جست‌وجوی استاد و پژوهشگر

Eugene Wayneمشاهده پروفایل

استاد

• Dynamical Systems
• Partial Differential Equations
• Mathematical Physics
• +۳ مورد دیگر

Eugene Wayne is a Professor in the Mathematics Department at Boston University. He is affiliated with the Center for BioDynamics at the same institution. His research focuses on dynamical systems, partial differential equations, and mathematical physics. Specific interests include fluid dynamics, nonlinear waves, and the application of KAM theory to PDEs. He teaches advanced courses like MA 876 (Special Topics in Partial Differential Equations) and has explored the role of technology in mathematics education through computer algebra systems. His research emphasizes understanding viscous/inviscid fluid behavior, extending KAM theory to PDE contexts, and analyzing equations like Navier-Stokes and nonlinear Schrödinger equations. Recent work includes studies on metastable states, vortex dynamics, and Taylor dispersion justification through center manifold methods. Preprints are available via mp_arc and the Nonlinear Science e-Print Archive. No academic awards are explicitly mentioned in the provided texts. His advising and grants are not detailed here, though his teaching and research collaborations suggest active involvement in academic mentorship. He maintains active research in mathematical physics and fluid dynamics through his affiliation with the Center for BioDynamics.

Wuzhe Xuمشاهده پروفایل

استادیار مهمان

• Applied Mathematics
• Computational Mathematics
• Machine Learning
• +۴ مورد دیگر

Wuzhe Xu is a Marshall H. Stone Visiting Assistant Professor in the Department of Mathematics and Statistics at the University of Massachusetts Amherst, mentored by Prof. Yulong Lu. He holds a Ph.D. in Mathematics from the University of Minnesota Twin Cities (2022), supervised by Prof. Li Wang. His research focuses on applied mathematics, computational methods, and interdisciplinary applications in fluid dynamics, partial differential equations, and machine learning. Education: Ph.D. in Mathematics, University of Minnesota Twin Cities, 2022 Research Interests: Dr. Xu develops advanced computational techniques for solving multiscale kinetic equations, diffusion models, and physics-guided machine learning frameworks. His work addresses challenges in fluid dynamics, radiative transfer, and long-time prediction of evolution equations. He emphasizes asymptotic preserving schemes and transfer learning applications to ensure numerical stability and accuracy across diverse physical systems. Publications Trends: His recent work (2021–2025) highlights innovations in generative downscaling of PDE solvers, super-resolution techniques in fluid dynamics, and mathematical modeling of granular media. Earlier studies (2013–2014) explored epidemiological trends of infectious diseases in Guangxi Province, China. Awards & Recognition: Notable for award-winning teaching and research excellence in applied mathematics. Advising & Grants: Active in mentoring students through interdisciplinary projects. Current grants and collaborations focus on advancing computational methods for fluid dynamics and machine learning applications. Labs/Teams: Collaborates closely with the Applied Mathematics Research Group at UMass Amherst and maintains ties with Prof. Li Wang's team at the University of Minnesota.

• Calculus of Variations
• Partial Differential Equations
• Optimal Transport
• +۴ مورد دیگر

Alessio Figalli is a renowned mathematician and Professor at the Department of Mathematics , ETH Zurich , where he also serves as Director of the Forschungsinstitut für Mathematik (FIM) . Born in Rome (Italy) in April 1984, he holds Italian citizenship and is a Swiss permanent resident. His academic journey includes a PhD from the Scuola Normale Superiore (Pisa) and École Normale Supérieure (Lyon), with postdoctoral roles at institutions like the University of Nice and École Polytechnique. Prior to ETH Zurich, he held professorships at the University of Texas at Austin (2009–2019). Figalli's research focuses on Calculus of Variations , Partial Differential Equations (PDEs) , Optimal Transport , and Free Boundary Problems . His work bridges pure and applied mathematics, with applications in geometry, probability, and physics. He is particularly known for contributions to optimal transport theory, regularity in free boundary problems, and stability estimates for geometric inequalities. Figalli has authored over 200 publications, including seminal papers on regularity of optimal transport maps , stability of geometric inequalities , and nonlocal PDEs . His invited lectures include plenary talks at the International Congress of Mathematicians (2018) and the Bourbaki seminar . He has supervised numerous PhD students and postdocs, fostering collaborations globally. Awards: Fields Medal (2018), EMS Prize (2012), Feltrinelli Prize (2017). Editorial Roles: Arch. Ration. Mech. Anal., Duke Math. J., and 10+ other journals . Grants: ERC Grant RSPDE , NSF awards, and the Swiss National Science Foundation. His ERC-funded project RSPDE explores regularity and stability in PDEs, while his mentorship has shaped the careers of over 30 students/postdocs. Figalli actively promotes mathematics through outreach, including public lectures and media engagements.

