatorial Optimization , Approximation Algorithms , Network Design , and Algorithmic Game Theory . Key contributions include advancements in node connectivity augmentation, graph stabilization, and polytope diameter analysis. She has received prestigious awards such as the NWO-VIDI Award (Netherlands), NSERC Discovery Accelerator Supplements, and the Early Researcher Award (Ontario). Laura co-organizes the Bocconi Theory Day (May 2024) and serves on program committees for conferences like SODA, ESA, and IPCO. She is an Associate Editor for Mathematical Programming , Mathematics of Operations Research , and Operations Research Letters . Current advisees include PhD students Sean Kafer, Dylan Hyatt-Denesik, and Lucy Verbeck. Her work bridges theoretical foundations and practical applications, with notable publications in Mathematical Programming , SIAM Journal on Optimization , and Operations Research . Recent projects explore stabilization of capacitated matching games and iterative randomized rounding techniques for combinatorial problems.

• Combinatorial Optimization
• Approximation Algorithms
• Network Design
• +۳ مورد دیگر

Eldad Haber is a Professor in the Department of Earth, Ocean & Atmospheric Sciences at the University of British Columbia (UBC), affiliated with the Faculty of Science. He holds memberships in the Institute of Applied Mathematics and the CAIDA: UBC ICICS Centre for Artificial Intelligence Decision-making and Action. His research focuses on advancing computational methods at the intersection of geophysics, machine learning, and optimization, with applications in inverse problems, electromagnetic imaging, and reservoir characterization. Key areas of investigation include geophysical inversion techniques, machine learning for data-driven modeling, and the development of efficient algorithms for large-scale problems. Haber’s work integrates disciplines such as geophysics, computer science, and applied mathematics to address challenges in subsurface imaging, environmental monitoring, and resource exploration. Recent research trends in his publications emphasize the application of neural networks to inverse problems, optimization under uncertainty, and multiscale methods for improving computational efficiency. His work on ‘Synthetic Geology’ and ‘Graph Neural Networks’ highlights innovative approaches to geoscientific challenges using deep learning. Collaborations with industry and academic partners have yielded practical solutions for airborne electromagnetic data inversion and mineral prospectivity mapping. His contributions span theoretical advancements in adjoint methods, stochastic optimization, and data assimilation, alongside applied projects in hydrogeology and mineral exploration. Haber’s publications reflect a commitment to bridging computational innovation with real-world geophysical applications.

• Geophysics
• Inverse Problems
• Computational Methods
• +۵ مورد دیگر

Renjie Liao is an Assistant Professor (tenure-track) in the Department of Electrical and Computer Engineering (ECE) at the University of British Columbia (UBC), with an associated appointment in the Department of Computer Science. He is also a Faculty Member at the Vector Institute and a Canada CIFAR AI Chair. Prior to UBC, Dr. Liao was a Visiting Faculty Researcher at Google Brain and held a Senior Research Scientist position at Uber Advanced Technologies Group during his PhD. He earned his B.Eng. (Automation) from Beihang University, M.Phil. (CS) from the Chinese University of Hong Kong, and PhD (CS) from the University of Toronto. His research focuses on probabilistic and geometric deep learning , with key contributions in deep generative models, geometric deep learning, neural algorithmic reasoning, and generalization bounds. Notable areas include 3D point cloud analysis, self-driving systems, and healthcare applications using graph neural networks. His work bridges theoretical foundations (e.g., PAC-Bayes bounds) with practical applications like motion forecasting and medical imaging. Education: B.Eng. in Automation, Beihang University (2011) M.Phil. in Computer Science, CUHK (2015) PhD in Computer Science, UofT (2021) Dr. Liao has received awards such as the RBC Graduate Fellowship and Connaught International Scholarship. His lab (Deep Structured Learning Lab) emphasizes principled mathematical approaches to solving complex problems. He advises students in machine learning, computer vision, and robotics, encouraging applications from those with strong coding/mathematical backgrounds. Labs/Teams: Deep Structured Learning Lab (UBC) Vector Institute Collaboration

