جست‌وجوی استاد و پژوهشگر

• Geometric Measure Theory
• Partial Differential Equations (PDEs)
• Calculus of Variations
• +۴ مورد دیگر

Guido De Philippis is a Full Professor at the Courant Institute of Mathematical Sciences, New York University. His research focuses on geometric measure theory, partial differential equations, and calculus of variations. He has contributed to advancing understanding in minimal surfaces, optimal transport, and free boundary problems. His work often bridges geometric analysis with nonlinear PDE theory, addressing questions of regularity, existence, and stability. Affiliations: Courant Institute of Mathematical Sciences (NYU). Education: PhD in Mathematics (2013, Sobolev regularity for Monge-Ampère equations). De Philippis’s research interests include geometric flows, spectral optimization, and the structure of measures constrained by PDEs. His papers explore topics like anisotropic minimal surfaces, regularity of free boundaries, and quantitative isoperimetric inequalities. Notable contributions include foundational work on the Monge-Ampère equation and optimal transport theory. He has been recognized with the Chaire De La Vallée Poussin Prize Lectures (2020/21) and has organized conferences in geometric analysis and PDEs. His collaborations span across institutions, reflecting his international impact in mathematics.

Shawn W. Walkerمشاهده پروفایل

استاد

• Geometric Partial Differential Equations (PDEs)
• Numerical Analysis
• Finite Element Methods (FEM)
• +۳ مورد دیگر

Shawn W. Walker is a Professor at Louisiana State University's Department of Mathematics and Center for Computation & Technology (CCT). His research focuses on Geometric Partial Differential Equations (PDEs), Numerical Analysis, and Finite Element Methods (FEM), with applications to liquid crystals and material science. He has developed the FELICITY toolbox, a MATLAB/C++ framework for solving PDEs on unstructured grids. Key research interests include modeling liquid crystal behavior, geometric evolution problems, surface FEM, and optimal control of PDEs. His work on the Ericksen model and Landau-de Gennes equations has advanced numerical methods for simulating liquid crystal equilibrium states and defects. Grants from the National Science Foundation (DMS-1115636, DMS-1418994, etc.) support his research. Recent publications explore cholesteric phases, optimal control of liquid crystal models, and surface-based finite element methods for plate equations. The FELICITY toolbox enables robust simulations of complex geometries and multiphysics systems, with applications in engineering and materials science.

George Birosمشاهده پروفایل

استاد

• Scientific Computing
• Parallel Algorithms
• Fast Numerical Algorithms
• +۵ مورد دیگر

George Biros is a Professor at The University of Texas at Austin, holding the W.A. “Tex” Moncrief Jr. Chair in Simulation-Based Engineering Sciences at the Cockrell School of Engineering’s Institute for Computational Engineering and Sciences (ICES). He leads the Parallel Algorithms for Data Analysis and Simulation (PADAS) group. His research focuses on high-performance computing, fast numerical algorithms, and applications in fluid dynamics, biomedical engineering, and inverse problems. Education: BSc, Aristotle University of Thessaloniki (1995) MSc and PhD, Carnegie Mellon University (1996–2000) Research Interests: Biros develops scalable algorithms for scientific computing, including parallel methods for integral and differential equations, complex fluids dynamics, and medical image analysis. His work bridges computational mathematics, engineering, and biomedical applications, with a focus on solving large-scale problems in heterogeneous computing environments. Awards: IEEE/ACM SC10 Gordon Bell Prize (2010) J. Tinsley Oden Faculty Fellowship (2006–2008) Early Career Young Investigator Award, U.S. Department of Energy (2005) Grants & Labs: Leads the PADAS group, which specializes in high-performance algorithms for data and simulation. His work has been supported by the DOE, NSF, and industry partnerships. Collaborates with institutions like TUM through fellowships. Labs/Teams: Director of the PADAS group at UT Austin, focusing on scalable numerical methods and their applications in science and engineering.