Igor Cialencoمشاهده پروفایل

استاد

• Quantitative Finance
• Statistical Inference for Stochastic PDEs
• Stochastic Control
• +۳ مورد دیگر

Igor Cialenco is a Full Professor in the Department of Applied Mathematics at Illinois Institute of Technology (College of Computing). He holds a Ph.D. in Applied Mathematics from the University of Southern California (2007). Research Interests : Quantitative Finance, Statistical Inference for Stochastic PDEs, Stochastic Control, Environmental Finance, and Functional Analysis. Professional Roles : Managing Editor for the International Journal of Theoretical and Applied Finance, and editorial board member of multiple journals including SIAM Journal on Financial Mathematics. Research Trends : His recent publications focus on stochastic modeling in environmental finance (e.g., groundwater markets), robust control under model uncertainty, and statistical inference for SPDEs. The keywords span Financial Mathematics, Stochastic Processes, Environmental Modeling, and Statistical Learning. Scientific Awards : 2024 Researcher to Know (ISTC), 2024 College of Computing Dean's Excellence Award for Research, multiple Dean's Excellence awards in teaching and research. Mentorship & Funding : Mentored numerous PhD and MS students in quantitative finance and SPDEs. Current NSF grant DMS-2407549 supports research on stochastic modeling for water rights management. Past NSF grants include DMS-1907568 and DMS-0908099.

Nicolas Charonمشاهده پروفایل

استادیار

• Morphological Shape Analysis
• Medical Imaging
• Computational Anatomy
• +۷ مورد دیگر

Nicolas Charon is an Assistant Professor in the Department of Mathematics at the University of Houston. He holds a PhD in Mathematics from École normale supérieure de Cachan (2013) and completed postdoctoral research at the University of Copenhagen. His research focuses on geometric methods for medical imaging and computational anatomy, including diffeomorphic registration, functional shape analysis, and Riemannian metrics on curves/surfaces. He has been supported by the NSF DMS-1945224 CAREER Award and has developed software tools like fshapesTk , h2metrics , and ShapeGraph_H2match . Key research areas include varifold-based shape registration, metamorphoses of functional shapes, and sparse methods for diffusion MRI compression. Charon has advised three PhD students and contributed to over 50 publications. His work bridges theoretical mathematics with applications in healthcare, including stroke risk assessment via left atrial appendage morphology analysis and 4D human shape synthesis. Recent projects involve scalable frameworks for PDE solution operators, self-supervised learning of human body scans, and data-driven elastic shape analysis with topological constraints. Charon’s interdisciplinary approach integrates geometry, optimization, and machine learning to advance computational anatomy and medical imaging technologies.

Bei Huمشاهده پروفایل

استاد

• Partial Differential Equations
• Mathematical Biology
• Numerical Differential Equations
• +۳ مورد دیگر

Bei Hu is a Professor and current Acting Associate Chair in the Department of Applied and Computational Mathematics and Statistics (ACMS) at the University of Notre Dame. He also serves as the Director of the MS Program in ACMS . His academic career includes roles such as Chair of the Department of ACMS (2018–2021), Associate Dean of the College of Science (2010–2013), and Chair of the Department of Mathematics (2008–2010). Education: Ph.D. in Mathematics from the University of Minnesota (1990), followed by M.S. degrees from the University of Minnesota (1989) and East China Normal University (1984), and a B.S. from East China Normal University (1982). Research focuses on Partial Differential Equations (PDEs) and their applications in Mathematical Biology , Engineering , and Financial Mathematics . Key areas include: Free boundary problems in tumor growth and atherosclerosis modeling Analysis of PDE properties (existence, uniqueness, asymptotic behavior) Numerical methods for differential equations and scientific computing Applications in credit risk modeling and financial derivatives His work employs advanced techniques such as elliptic/parabolic a priori estimates, variational inequalities, bifurcation analysis, and numerical simulations. Notable publications include "Blow-up Theories for Semilinear Parabolic Equations" (2011) and "Credit Rating Migration Risks in Structure Models" (2024). Teaching includes advanced courses like ACMS 60850 Applied Probability , with a focus on stochastic processes, martingales, and qualifying exam preparation for Ph.D. students.

