Find Academic Supervisors

Kai Xu is a Morrey Visiting Assistant Professor in the Mathematics department at the University of California, Berkeley, mentored by Richard Bamler. Appointed in 2025, he holds a PhD from Duke University supervised by Hubert Bray. His research addresses foundational problems at the intersection of differential geometry and analysis. His educational background includes: PhD in Mathematics, Duke University (2025), supervised by Hubert Bray Xu's research spans geometric analysis, calculus of variations, and metric geometry with concentrated focus on 3D scalar curvature geometry, weak inverse mean curvature flow, nonlinear potential theory (p-harmonic functions for $1 \leq p \leq \infty$), and spectral Ricci curvature bounds. His work systematically explores connections between curvature constraints, topological properties, and geometric flows through rigorous analytical methods. His publication record (2022-2025) reveals consistent advancement in scalar curvature theory, inverse mean curvature flow, and spectral Ricci geometry. Key contributions include spectral splitting theorems, drawstring constructions for scalar curvature constraints, and topological gap theorems for positive scalar curvature 3-manifolds. His collaborative work with leading mathematicians appears in journals including Duke Mathematical Journal and Calculus of Variations and Partial Differential Equations. No scientific awards are mentioned in the provided text. Teaching responsibilities include Math 104 in Fall 2025. Information regarding student advising and grant funding is not specified in available materials. No dedicated laboratory or research team structure is described in the source text.

• Geometric Analysis
• Calculus of Variations
• Metric Geometry
• +4 more

Ali Feizmohammadi is an Assistant Professor, Teaching Stream (LTA) in the Department of Mathematics at the University of Toronto Mississauga, affiliated with the Mathematical and Computational Sciences division. His research focuses on inverse problems, partial differential equations, and geometric analysis. He holds a position emphasizing teaching excellence within the university's framework. His work addresses advanced mathematical challenges such as coefficient identification in subdiffusion equations, fractional Laplacian problems on Riemannian manifolds, and nonlinear elliptic equations on manifolds. Recent articles highlight contributions to the Calderón problem in various contexts, wave equation control, and spacetime finite element methods. No scientific awards or grants are explicitly listed in the provided information. He has not yet listed advisees in the available data. His research trends emphasize rigorous mathematical analysis of inverse problems in both classical and fractional PDE frameworks, with applications to geometric and control-theoretic questions. Dr. Feizmohammadi's work spans theoretical advancements in inverse problems, numerical methods for control systems, and the interplay between differential geometry and PDEs. His contributions address both fundamental theory and applied methodologies in mathematical physics and engineering.

• Inverse Problems
• Partial Differential Equations
• Mathematical Physics
• +3 more

Rima Alaifari is an Assistant Professor for Applied Mathematics at ETH Zürich, specializing in inverse problems, applied harmonic analysis, and scientific machine learning. She is an associated member of the ETH AI Center and will assume a full professorship at RWTH Aachen University in 2025. Her work focuses on stability analysis, regularization, and operator learning, with applications in phase retrieval and robustness of neural networks. Alaifari holds a Ph.D. in Mathematics from Vrije Universiteit Brussel (2014), where she studied under Prof. Ingrid Daubechies and Prof. Michel Defrise. She completed her M.Sc. in Applied and Industrial Mathematics at Johannes Kepler University, Linz (2010). Her academic career includes postdoctoral fellowships at ETH Zurich (2014–2016) and a Marie Curie-funded position (2016). Her research interests span inverse problems, phase retrieval, stability in machine learning, and operator learning. Notable projects include SNF-funded work on phase retrieval (2019) and collaborations on adversarial robustness in medical imaging (e.g., CT reconstruction). She has mentored PhD students Tandri Gauksson and Matthias Wellershoff, and postdocs Francesca Bartolucci (now at TU Delft) and Jesse Railo (Finnish Inverse Prize winner). Teaching includes courses on inverse problems, time-frequency analysis, and robustness of deep neural networks. She actively participates in international conferences, delivering plenary talks at venues like the International Conference on Computational Harmonic Analysis (2022) and ICERM (2023). Her work bridges mathematical foundations and practical applications, emphasizing stability and robustness in computational methods.