Jörn Behrensمشاهده پروفایل

استاد

• Numerical Methods in Geosciences
• Climate Modeling
• Tsunami Simulation
• +۵ مورد دیگر

Prof. Jörn Behrens is a Professor of Numerical Methods in Geosciences at the University of Hamburg's Department of Mathematics. His work focuses on computational geoscience, including adaptive numerical methods, tsunami modeling, and multiscale simulations. He leads the Numerical Methods in Geosciences research group and is affiliated with the Climate, Climatic Change, and Society (CLICCS) cluster of excellence. Education & Positions: Since 2009: Full Professor at University of Hamburg 2012: Visiting Scientist at Isaac Newton Institute, Cambridge 2007–2009: Privatdozent at University of Bremen Extensive experience at Alfred-Wegener-Institut and TU Munich Research Interests: Behrens develops numerical algorithms for geophysical flows, emphasizing adaptive mesh refinement, discontinuous Galerkin methods, and tsunami hazard modeling. His work bridges computational mathematics and geoscience applications, with a focus on environmental challenges like climate modeling and disaster risk assessment. Recent efforts include cloud computing for instant hazard simulations and interdisciplinary projects like the Global Tsunami Model (GTM). Key Publications: Recent work includes bathymetry reconstruction via PDE-constrained optimization, low-rank preconditioning for fluid flow, and tsunami hazard frameworks within EPOS. These reflect his expertise in computational methods and geophysical problem-solving. Grants & Projects: Lead on EU-funded DT-GEO (Digital Twin for Geophysical Extremes) Co-chair of COST Action AGITHAR (Tsunami Hazard Research) Contributions to CLICCS and EPOS infrastructure development Labs & Teams: Leads the Numerical Methods in Geosciences group, collaborating internationally on tsunami modeling, climate systems, and high-performance computing.

Christopher Battyمشاهده پروفایل

دانشیار

• Computer Graphics
• Scientific Computing
• Physics-based Simulation
• +۶ مورد دیگر

Christopher Batty is an Associate Professor and Director of Infrastructure at the University of Waterloo's Department of Computer Science. His research focuses on computer graphics and scientific computing, with an emphasis on physics-based numerical simulation of fluids and solids for applications in animation, visual effects, and interactive environments. He holds a Ph.D. from the University of British Columbia (2010) and a B.C.Sc. from the University of Manitoba (2004). His work spans fluid dynamics, solid mechanics, and geometry processing, addressing challenges like surface reconstruction, multi-scale simulations, and efficient solvers for complex fluid-solid interactions. Recent contributions include novel methods for divergence-free fluid editing, discrete elastic rod optimization, and Monte Carlo-based approaches for PDEs on surfaces. Batty’s research integrates computational geometry, numerical analysis, and optimization to create scalable and accurate tools for procedural fluid and solid simulation. His articles emphasize robustness in handling thin obstacles, narrow gaps, and intricate boundary conditions, often leveraging advanced techniques like closest point methods and monolithic solvers. No scientific awards are listed, though his extensive publication record reflects significant contributions to the field. He leads projects on adaptive liquid simulations, surface-only deformable models, and high-resolution embedded fluid surfaces.

Ionuţ Munteanuمشاهده پروفایل

دانشیار

• Controllability and Stabilization of Partial Differential Equations
• Stochastic Partial Differential Equations
• Navier-Stokes Equations
• +۳ مورد دیگر

Ionuţ Munteanu is an Associate Professor at Al. I. Cuza University, Iasi, and a researcher at the Institute of Mathematics 'O. Mayer', Romanian Academy. He holds a PhD in Mathematics from Al. I. Cuza University (2012), supervised by Academician Viorel Barbu. His research focuses on controllability, stabilization, and existence of solutions for deterministic and stochastic partial differential equations, particularly Navier-Stokes and parabolic-type equations with memory. Education: Bachelor's: Faculty of Mathematics, Al. I. Cuza University (2003–2007) Master's: Mathematical Modelling, same institution (2007–2009) PhD: Stabilization of Navier-Stokes Equations (2012) Research Interests: Boundary feedback stabilization for PDEs Controllability and existence analysis for stochastic/parabolic systems Applications to fluid dynamics and magnetohydrodynamics Achievements: Recipient of the 'Dimitrie Pompeiu' Prize (2023) Winner of the Humboldt Fellowship (2015–2017 and 2022) Author of Boundary Stabilization of Parabolic Equations (Birkhauser, 2019) Grants & Collaborations: Directed grants such as GI-UAIC-2018-03 Participated in PN-III national projects Visited institutions like INSA Rouen and University of Bielefeld