• Deep Learning
• Machine Learning
• Computer Vision
• +۴ مورد دیگر

Uri M. Ascher is a Professor in the Department of Computer Science at the University of British Columbia's Faculty of Science. He has established himself as a leading researcher in numerical analysis, scientific computing, and computational methods for differential equations. His work bridges theoretical mathematics with practical applications in computer animation, computational finance, and inverse problems. Ascher's research interests focus on numerical methods for evolutionary differential equations, boundary value problems, differential-algebraic equations, and their applications. His work spans both theoretical developments in numerical analysis and practical implementations in scientific computing. He has made significant contributions to the fields of computer animation through numerical methods for simulating deformable objects and physics-based animation. His publication record shows a consistent trajectory of high-impact research spanning several decades, with recent work focusing on the intersection of numerical methods with machine learning, particularly neural differential equations and data-driven approaches to inverse problems. His research demonstrates a unique ability to connect classical numerical analysis with emerging computational challenges in visual computing and scientific simulation. Fellow of the Royal Society of Canada (FRSC), 2018 SIAM Fellow, 2010 CAIMS Research Prize, 2010 Ascher has supervised numerous graduate students and collaborated extensively with researchers across disciplines. His work on numerical methods for computer animation has led to practical implementations used in graphics applications. He maintains active collaborations with researchers in scientific computing, applied mathematics, and computer graphics. Ascher is also affiliated with the Scientific Computing Laboratory and the Institute for Applied Mathematics at UBC, reflecting the interdisciplinary nature of his work.

• Numerical Analysis
• Scientific Computing
• Differential Equations
• +۴ مورد دیگر

Marilyn Lightstone is a Professor in the Mechanical Engineering department at McMaster University , specializing in Computational Fluid Dynamics (CFD) , Heat Transfer , and Turbulent Flows . Her research spans Thermo Fluid Sciences with applications to Nuclear Reactor Cooling Systems , Solar Energy Thermal Storage , and Atria Geometries . She collaborates with institutions like Queen’s University and University of Waterloo alumni. Education: B.Sc., Queen’s University (1985) M.A.Sc., University of Waterloo (1987) Ph.D., University of Waterloo (1992) Research Interests focus on advanced turbulence and particle dispersion models for industrial and nuclear applications. Key areas include: Development of stochastic and DNS-based particle dispersion models Thermal mixing prediction in nuclear subchannel geometries Validation of CFD models for atria flow dynamics Optimization of thermal storage systems for solar energy Investigation of turbulence modulation by particulates Her work on Detached Eddy Simulation (DES) achieved 68.4 Hz pulsation frequency prediction (vs experimental 68 Hz) in nuclear subchannels, while exploring cost-effective Unsteady RANS methods. Recent projects involve collaborations with Solar Buildings Research Network and funding from UNENE and NSERC CRD . Scientific Contributions : McMaster Students Union Teaching Award recipient Advising Legacy includes guiding Eleanor Hennick, Cathy Strutt, Fusheng Yan, and current students Matthew Cernick, Stephen Murray, and Alan Chettle. Her lab addresses fundamental fluid mechanics challenges with practical applications in energy and nuclear safety.

• Computational Fluid Dynamics (CFD)
• Heat Transfer
• Turbulent Flows
• +۲ مورد دیگر

Bartosz Protas is a Professor in the Department of Mathematics and Statistics at McMaster University , where he served as Director of the School of Computational Science & Engineering (2009-2019) and currently holds the Chair of the Department. His research bridges computational fluid dynamics, optimization theory, and applied mathematics, focusing on extreme behavior in fluid models, vortex dynamics, and electrochemical systems. Research Themes: Finite-time singularity formation in Navier-Stokes/Euler equations, vortex stability analysis via shape calculus, optimal turbulence closures, electrochemical inverse problems with binder migration and dendrite growth, and Riemann-Hilbert applications in fluid mechanics. Collaborations: Kyoto University (Takashi Sakajo), University of Rouen (Ionut Danaila), University of Michigan (Charles Doering), and industrial partners like General Motors. Awards & Invitations: JSPS Visiting Fellow (2017), SHARCNET Chair (2003), and multiple visiting professorships at top European institutions. Advising: Supervised 15+ Ph.D./M.Sc. students including Diego Ayala (recipient of Canadian Applied Mathematics Society's Cecil Graham Award) and Vladislav Bukshtynov. His recent publications analyze lithium plating in batteries, singularities in 3D Euler flows, and Sobolev gradient methods for PDE optimization.

• Applied Mathematics
• Computational Fluid Dynamics
• Optimization and Control Theory
• +۹ مورد دیگر

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.