• Inverse Problems
• Applied Harmonic Analysis
• Machine Learning
• +3 more

Rima Alaifari is currently an Assistant Professor for Applied Mathematics at ETH Zürich , where she works on applied analysis, inverse problems, and scientific machine learning. Her research emphasizes stability analysis and regularization of inverse problems, applied harmonic analysis, phase retrieval, and operator learning. She is an associated member of the ETH AI Center and will transition to a full professorship at RWTH Aachen University in 2025 as Chair of Analysis and its Applications. Education : PhD in Mathematics (2010–2014, Vrije Universiteit Brussel); MSc in Applied and Industrial Mathematics (2005–2010, Johannes Kepler University) Research Focus : Stability estimates for inverse problems, phase retrieval in wavelet/Gabor transforms, operator learning with neural networks, and deep learning robustness. Article Trends : Her recent work bridges harmonic analysis with machine learning, focusing on phase retrieval stability, adversarial perturbations in imaging, and mathematically grounded neural operator frameworks like ReNO and CNO. Advising : She has supervised PhD students like Tandri Gauksson and Matthias Wellershoff. Former postdoctoral researchers include Francesca Bartolucci (now at TU Delft) and Jesse Railo (Finnish Inverse Prize winner).

• Applied Analysis
• Inverse Problems
• Scientific Machine Learning
• +7 more

Qin Li is an Associate Professor in the Mathematics Department at the University of Wisconsin-Madison. She holds affiliations with the Wisconsin Institutes for Discovery and serves as a senior PI at the Institute for Foundations of Data Science. Her research focuses on numerical analysis, scientific computing, and inverse problems, with a strong emphasis on kinetic theory and multiscale PDEs. Her work spans computational methods for inverse transport and radiative transfer equations, Bayesian approaches in optical tomography, and optimization techniques for solving stochastic and deterministic PDEs. Recent publications highlight applications of diffusion models, Wasserstein gradient flow, and random sampling in inverse problems, as well as control theory for Vlasov-Poisson systems and reconstruction of chemotaxis kernels. She leads a research group within the Mathematics Department and has received funding from the National Science Foundation (NSF), the Office of Naval Research (ONR), and the Wisconsin Alumni Research Foundation (WARF). Her lab, Kinetic At Madison, explores nonlinear hyperbolic PDEs and their applications. She also contributes to teaching as a TA Supervisor.

• Numerical Analysis
• Scientific Computing
• Inverse Problems
• +8 more

Eitan Tadmor is a Distinguished University Professor at the Department of Mathematics and Institute for Physical Science & Technology at the University of Maryland. He holds the 2024 Chaire d'excellence at Sorbonne University's Fondation Sciences Mathématiques de Paris, and has served as Director of multiple research centers including the Center for Scientific Computation and Mathematical Modeling (2002-2016) and The Sackler Institute of Scientific Computation (1993-1996). Current: University of Maryland (2005-present) Previous: UCLA (1995-2002), Tel-Aviv University (1989-1995), CalTech (1980-1982) His research spans nonlinear conservation laws , entropy-stable schemes , collective dynamics , spectral methods , and multiscale modeling . He pioneered the spectral viscosity method and developed stability criteria for numerical schemes. Recent publications focus on swarm-based optimization , Euler-Poisson equations , and hydrodynamic alignment with over 15000 citations. His work on kinetic formulations and regularizing effects in PDEs has become foundational in computational mathematics. 2022 Norbert Wiener Prize (AMS-SIAM) 2022 Gibbs Lecturer (AMS) 2015 Peter Henrici Prize (SIAM-ETH) 2013-2021 Fellow of AMS/SIAM NSF grants (1999, 2008-2012, 2012-2020) He developed CentPack software for hyperbolic conservation laws and co-authored influential review papers on numerical methods and mathematical modeling. His collaborative work with institutions like IPAM, KI-Net, and ETH-ITS demonstrates international scientific leadership.