• Numerical Analysis
• Computational Fluid Dynamics
• Geometric Evolution Equations
• +۳ مورد دیگر

Robert Nürnberg is an Associate Professor in the Department of Mathematics at the University of Trento. His research focuses on numerical analysis, computational fluid dynamics, and geometric evolution equations, with a particular emphasis on finite element methods for multiphase flow, phase-field models, and biomembrane dynamics. He teaches courses such as Numerical methods for PDEs and Scientific computing , covering finite element implementations in MATLAB/Python and programming for mathematical sciences. His work addresses complex phenomena like Willmore flow, anisotropic crystal growth, and surfactant dynamics, often combining rigorous mathematical analysis with computational innovation. Recent contributions include parametric finite element methods for geometric PDEs and structure-preserving discretizations of elastic flow in Riemannian manifolds. Dr. Nürnberg collaborates on interdisciplinary projects involving additive manufacturing, tumor growth modeling, and fluidic biomembrane dynamics. His methods emphasize stability, accuracy, and applicability to real-world problems like void electromigration and phase separation in materials science.

• Differential Geometry
• Geometric Analysis
• Partial Differential Equations
• +۳ مورد دیگر

Alessandro Carlotto is a Full Professor in the Department of Mathematics at the University of Trento. His academic role includes teaching advanced courses such as Minimal Surfaces and Partial Differential Equations , reflecting his research expertise in geometric analysis and differential geometry. His teaching focuses on equipping students with foundational knowledge in these fields, enabling them to engage with contemporary research literature. While no specific educational background is detailed in the provided text, his courses emphasize advanced mathematical topics relevant to graduate studies in Mathematics (LM). Research interests are inferred from his teaching, including minimal surface theory, elliptic and parabolic PDEs, and geometric modeling. No scientific awards or grants are listed in the available data. His academic contributions are centered on mentoring through specialized coursework, though no advisee names or research groups are explicitly mentioned. The courses he co-teaches, such as Partial Differential Equations with Alberto Valli, highlight collaborative academic endeavors within the department.

• 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.

• Optimal Control
• Partial Differential Equations
• Mean Field Games
• +۶ مورد دیگر

Associate Professor at the University of Pavia's Department of Economics and Management, specializing in optimal control, partial differential equations, and mean field games with applications to economics and finance. Holds an Italian qualification (ASN) for associate professorship and a French qualification (maître de conférences). Research focuses on dynamic optimization, stochastic systems, and economic modeling of human capital, environmental quality, and pandemic impacts. Active in teaching mathematics for economics and optimization at undergraduate and graduate levels. Education: PhD in Mathematics from University of Padua (2015), postdocs at University of Graz (Austria) and University of Padua. Extensive visiting collaborations in France, Chile, and Brazil. Research Highlights: Pioneering work on mean field games modeling spatial interactions in human capital dynamics and environmental quality regulation. Contributions to sparse optimization, inverse problems in fracture mechanics, and large deviations in stochastic volatility models. Recent Publications Trends: Focus on infinite-dimensional mean field games (e.g., Hilbert spaces), pandemic economics, and environmental policy modeling. Over 15 peer-reviewed articles spanning optimal control, PDEs, and economic applications. Grants & Leadership: Coordinator of Italian GNAMPA projects on MFGs and environmental models. Organized conferences including AMASES 2022 and special sessions at international optimal control conferences. Referee for top journals like SIAM J. Control Optim. and J. Mathematical Economics. Teaching: Courses include 'Mathematics for Management,' 'General Mathematics,' and PhD-level 'Static and Dynamic Optimization.' Oversees tutoring programs and international collaborations via ERASMUS+.