• Algorithms
• Optimization
• Machine Learning
• +۴ مورد دیگر

Dominique Orban is a Full Professor in the Department of Mathematics and Industrial Engineering at Polytechnique Montréal. He holds a Ph.D. from FUNDP Namur and INP Toulouse and has established himself as a leading researcher in numerical optimization. His academic affiliations include the Institute for Data Valorization (IVADO) and the Decision Analysis Study and Research Group (GERAD). Professor Orban's research focuses on numerical mathematics, particularly continuous nonlinear optimization, nonlinear systems of equations, and numerical linear algebra. His work involves designing specialized numerical algorithms for optimization problems, with particular interest in degeneracy and ill-posed problems. His research spans theoretical development of algorithms, their implementation in software, and applications to real-world problems such as image reconstruction, optimal structure design, and optimization under differential constraints. Analysis of his recent publications (2021-2025) reveals a strong focus on developing practical optimization algorithms with theoretical guarantees. His work spans multiple areas including nonsmooth optimization, iterative methods for linear systems, regularization techniques, and software implementation in Julia. A notable trend is his increasing focus on developing open-source software tools that make advanced optimization methods accessible to practitioners. Over 160 publications including journal articles, conference papers, and technical reports Multiple publications in top optimization journals each year through 2025 Strong emphasis on both theoretical foundations and practical implementation Increasing focus on Julia-based optimization software development Professor Orban has successfully supervised 9 doctoral students and 13 master's students to completion, demonstrating his commitment to mentoring the next generation of researchers. His supervision style appears to balance theoretical depth with practical implementation skills, preparing students for both academic and industry careers. His research has been supported through various institutional and collaborative grants, enabling him to maintain an active research program with multiple ongoing projects. His contributions to the field include significant software developments such as Krylov.jl, JSOSuite.jl, and DCISolver.jl, which have made advanced optimization techniques more accessible to the broader scientific community. These tools reflect his philosophy of bridging theoretical optimization with practical computational implementation.

• Numerical Optimization
• Nonlinear Programming
• Numerical Linear Algebra
• +۴ مورد دیگر

Yuntong She is a Professor in the Department of Civil and Environmental Engineering at the University of Alberta's Faculty of Engineering, holding this position since 2024 after progressing from Assistant Professor (2012-2019) to Associate Professor (2019-2024). His research focuses on computational hydraulics with specialization in cold regions engineering and infrastructure resilience. His educational background includes: Ph.D. in Civil & Environmental Engineering, University of Alberta (2004-2008) M.Sc. in Hydraulic Engineering, Tsinghua University (2000-2003) B.Sc. in Hydraulic Engineering, Tsinghua University (1996-2000) Dr. She's research centers on river ice engineering, freeze-up/breakup processes, and ice jam flood forecasting, with additional expertise in sediment transport, urban drainage systems, and oil pipeline integrity. His work combines advanced computational modeling with field data analysis to address hydraulic challenges in cold climates, particularly focusing on infrastructure protection and climate change adaptation. Recent projects demonstrate strong integration of machine learning techniques for ice quantification and transient analysis for pipeline monitoring. Analysis of his 15 most recent publications (2019-2023) reveals three dominant research thrusts: (1) river ice dynamics and flood forecasting (40% of publications), particularly ice jam formation and breakup initiation; (2) pipeline safety and leak detection using transient analysis methods (25%); and (3) renewable energy system performance in cold climates, especially snow impacts on solar panels (15%). His work consistently bridges theoretical modeling with practical engineering applications in Canadian river systems. His scientific recognition includes: Editor's Choice award from Science of the Total Environment (2020) CGU-HS CRIPE Gerard Medal for river ice research (2009) Dr. She actively supervises graduate students, as evidenced by multiple publications with student first authors across river ice, pipeline, and renewable energy topics. His teaching portfolio includes core courses CIV E 431 Water Resources Engineering, CIV E 395 Civil Engineering Analysis III, and specialized CIV E 636 River Ice Engineering. Research collaborations span multiple Canadian institutions with field studies concentrated in the Mackenzie Delta and North Saskatchewan River systems. His laboratory work involves computational modeling of ice processes and pipeline systems, utilizing both proprietary simulation tools and public-domain models like River1D. Current projects focus on climate change impacts on river ice regimes and machine learning applications for real-time infrastructure monitoring in cold regions.

• Computational Hydraulics
• River Ice Engineering
• Freeze-up and Breakup Processes
• +۵ مورد دیگر