Variational PDE Models Professors and Supervisors | ApplySharif

جست‌وجوی استاد و پژوهشگر

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

نمایش ۳۱-۴۵ از ۷۷۴ استاد راهنما

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

Ankush Agarwalمشاهده پروفایل

دانشیار

Western UniversityCanada

Ankush Agarwal is an Associate Professor in the Department of Statistical and Actuarial Sciences at the University of Western Ontario. His research focuses on mathematical finance, financial statistics, and Monte Carlo methods, with applications to risk management and derivatives pricing. He supervises PhD students in quantitative finance and has taught courses on Monte Carlo methods and advanced financial modeling at Western University. Education: PhD in Mathematics from Tata Institute of Fundamental Research (2015) Research interests span regime-switching models, longevity risk hedging, stochastic differential equations, and rare event simulation. His work combines theoretical probability with computational techniques for financial applications. Recent publications include studies on McKean-Vlasov SDEs, implied Sharpe ratio estimation, and optimal portfolio strategies under stochastic volatility. These works demonstrate his expertise in stochastic processes and financial engineering. Supervision: Current PhD advisees include Ying Liao, Buchun Wang, and Shuya Zhang at the University of Glasgow. Former advisees include Yongjie Wang and Yihan Zou.

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Daniele Venturiمشاهده پروفایل

استاد

University of California, Santa CruzUnited States

Daniele Venturi is a Professor of Applied Mathematics at the University of California, Santa Cruz, where he has been faculty since 2015, rising from Assistant Professor to full Professor by 2021. Previously, he was a Research Assistant Professor at Brown University from 2010-2015. His academic journey began at the University of Bologna, where he earned both his combined B.S./Sc.M. in Mechanical Engineering (2002) and Ph.D. in Applied Physics with a focus on thermo-fluid dynamics (2006). University of Bologna: B.S./Sc.M. Mechanical Engineering (2002), Ph.D. Applied Physics (2006) Brown University: Research Assistant Professor (2010-2015) UC Santa Cruz: Assistant to Associate to Full Professor (2015-present) Professor Venturi's research spans multiple cutting-edge areas in computational mathematics. His primary interests include stochastic modeling and uncertainty quantification, numerical tensor methods for high-dimensional PDEs, data-driven modeling approaches, approximation of functional-differential equations, and theoretical/computational fluid dynamics. His work bridges theoretical mathematical frameworks with practical computational implementations, particularly focusing on overcoming the curse of dimensionality in complex systems. His recent research has been heavily focused on hierarchical tensor methods for solving high-dimensional partial differential equations. The analysis of his publication record reveals a strong emphasis on developing computational frameworks that address high-dimensional challenges in uncertainty quantification and model reduction. His work frequently intersects machine learning techniques with traditional numerical methods, particularly in developing physics-informed neural networks and multifidelity modeling approaches. A consistent theme across his publications is the development of mathematical frameworks that maintain computational tractability while preserving physical fidelity in complex systems. Professor Venturi has secured substantial research funding from major agencies including the Air Force Office of Scientific Research (AFOSR), Department of Energy (DoE), National Science Foundation (NSF), Army Research Office (ARO), and Defense Advanced Research Projects Agency (DARPA). His most significant current grant is a 2024-2029 AFOSR MURI award totaling $7.5M as co-PI for 'Tensor Network for simulating kinetic systems.' 2024-2029: AFOSR MURI, $7.5M (co-PI) 2023-2027: DoE, $3.8M (co-PI) 2023-2026: AFOSR, $2.5M (co-PI) 2020-2025: NSF TRIPODS, $2.3M (co-PI) At UC Santa Cruz, Venturi teaches a range of courses including Fundamentals of Uncertainty Quantification, Applied Dynamical Systems, Nonlinear Dynamical Systems, and Numerical Methods for Differential Equations. His teaching spans both undergraduate and graduate levels, reflecting his expertise across theoretical and computational mathematics. His lecture notes for these courses are publicly available and demonstrate his commitment to pedagogical excellence in complex mathematical subjects.