• Machine Learning
• Neural Networks
• Quantum Computing
• +۳ مورد دیگر

Zheng Zhang is a Professor in the Department of Electrical and Computer Engineering at the University of California, Santa Barbara. His research focuses on neural networks, quantum computing, uncertainty quantification, and optimization, with particular emphasis on tensor networks, low-rank compression methods, and hardware-efficient machine learning systems. He leads efforts in developing memory-efficient training algorithms for large language models (LLMs), tensorized optical networks, and physics-informed neural PDE solvers. Key contributions include FLAT-LLM for LLM compression, FETTA hardware accelerators, and DeepOHeat for thermal simulation in 3D-IC design. His work spans cross-disciplinary areas such as quantum-inspired algorithms, stochastic control, and yield-aware optimization of photonic ICs. He holds a faculty position in the College of Engineering and is affiliated with the ECE department. Research trends in his 2025 publications emphasize scalable training techniques for transformers, zeroth-order optimization methods, and optical computing integration. His work consistently addresses computational efficiency, memory constraints, and hardware acceleration across domains like AI, quantum computing, and electronic design automation. Notable grants and lab affiliations include projects on FPGA-based neural training, quantum circuit simulation, and tensor-compressed PDE solvers. He advises on edge computing, neuromorphic systems, and uncertainty-aware design tools for integrated circuits.

Jianxin Zhouمشاهده پروفایل

استاد

• Applied Mathematics
• Nonlinear Multiple Solution Problems
• Instability Analysis
• +۳ مورد دیگر

Jianxin Zhou is a Professor of Mathematics at Texas A&M University, affiliated with the College of Arts & Sciences. His primary research focuses on applied analysis and scientific computation, particularly in nonlinear multiple solution problems, instability analysis, multi-level optimization, and control/game theory for differential systems. He earned his Ph.D. in Mathematics from Pennsylvania State University in 1986. Education: Ph.D., Mathematics, Pennsylvania State University, 1986 Research Interests: His work emphasizes computational methods for solving nonlinear PDEs, including minimax-based algorithms, boundary element methods, and numerical optimization techniques. He has contributed to the development of the local minimax method and virtual geometric object approaches for identifying multiple solutions in complex systems. Recent Research Projects: Computational theory and methods for nonlinear multiple solution problems Development of augmented singular transforms and Newton-correction methods Analysis of eigenvalue problems and spatial vector solitons Advising & Grants: He has supervised 9 Ph.D. students and actively participates in international workshops, such as the 2009 Variational and Computational Methods conference in Beijing. His grants and collaborations include projects supported by the NSF and CNSF. Labs/Teams: Collaborates with researchers in computational mathematics, including work on multiscale enrichment procedures and boundary control systems.

Andrew Eberhardمشاهده پروفایل

استاد

• Optimization Theory
• Machine Learning
• Convex Analysis
• +۷ مورد دیگر

Professor Andrew Eberhard holds the position of Deputy Head of Department (Research and Innovation) in the Department of Mathematical and Geospatial Sciences (MGS) at RMIT University's School of Science. He has also served as Deputy Director of the Australian Mathematical Sciences Institute (AMSI) and currently sits on its board. His academic journey includes visiting professorships at institutions such as the University of Perpignan (France), Dalhousie University (Canada), and Blaise Pascal University (France). He has contributed significantly to optimization theory and applications, including roles as Co-chair of the AustMS special interest group (MoCaO) from 2018–2024 and Program Leader of the Platform Technologies Research Institute (2012–2015). Professor Eberhard's research focuses on nonsmooth analysis, convex functions approximation, optimization algorithms, and their applications in control theory, signal processing, and economic modeling. He has applied his theoretical work to real-world challenges like utility function estimation, air traffic flow management, and blood inventory optimization. His recent work includes advancements in stochastic optimization with integer variables and machine learning integration for enhancing algorithm performance. He has been an invited speaker at prestigious events such as the International Conference on Applied Mathematics (2024, Vietnam), Variational and Nonsmooth Analysis (2024, Hong Kong Polytechnic University), and ANZIAM's plenary session (2012, Australia). His teaching interests emphasize nonsmooth analysis and its interdisciplinary applications. Professor Eberhard has supervised numerous research projects, including those on hyperparameter optimization in machine learning, MIMO signal processing, and asset protection routing. His grants include leadership roles in developing algorithms for stochastic integer programming and multi-criteria decision-making under uncertainty. He collaborates actively through the Platform Technologies Research Institute and is affiliated with multiple academic journals as an editor or reviewer.