• Nonlinear Conservation Laws
• Spectral Methods
• Kinetic Theory
• +5 more

June-Yub Lee is a Professor of Mathematics and Vice President at Ewha Womans University, leading the Office of University Planning and Coordination. He is also a member of the Institute of Mathematical Sciences. His academic background includes a Physics major and Mathematics minor from KAIST, followed by a Ph.D. in Mathematics from New York University's Courant Institute. Prof. Lee specializes in numerical analysis, inverse problems, computational fluid dynamics, and phase-field modeling. His research focuses on developing robust numerical methods for partial differential equations, with applications in materials science and fluid dynamics. Notable contributions include work on operator splitting methods, energy-stable schemes for phase-field models, and inverse problem solutions in electrical impedance tomography. He has organized major international conferences like ICM2014 and ICIP series, and serves on editorial boards of journals such as the Journal of the Korean Society for Industrial and Applied Mathematics (KSIAM). His work bridges theoretical mathematics with computational science, emphasizing interdisciplinary applications. Prof. Lee has actively contributed to academic administration, including serving as Director of the Division of Natural Sciences at the National Research Foundation (NRF). His research lab focuses on scientific computation, with recent projects exploring long-time simulations of phase-field crystal models and high-order numerical methods for multiphase systems.

• Numerical Analysis
• Inverse Problems
• Computational Fluid Dynamics
• +3 more

Prof. Dr. Irwin Yousept is a Full Professor of Mathematics at Universität Duisburg-Essen, leading the research group AG Optimal Control of Partial Differential Equations. His work focuses on the mathematical analysis and numerical solutions of electromagnetic problems, particularly in superconductivity and inverse problems. He holds a PhD from TU Berlin (2008) and has held academic positions at TU Darmstadt and TU Berlin. His research includes PDE-constrained optimization, numerical analysis, and applications in high-temperature superconductivity and electromagnetic shielding. Affiliations: Universität Duisburg-Essen, Fakultät für Mathematik Education: Diplom (2005), PhD (2008) in Mathematics from TU Berlin Research interests span Maxwell's equations, numerical methods for PDEs, and optimal control, with applications in superconductivity, electromagnetic shielding, and induction heating. He has authored over 40 publications and received awards including the Richard-von-Mises-Preis GAMM (2014). Current grants include DFG-funded projects on inverse problems and superconductivity.

• Electromagnetics
• Numerical Analysis
• Optimization
• +3 more

Ameya Jagtap is an Assistant Professor (Tenure-Track) in the Department of Aerospace Engineering at Worcester Polytechnic Institute (WPI), USA. Prior to this, he served as an Assistant Professor of Applied Mathematics (Research) at Brown University from 2021 to 2024. He holds a Ph.D. and M.E. in Aerospace Engineering from the Indian Institute of Science (IISc), and completed postdoctoral research at TIFR-CAM (India) and Brown University's Division of Applied Mathematics. His research bridges mechanical/aerospace engineering, applied mathematics, and computation, focusing on scientific machine learning algorithms that integrate data and physics. Key areas include physics-driven deep learning, uncertainty quantification, multi-scale simulations, and novel neural network architectures like quantum and graph networks. He serves on editorial boards for Neural Networks , Neurocomputing , and others. His work emphasizes interpretable neural operators for PDE solutions, domain decomposition methods, and adaptive activation functions to enhance PINN convergence. Notable contributions include XPINNs (extended physics-informed neural networks) and causal sweeping frameworks for PDEs. His research has been widely cited, particularly for PINN applications in supersonic flows and high-dimensional PDEs. Jagtap has delivered invited talks at institutions like Los Alamos National Laboratory, Tsinghua University, and the Alan Turing Institute. He is also recognized as a Top 2% World Scientist by Stanford University.