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Gerhard Huiskenمشاهده پروفایل

استاد

University of TübingenGermany

Prof. Dr. Gerhard Huisken is a Professor at Eberhard Karls University of Tübingen and Director of the Mathematical Research Institute Oberwolfach. His research focuses on geometric analysis, differential geometry, and mathematical relativity, with significant contributions to mean curvature flow, Ricci flow, and geometric evolution equations. He has authored numerous influential papers on topics such as curvature flows, singularity analysis, and applications to general relativity. Key positions include leadership at Oberwolfach and teaching roles in advanced courses like 'Mathematical Relativity' and 'Introduction to Ricci Flow'. His work bridges geometric analysis with physics, contributing to the Poincaré conjecture through Ricci flow studies. Collaborations include projects with S. Brendle and C. Sinestrari on convex solutions and flow surgeries. Research highlights include the Riemannian Penrose inequality, inverse mean curvature flow, and long-term behavior of geometric flows. His academic contributions are documented in top journals like Inventiones mathematicae and Journal of Differential Geometry .

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Robin Neumayerمشاهده پروفایل

استادیار

Carnegie Mellon UniversityQatar

Robin Neumayer is an Assistant Professor in the Department of Mathematical Sciences at Carnegie Mellon University. Her research focuses on the intersection of calculus of variations, partial differential equations (PDE), and geometric analysis, with a particular emphasis on stability and regularity in geometric inequalities. Education: Ph.D. in Mathematics, University of Texas at Austin, supervised by Alessio Figalli and Francesco Maggi. Her work explores problems related to Sobolev inequalities, isoperimetric problems, scalar curvature, and free boundary phenomena. Recent publications highlight collaborations with leading researchers and address topics such as quantitative stability, anisotropic geometries, and nonlinear PDE. Scientific Awards and Fellowships: NSF Grant DMS-2155054 (2022-2025) RTG Postdoctoral Fellow at Northwestern University (2017-18, 2019-21) Institute for Advanced Study member (2018-19) She teaches courses such as Introduction to Differential Equations and maintains active research collaborations with institutions like the Center for Nonlinear Analysis.

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Boris Buffoniمشاهده پروفایل

مدرس ارشد

Swiss Federal Institute of Technology in LausanneSwitzerland

Boris Buffoni is a Senior Lecturer at École Polytechnique Fédérale de Lausanne (EPFL) in the School of Basic Sciences, Institute of Mathematics, specifically within the Chair of Partial Differential Equations. He maintains his office at MA C2 605 (MA Building), Station 8, 1015 Lausanne, Switzerland, and can be contacted at boris.buffoni@epfl.ch or +41 21 693 49 87. His academic role spans both teaching responsibilities across multiple mathematics programs and active research in theoretical and applied mathematics. Dr. Buffoni's research program centers on the calculus of variations applied to Lagrangian and Hamiltonian systems, with significant contributions to optimal transportation in Lagrangian dynamics and hydrodynamics. His work explores semi-global minimization methods for quasi-linear elliptic variational problems and the variational approach to capillary-gravity water waves and their energetic stability. Additional research foci include local bifurcation and center-manifold theory for elliptic PDEs, the configurations of infinite elastic cylinders under compression or traction, and the analytic theory of global bifurcation with applications to gravity waves and their secondary bifurcations. The trajectory of his recent publications reveals a deepening focus on three-dimensional water wave phenomena, particularly steady rotational flows, gravity-capillary solitary waves, and advanced mathematical techniques for analyzing these complex systems. His 2025 publications demonstrate continued innovation in applying Kato's approach to locally coercive problems and developing the theoretical foundations of global bifurcation. The consistent application of variational methods and bifurcation theory across his work represents a unifying theme in addressing challenging problems in fluid dynamics and nonlinear partial differential equations. Dr. Buffoni has received research support including an EPSRC grant (GR/L41059) for work on 'Multibump localised solutions for spatially homogeneous partial differential equations,' reflecting the significance of his contributions to the field. His teaching portfolio at EPFL includes foundational courses such as Analysis II, Functional Analysis I, and Partial Differential Equations of Evolution, where he imparts knowledge of differential and integral calculus of real functions of several variables, linear functional analysis, and fundamental techniques for solving evolution equations.