• Mathematical Analysis
• Applied Mathematics
• Partial Differential Equations
• +۵ مورد دیگر

Pierluigi Colli is a Professor in the Department of Mathematics at the University of Pavia. His research focuses on mathematical analysis of phase transition models, optimal control theory, and applications to tumor growth modeling. He leads the Mathematical Analysis and Applications research group and has contributed extensively to the study of Cahn-Hilliard equations, viscoelastic systems, and nonlinear PDEs. His work integrates advanced analytical techniques with computational approaches, addressing problems in material science, biological systems, and thermodynamics. Colli has published over 100 peer-reviewed articles, with recent emphasis on tumor growth models, fractional operators, and dynamic boundary conditions. His research interests include: Phase field systems and their applications Optimal control of PDEs Nonlinear parabolic equations Mathematical models in oncology Material deformation and viscoelasticity Nonlocal and fractional differential equations Recent studies analyze tumor growth dynamics with chemotaxis effects, sparsity-constrained control strategies, and the interplay between thermal memory and phase transitions. Colli’s contributions to well-posedness, asymptotic analysis, and numerical methods have advanced understanding of complex systems in applied mathematics.

Michael Mahoneyمشاهده پروفایل

استاد

• Machine Learning
• Big Data
• Statistical Analysis
• +۵ مورد دیگر

Michael Mahoney is a Professor in the Department of Statistics at the University of California, Berkeley. He holds roles as Vice President and Director of the Big Data Group at the International Computer Science Institute (ICSI), Group Lead for the Machine Learning and Analytics Group at Lawrence Berkeley National Laboratory (LBNL), and a core member of the RISELab within the Department of Electrical Engineering and Computer Sciences (EECS). He is also an Amazon Scholar. His research focuses on the applied mathematics of data, including randomized numerical linear algebra (RandNLA), optimization, and their applications in machine learning, climate science, genetics, and other domains. He teaches courses such as Linear Algebra for Data Science and has led initiatives like the FODA (Foundations of Data Analysis) Institute under the NSF TRIPODS program. His work emphasizes scalable algorithms, including contributions to RandBLAS/RandLAPACK frameworks and tools like SuperBench and SqueezeLLM. Mahoney collaborates with industry and academia, advancing methods for scientific machine learning and efficient AI infrastructure. Roles: Professor (UC Berkeley), Vice President (ICSI), Group Lead (LBNL), RISELab Member Education: Ph.D. in Computer Science (Yahoo! Research and Stanford University background) Research interests include algorithmic and statistical foundations of big data, with applications in internet analysis, climate modeling, and genomics. His work bridges theory and practice, developing scalable tools for high-dimensional data analysis. He advises numerous students and postdocs, contributing to projects like RandNLA, neural scaling laws, and physics-informed learning. His labs and teams focus on foundational methods for scientific machine learning and efficient AI systems.

• Mathematical Physics
• Analytical Mechanics
• Calculus of Variations
• +۷ مورد دیگر

Enrico Pagani is an Associate Professor in the Department of Mathematics at the University of Trento. His career includes roles as a Researcher (1983–1987) and Associate Professor of Analytical Mechanics (1987–present). He specializes in Mathematical Physics, focusing on geometric mechanics, calculus of variations, and non-holonomic systems. His research spans general relativity, continuum mechanics, wave propagation, and historical analysis of mathematical contributions by Grassmann and Peano. Education: PhD in Physics, University of Genoa (1979). Research Interests: Geometric formulations of classical mechanics (Lagrangian/Hamiltonian systems). Constrained dynamical systems and variational principles. Non-holonomic constraints and optimal control theory. Historical analysis of Grassmann’s exterior algebra and Peano’s contributions to mathematical analysis. Publications: Over 38 peer-reviewed papers, including works on geometric constrained variational calculus, non-holonomic systems, and historical mathematical analysis. Recent focuses include second variation analysis and geometric interpretations of Peano’s work. Editorial Roles: Assistant Editor, NoDEA - Nonlinear Differential Equations and Applications (1993–1999). Member of Editorial Boards for Nonlinear Studies (2001–present) and Mathematics in Engineering, Science and Aerospace (MESA) (2009–present). Collaborations: Extensive collaboration with Enrico Massa (University of Genoa) on geometric mechanics and calculus of variations. Key projects include the geometric approach to non-holonomic systems and Peano/Grassmann historical analysis.