• Scientific Machine Learning
• Deep Learning
• Computational Physics
• +8 more

Lina von Sydow is a Professor in Computational Science at Uppsala University's Department of Information Technology. She serves as Section Dean for the Mathematical-Computer Science Section since July 2023. Her academic journey includes becoming an Associate Professor in 2000, Senior Lecturer since 1997, and leading the Department of Information Technology from 2018 to 2023. PhD in Domain Decomposition Methods (1995, Uppsala University) Postdoctoral Fellow at Oxford University (1996-1997) Her research spans computational science with dual focuses on Computational Finance and Ice Sheet Modeling . In finance, she develops numerical methods for option pricing using PDEs, radial basis functions, and stochastic volatility models. In climate science, she contributes to ice sheet dynamics through full Stokes models and adaptive time-stepping approaches, particularly in simulating grounding line migration. Recent publications (2025) address gender disparities in IT education, including comparative analysis of admission trends and intervention studies to boost female enrollment. Earlier works (2020-2015) focus on high-order finite difference methods for financial derivatives, BENCHOP benchmarking projects, and preconditioning techniques for PDEs. Scientific awards include Excellent Teacher (2013) She actively collaborates on educational reforms, co-authoring studies like Gender-aware course reform in Scientific Computing (2013). Her leadership roles include Head of Department (2018-2023) and Section Dean (2023-present), influencing academic governance and interdisciplinary research. Labs and teams: Works with Uppsala University's Computational Science group, Elmer/ICE project collaborators (e.g., Per Lötstedt, Gong Cheng), and international partners in numerical finance and climate modeling.

• Computational Science
• Computational Finance
• Ice Sheet Modeling
• +2 more

Prof. Boris Vexler, born in 1977 in Moscow, Russia, is a Professor of Optimal Control at the TUM Department of Mathematics under the TUM School of Computation, Information and Technology . He has served as Dean of Studies since 2015 and as speaker of the International Research Training Group IGDK 1754 since 2012. Education: Diploma (2000) and Ph.D. (2004) in Mathematics from the University of Heidelberg; Habilitation (2008) from the University of Graz. His research focuses on numerical analysis of partial differential equations (PDEs) , particularly finite element methods for optimal control problems governed by parabolic, elliptic, and hyperbolic PDEs. Key contributions include error estimates , adaptive discretization , and handling state constraints and measure-valued controls . Recent publications emphasize transient Stokes equations , Navier-Stokes control , and sparsity-constrained optimization . His work often integrates scientific computing and uncertainty quantification . Scientific Awards: Award for best supervisor of the elite degree program TopMath (2018) Finalist, ECCOMAS Prize for best dissertation (2005) Leslie Fox Prize in Numerical Analysis, 2nd place (2004)

• Numerical Analysis
• Optimal Control
• Partial Differential Equ as Mathematics (summa cum laude)
• +10 more

Hong Wang is a Professor in the Department of Mathematics at the University of South Carolina, part of the McCausland College of Arts and Sciences. He specializes in numerical analysis and differential equations, with a focus on numerical methods for fractional and variable-order equations. His work addresses complex boundary conditions, optimal control, and scientific computing challenges in advection-diffusion systems. Education Ph.D. in Mathematics, University of Wyoming (1992) Research Interests His research emphasizes numerical approximation techniques for differential/integral equations, particularly fractional diffusion-wave equations, variable-order models, and stochastic systems. Key areas include finite element methods, spectral methods, and fast algorithms for solving high-dimensional and time-dependent problems. He explores applications in optimal control, viscoelasticity, and multi-scale modeling. Recent Work Trends Recent publications highlight advancements in fractional calculus applications, including variable-exponent diffusion, distributed-order equations, and stochastic fractional differential equations. His work often combines theoretical analysis with computational efficiency, addressing challenges like nonsmooth parameters and singular density functions. Grants and Advising No specific grants or advisees are listed, but his research collaborations span computational mathematics, applied physics, and engineering systems. Labs/Teams No dedicated lab or team is explicitly mentioned, though his research aligns with computational and applied mathematics groups at the University of South Carolina.