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Karsten Urbanمشاهده پروفایل

استاد

Ulm UniversityGermany

Prof. Karsten Urban is a Full Professor of Numerical Mathematics at the University of Ulm, leading the Institute for Numerical Mathematics. He holds roles such as Dean of Studies in Computational Science and Engineering (CSE) and Deputy Spokesman for the Research Association for Scientific Computing in Baden-Württemberg. He is an active member of prestigious societies including the Deutsche Mathematikervereinigung (DMV) and SIAM. His academic journey includes a PhD from RWTH Aachen (1995), Habilitation (2001), and a full professorship at Ulm since 2005. Research focuses on numerical methods for PDEs, reduced basis techniques, multiscale simulations in fluid mechanics, biomechanics, quantum sciences, and financial mathematics. He has pioneered wavelet-based methods and collaborated with industries on ship propulsion and energy trading models. His work integrates mathematical rigor with real-world applications, emphasizing model reduction and computational efficiency. Editorial Roles: Managing Editor of Advances in Computational Mathematics , Editor of SN Partial Differential Equations and Applications . Awards: Teaching award of Baden-Württemberg (2005), Science-Economy Cooperation Awards (2004, 2008). Administrative Roles: Member of the University Council and ASIIN expert committee. Supervises doctoral students in numerical analysis, quantum simulations, and biomechanics. Active in interdisciplinary projects, including quantum systems (IQST) and fracture healing modeling in collaboration with biomechanics experts. His contributions bridge academia and industry, driving innovation in computational methods.

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Mauro Werderمشاهده پروفایل

مدرس

ETH ZurichSwitzerland

Dr. Mauro Werder is a Lecturer at the Department of Civil, Environmental and Geomatic Engineering at ETH Zurich. His work focuses on glaciology, subglacial hydrology, and numerical modeling, combining computational methods with field measurements. He has developed widely used models such as GlaDS (Glacier Drainage System) and BITE (Bayesian Ice Thickness Estimation), and contributed to projects like SHMIP and 4D-Antarctica. Current Projects: Gladder (2025-2028), DIWING (2023-2026), LEAD (2020-2026), 4D-Antarctica (2019-2022), CORDS (2023-2024) Education: PhD in Glaciology (2009, Swiss National Science Foundation funded) His research spans subglacial drainage systems, sediment transport (SUGSET model), Bayesian inversion techniques, and field experiments involving artificial lakes and R-channels. He actively teaches courses on GPU-based PDE solving, applied glaciology, and reproducible scientific computing. Scientific Awards: Swiss National Science Foundation (SNF) Fellowship for Prospective Researchers (2010-2011) European Union (FP7) Marie Curie International Outgoing Fellowship (2011-2014) He collaborates with institutions like the Swiss Federal Institute for Forest, Snow and Landscape Research (WSL), and contributes to software development through packages like BITEmodel.jl and Parameters.jl. His fieldwork includes experiments on Greenland's Jakobshavn Isbræ and Switzerland's Plaine Morte glacier.

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Caterina Ida Zeppieriمشاهده پروفایل

استاد

University of MünsterGermany

Professor Caterina Ida Zeppieri is a distinguished mathematician at the Westfälische Wilhelms-University Münster (University of Münster) in Germany, where she leads the Research Group 'Analysis and Modelling' within the Institute for Analysis and Numerics. She has maintained a continuous academic presence since at least the Winter semester 2012/13 through to upcoming semesters in 2025/26, consistently teaching advanced mathematics courses and supervising research activities. Her research focuses on fundamental aspects of mathematical analysis with significant applications to materials science. She specializes in Calculus of Variations, Elliptic PDEs, Gamma-convergence, Homogenization theory, Free-discontinuity problems, Nonlinear elasticity, and Plasticity. Her work bridges theoretical mathematics with practical applications in understanding material behavior, particularly fracture mechanics and composite materials. Professor Zeppieri's publication record demonstrates a consistent and impactful research trajectory from 2007 through forthcoming publications in 2025. Her recent work shows a strong emphasis on stochastic homogenization techniques applied to free-discontinuity problems and singularly-perturbed functionals, revealing sophisticated mathematical approaches to modeling complex material behaviors across multiple scales. She regularly collaborates with leading researchers including Filippo Cagnetti, Gianni Dal Maso, and Lucia Scardia, contributing to significant advances in the mathematical understanding of material science phenomena. Her research has been published in top-tier mathematics journals including Calculus of Variations and Partial Differential Equations, Archive for Rational Mechanics and Analysis, and SIAM Journal on Mathematical Analysis. Within the department, Professor Zeppieri plays an active role in teaching advanced courses such as Partial Differential Equations, Calculus of Variations, and Advanced Topics in the Calculus of Variation, while participating in the department's Advanced Seminar in Applied Mathematics and Colloquium on Applied Mathematics.