Fabio Viciniمشاهده پروفایل

استادیار

• Numerical Analysis
• Adaptive Finite Element Method
• Virtual Element Methods
• +۹ مورد دیگر

Fabio Vicini is a Fixed-term tenure-track Assistant Professor at the Department of Mathematical Sciences (DISMA), Politecnico di Torino. He is affiliated with the Numerical Analysis and Scientific Computing research group and contributes to the College of Civil and Building Engineering and the College of Mathematical Engineering. His work bridges theoretical numerical methods and high-performance computing applications. Research Interests: His primary research areas include Adaptive Finite Element Methods, Virtual Element Methods (VEM), Computational Fluid Dynamics (CFD), Parallel Computing, Reduced Order Modeling, and High-Performance Solvers for Partial Differential Equations in complex geometries. His work emphasizes algorithmic robustness, efficiency, and scalability in scientific simulations. Recent Publication Trends: His latest articles (2024–2025) focus on advancing the Virtual Element Method—particularly in 3D adaptive settings, stabilization-free error bounds, mesh optimization, and performance on poorly shaped elements. Additional work involves nonlinear optimization for material simulation and extended finite elements for coupled 3D–1D problems, reflecting a strong trend in numerical analysis and computational mathematics. Scientific Awards: Communicating Research to the Citizens (Politecnico di Torino, 01-07-2025) Effective Communication with Businesses (Politecnico di Torino, 01-07-2025) Advising and Teaching: He supervises two PhD students—Lorenzo Neva and Karol Lizeth Cascavita Mellado—in the Mathematical Sciences program. He teaches core courses such as High-Performance Scientific Computing I & II, Model Order Reduction and Machine Learning, and Programming and Scientific Computing across multiple engineering programs including Aerospace, Automotive, and Mathematical Engineering. Labs and Research Groups: He is an active member of the Numerical Analysis and Scientific Computing research group at DISMA, focusing on the development and analysis of numerical schemes for large-scale and complex-domain physical simulations.

Wuchen Liمشاهده پروفایل

استادیار

• Transport information geometry
• Complex dynamical systems
• Partial differential equations (PDEs)
• +۸ مورد دیگر

Wuchen Li is an Assistant Professor in Mathematics at the University of South Carolina , specializing in Transport information geometry and its applications across Complex Dynamical systems, PDEs, Statistics, Optimization, Control and Games, Mathematical Data science, Graphs and Neural networks , and Scientific Computations . His work bridges theoretical mathematics with practical algorithms for machine learning, Bayesian inference, and optimal transport problems. His research explores geometric frameworks for probability spaces, including Wasserstein-2 metrics , Onsager gradient flows , and primal-dual hybrid gradient algorithms . Recent publications focus on accelerated sampling methods, mean field control systems, and novel applications of optimal transport in high-dimensional settings. 2022 : Air Force Office of Scientific Research YIP award for Transport Information Geometric Computations Key article trends include stochastic differential equations (37%), Wasserstein gradient flows (42%), Markov chain Monte Carlo (28%), and Hamilton-Jacobi-Bellman equations (33%). Subfields span accelerated optimization , nonlinear mobility metrics , generative modeling , and reaction-diffusion systems .

Yuhua Zhuمشاهده پروفایل

استادیار

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

Yuhua Zhu is a tenure-track Assistant Professor in the Department of Statistics and Data Science at the University of California, Los Angeles (UCLA). Her research focuses on the intersection of partial differential equations (PDEs) and machine learning, with particular emphasis on using differential equations to analyze and design efficient reinforcement learning and optimization algorithms. Education: Ph.D. in Mathematics, University of Wisconsin-Madison (2019), advised by Shi Jin M.S. in Mathematics, University of Wisconsin-Madison (2015) B.S. in Mathematics, Shanghai Jiao Tong University (2014) Postdoc at Stanford University (2019–2022), mentored by Lexing Ying Research Interests: Her work spans sequential decision-making (e.g., continuous-time reinforcement learning, multi-armed bandits), optimization (gradient-free methods, asynchronous-SGD, convergence analysis), and uncertainty quantification in kinetic equations (sensitivity analysis, high-dimensional uncertainty). She develops PDE-based frameworks for model-free reinforcement learning and consensus-based optimization methods for distributed learning systems. Recent Work Trends: Her publications emphasize PDE-driven approaches to reinforcement learning, consensus-based optimization in federated learning, and uncertainty quantification in kinetic equations. Recent topics include addressing adversarial attacks in federated learning, high-order discretization in policy evaluation, and stabilization of kinetic equations with reflective boundary conditions. Awards & Grants: No specific awards mentioned in the text. Current recruiting focuses on graduate students in reinforcement learning and optimization for machine learning problems. Labs & Collaborations: Collaborations include work with Jose Carrillo, Shi Jin, Lexing Ying, and others on consensus-based methods and kinetic theory applications in machine learning.