• Numerical Analysis
• Differential Equations
• Fractional Calculus
• +3 more

Suchuan Dong is a Professor in the Department of Mathematics at Purdue University , affiliated with the Center for Computational and Applied Mathematics . His work bridges Computational Mathematics and Machine Learning , focusing on High-Order Numerical Methods and Multiphase Flows . Academic Background Post-Doc in Applied Mathematics, Brown University (2004) Ph.D. in Mechanical Engineering, SUNY Buffalo (2001) M.S. in Physics, Zhejiang University (1995) B.S. in Aerospace Engineering, National University of Defense Technology (1992) His research centers on Neural Network-Based Numerical Methods and Data-Driven Scientific Computing , with applications to Computational Fluid Dynamics , Contact Line Dynamics , and High-Performance Computing . Publications highlight Physics-Informed Neural Networks , Energy-Stable Schemes , and Extreme Learning Machines for PDEs. The articles reflect trends in Neural Network Applications to Dynamic PDEs , Phase Field Modeling , and High-Dimensional Computing , often combining High-Order Numerical Methods with Interfacial Phenomena . His recent work focuses on Exact Time Integration Algorithms and Hidden-Layer Concatenation for stability and efficiency. He leads research in the Center for Computational and Applied Mathematics , emphasizing Thermodynamically Consistent Modeling and Flow-Structure Interactions . His teaching includes MA-36600: Ordinary Differential Equations (Spring 2025).

• Computational and Applied Mathematics
• High-Order Numerical Methods
• Multiphase Flows
• +7 more

Alexandre Ern is a Senior Researcher at CERMICS (École des Ponts ParisTech) and INRIA Paris (SERENA team), where he has contributed since 1995 and 2016, respectively. He holds a professorship at École des Ponts (since 1997) and previously served as Associate Professor at École Polytechnique (2010–2022). Since 2015, he heads the Master's program in Applied Mathematics at École des Ponts. Ern is Co-Editor-in-Chief of the IMA Journal of Numerical Analysis (since 2024) and Associate Editor for multiple top journals including SIAM Journal on Scientific Computing and ESAIM Mathematical Modelling and Numerical Analysis. His research focuses on numerical methods (finite elements, discontinuous Galerkin, hybrid high-order schemes), a posteriori error estimation , and applications in fluid/solid mechanics and environmental flows (hydrology, porous media). He develops structure-preserving discretizations for complex PDEs and explores computational geosciences and wave propagation. Ern's publications emphasize robust error analysis , high-order discretizations , and computational efficiency for elliptic, parabolic, and hyperbolic systems. Recent work explores hybrid high-order methods for wave equations, Maxwell's equations, and interface problems, often leveraging polynomial-degree-robust techniques and unfitted meshes. Scientific Awards: Paul Caseau Prize (2020) Nominee, AMIES Prize (2020) ENPC Best PhD Award (supervision) UPE Best PhD Award (supervision) Frontiers of Science Award, Int. Congress Basic Science (2024) He actively advises PhD students (31+ supervised) and postdocs, with projects funded by industrial partners including CEA, EDF, and Safran. His team collaborates with CERMICS and INRIA's SERENA lab, focusing on computational mechanics, model reduction, and large-scale fracture networks.

• Numerical Analysis
• Finite Element Methods
• Computational Mechanics
• +5 more

Gui-Qiang G. Chen is a Professor at the Mathematical Institute , University of Oxford, and a Professorial Fellow of Keble College. He serves as Director of the Oxford Centre for Nonlinear Partial Differential Equations (OxPDE) , focusing on nonlinear PDEs, hyperbolic conservation laws, and their applications to fluid dynamics, geometry, and mathematical physics. His research spans Partial Differential Equations , Nonlinear Analysis , Shock Wave Theory , and Free Boundary Problems , with recent work on stochastic PDEs , geometric PDEs , and numerical analysis . His publications cover topics like transonic shocks , hypersonic flow , and compressible fluid dynamics . Gui-Qiang Chen has co-authored 15+ major publications since 2007, including research monographs on shock reflection-diffraction and Prandtl-Meyer reflection configurations , and his work appears in leading journals like Annals of Mathematics and Communications on Pure and Applied Mathematics . Awards and fellowships include: 2024 Polya Prize (London Mathematical Society) Doctor of Science (Oxford, 2024) Member of Academia Europaea (2022) Member of the European Academy of Sciences (2020) Fellow of the American Mathematical Society (2017) SIAM Fellow (2013) Chinese National Prize of Sciences (1990) He supervises DPhil/PhD students in nonlinear PDEs and related fields, and his research is supported by Oxford's mathematical infrastructure and international collaborations.

• Partial Differential Equations
• Nonlinear Analysis
• Hyperbolic Conservation Laws
• +12 more