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Georg Stadlerمشاهده پروفایل

استاد

New York UniversityUnited States

Georg Stadler is a Professor of Mathematics and Computer Science at New York University's Courant Institute. His research focuses on computational inverse problems, uncertainty quantification, and PDE-constrained optimization, driven by applications in climate modeling, geophysics, and plasma physics. He holds a PhD from the University of Graz (2004) and has been recognized with awards including the Gordon Bell Prize (2015) and the SIAM Computational Science & Engineering Best Paper Prize (2019). Education: Ph.D. (Dr.), Mathematics, University of Graz, Austria, 2004. M.S. (Mag.), Mathematics, University of Graz, Austria, 2001. M.S., Mathematics and Geometry Education, Graz University of Technology and University of Graz, 2001. Research Interests: Large-scale PDE solvers, Bayesian inverse problems, extreme event probability estimation, and optimization under uncertainty. Applications in climate (sea/land ice, tsunamis), plasma physics (fusion), and computational earth science (mantle flow, plate tectonics). Recent Research Trends: His work emphasizes scalable algorithms for high-dimensional Bayesian inverse problems, with applications to tsunamis, stellarator coil design, and ice sheet dynamics. Recent articles highlight advancements in extreme event probability estimation and robust multigrid solvers for incompressible Stokes equations. Awards: Gordon Bell Prize (2015) for extreme scalability of implicit solvers. SIAM Best Paper Prize (2019) for computational science contributions. Young Scientist ASCINA Award and Springer CSE Prize (2011). Advising & Grants: Current PhD student Sonia Reilly and former advisees include Chen Li and Shanyin Tong. His research is supported by NSF, ONR MURI, and the Simons Foundation. He co-leads the Computational Mathematics and Scientific Computing Seminar at Courant. Labs & Collaborations: Active in Courant’s interdisciplinary groups, focusing on high-performance computing and inverse problems. Collaborates with institutions like UT Austin on mantle dynamics and fusion energy projects.

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Giang Tranمشاهده پروفایل

دانشیار

University of WaterlooCanada

Dr. Giang Tran is an Associate Professor in the Department of Applied Mathematics at the University of Waterloo, where she leads research in sparse modeling and computational mathematics. She holds a PhD from UCLA and previously served as a Bing Instructor at the University of Texas at Austin. Her research explores sparse optimization techniques with applications in medical imaging, dynamical systems, and data science. Recent publications focus on developing novel algorithms for sparse random feature expansions and dynamical system identification. She mentors numerous graduate and undergraduate researchers through projects on neural networks, transformers, and epidemic forecasting. Awards include the NSERC Discovery Grant and SIAM Student Paper Prize. Dr. Tran teaches advanced courses in numerical methods and functional analysis, contributing to curriculum development in computational mathematics.

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Celia Reinaمشاهده پروفایل

دانشیار

University of PennsylvaniaUnited States

Celia Reina is an Associate Professor in the Department of Mechanical Engineering and Applied Mechanics at the University of Pennsylvania’s School of Engineering and Applied Science (SEAS). Her research focuses on multiscale modeling of materials, bridging statistical mechanics, thermodynamics, and machine learning. She develops novel frameworks for predicting non-equilibrium material behavior using data-driven methods and uncertainty quantification. Her work emphasizes integrating computational tools like neural networks (Stat-PINNs, VONNs) with physical principles to model dissipative systems, phase transitions, and mesoscale dynamics. Key areas include coarse-graining techniques, epistemic uncertainty analysis, and predictive modeling of complex materials under dynamic loading. Recent publications highlight advancements in stochastic systems, resonant metamaterials, and the derivation of thermodynamic models from particle-level fluctuations. She leads efforts in experimental-simulation co-design to enhance predictive capabilities in materials science.

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Ryan Murrayمشاهده پروفایل

استادیار

North Carolina State UniversityUnited States

Ryan Murray is an Assistant Professor in the Department of Mathematics at North Carolina State University (NC State). His research focuses on developing mathematical tools to address problems in applied analysis, including calculus of variations, partial differential equations (PDEs), and their applications to machine learning, fluid dynamics, and control theory. He holds a PhD in Mathematics from Carnegie Mellon University (2016). His expertise spans regularization methods for machine learning, singular perturbations in materials science, algorithms for distributed optimization, and singularity formation in fluid dynamics. His work is supported by the National Science Foundation (NSF) and the Simons Foundation. He actively collaborates with researchers in data science, PDE analysis, and optimization. Key research areas include adversarial training in classification, geometric data analysis via statistical depths, and the analysis of vortex sheet singularities. His teaching experience includes courses on partial differential equations, optimal control theory, and linear control systems. Ryan has published extensively in journals such as SIAM Journal on Mathematics of Data Science , Archive for Rational Mechanics and Analysis , and Journal of Machine Learning Research . His articles explore topics ranging from graph-based learning to fluid dynamics instabilities.

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Scott Armstrongمشاهده پروفایل

استاد

New York UniversityUnited States

Scott Armstrong is a Professor of Mathematics at the Courant Institute of Mathematical Sciences, New York University. His research focuses on partial differential equations, calculus of variations, and probability theory, with a specialization in stochastic homogenization of PDEs in random media and related statistical mechanical systems. He holds a Ph.D. from UC Berkeley (2009) and a B.S. from Texas A&M University (2002). Education: Ph.D. in Mathematics, University of California, Berkeley, USA (2009) B.S. in Mathematics, Texas A&M University, USA (2002) Research Interests: Scott's work addresses fundamental questions in homogenization theory, including quantitative estimates for elliptic and parabolic equations in random media, renormalization group methods, and applications to statistical mechanics. His contributions bridge analysis, probability, and mathematical physics, with a focus on rigorous mathematical frameworks for understanding macroscopic behavior from microscopic models. Publications: His recent work includes studies on anomalous diffusion, renormalization group techniques, and quantitative homogenization in high-contrast media. Over 50 peer-reviewed articles highlight his expertise in stochastic PDEs, elliptic regularity, and variational methods. Awards: No specific awards listed in the provided text. Advising & Grants: No student advisees or grant details explicitly mentioned in the text. Labs/Teams: No dedicated labs or collaborative teams explicitly noted, though his research likely involves interdisciplinary collaborations within the Courant Institute.

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Borjan Geshkovskiمشاهده پروفایل

پژوهشگر

University of DeustoSpain

Borjan Geshkovski is a Researcher affiliated with the Universidad Autónoma de Madrid (UAM) under a Marie Skłodowska-Curie fellowship at the Conflex Project. He has been associated with institutions such as FAU Erlangen-Nürnberg, University of Deusto, and the DyCon team during his academic journey. PhD in Control Theory (2021, UAM) MSc in Applied Mathematics (2016–2018, University of Bordeaux) BSc in Applied Mathematics and Computer Science (2012–2016, University of Bordeaux) His research focuses on the intersection of Control Theory and Free Boundary Problems in fluid mechanics, with recent explorations into Deep Learning from a mathematical control perspective. Key contributions include work on turnpike properties, optimal actuator design, and controllability of nonlinear PDEs. Scientific awards include the Best Review and Presentation Prize at the 2nd ConFlex workshop (2019). His publications span topics like neural ODEs, porous medium flows, and obstacle problems, reflecting collaborations with the DyCon team and ConFlex consortium.

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Huan Leiمشاهده پروفایل

استادیار

Michigan State UniversityUnited States

Huan Lei is an Assistant Professor at Michigan State University, holding a joint appointment in the Department of Computational Mathematics, Science and Engineering and the Department of Statistics and Probability. He earned his Ph.D. in Applied Mathematics from Brown University in 2012 under George Karniadakis and a B.S. in Special Class for the Gifted Young from the University of Science & Technology of China in 2005. His research integrates scientific machine learning with numerical analysis to develop structure-preserving algorithms for partial and stochastic differential equations arising in multi-scale systems. His work spans multi-scale modeling , non-Markovian dynamics , coarse-grained molecular simulations , and data-driven parameterization . Recent publications focus on learning generalized Langevin equations with state-dependent memory, consensus-based free energy surfaces, and non-equilibrium coarse-grained models. His team applies these methods to fluid dynamics, biomolecular solvation, and climate systems. NSF CAREER Award (2021) Brown University Dissertation Fellowship (2012) He advises graduate and undergraduate researchers and seeks Ph.D. candidates with expertise in numerical analysis or scientific computing. His group receives funding from NSF, DOE, Ford, and MSU Foundation.

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فقط ۷۵۰ نتیجه اول نمایش داده می‌شود. برای رسیدن به نتایج دقیق‌تر، جست‌وجو را با فیلترها محدودتر کنید.