Franco Brezziمشاهده پروفایل

استاد

• Partial Differential Equations
• Numerical Analysis
• Finite Element Methods
• +۷ مورد دیگر

Franco Brezzi is an Italian mathematician with significant contributions to numerical analysis and partial differential equations. He has held prestigious positions, including Professor of Numerical Analysis at the Istituto Universitario di Studi Superiori (IUSS) of Pavia (2006-2015), Full Professor of Mathematical Analysis at the University of Pavia (1977-2006), and Professor of Mathematical Analysis at the Polytechnic of Torino (1975-1977). Currently, he is an Associate Researcher at IMATI-CNR (Istituto di Matematica Applicata e Tecnologie Informatiche) in Pavia and Vice-President of the European Mathematical Society (EMS) since 2013. Managing Editor of M3AS (Mathematical Models & Methods in Applied Sciences) Editor-in-Chief of Numerische Mathematik and Calcolo Research Interests : His work bridges theoretical and numerical analysis of PDEs, focusing on mixed finite element methods, stabilization techniques, residual-free bubbles, and advanced discretization frameworks like mimetic finite differences and virtual element methods. These contributions have shaped computational mechanics and numerical approximation over decades. Scientific Awards : Ritz-Galerkin Medal (2016) SIAM Fellow (2015) Leonhard Euler Medal (2014) Blaise Pascal Medal for Mathematics (2012) SIAM Von Neumann Award (2009) Gauss-Newton Medal (2004) ISI Highly Cited Researcher (2003) Fellow of IACM (2002)

• Partial Differential Equations
• Homogenization
• Calculus of Variations
• +۳ مورد دیگر

Peter S. Morfe is a Research Fellow at the Max Planck Institute in Leipzig, supervised by Felix Otto. He holds a Ph.D. from the University of Chicago (2022), advised by Panagiotis Souganidis. His research focuses on elliptic/parabolic PDEs, homogenization, calculus of variations, and stochastic processes. He was supported by the NSF Mathematical Sciences Postdoctoral Research Fellowship from 2022 to 2024. His educational background includes a Ph.D. in Mathematics (2022) from the University of Chicago and prior academic training leading to his postdoctoral appointment. Research interests span theoretical and applied analysis, with emphasis on homogenization theory, nonlinear PDEs, and stochastic methods. Key themes include phase transitions, effective properties of random media, and connections between PDEs and probability. His work bridges mathematical physics and materials science, addressing questions in continuum mechanics and stochastic processes. Publications highlight contributions to drift-diffusion equations, Gaussian free fields, and homogenization in heterogeneous media. Recent work explores scaling limits, Pareto peeling, and surface tension phenomena in periodic environments. Notable collaborations include studies with Felix Otto, Panagiotis Souganidis, and others in fluid dynamics and stochastic analysis. Morfe’s NSF fellowship (2022–2024) underscores recognition of his research potential. He has contributed to advancing techniques in viscosity solutions and variational principles, with applications to both pure and applied mathematical problems. His work often integrates analytical rigor with computational insights, reflecting interdisciplinary strengths.

Agnes Lamacz-Keymlingمشاهده پروفایل

پژوهشگر

• Analysis of PDEs
• Multiscale problems and homogenization
• Wave phenomena

Dr. Agnes Lamacz-Keymling is a researcher at the University of Duisburg-Essen in the AG Optimal Control of Partial Differential Equations. Her research focuses on analysis of PDEs, multiscale problems, homogenization, and wave phenomena. PhD in Mathematics (2011) and Diploma in Mathematics (2008) from TU Dortmund Her work spans homogenization of periodic structures, wave propagation in heterogeneous media, negative index meta-materials, and multiscale modeling. She has received third-party funding through a DFG project on wave propagation in periodic structures and negative refraction. Recent publications highlight trends in Bloch wave homogenization, dispersive wave models, and photonic crystal analysis. Her teaching includes Mathematics E3 and E4 in the winter semester 2021/22. Third-party funding: DFG project on wave propagation in periodic structures and mechanisms of negative refraction (2014)

Mikko Parviainenمشاهده پروفایل

دانشیار

• Nonlinear Partial Differential Equations
• Stochastic Game Theory
• Calculus of Variations
• +۳ مورد دیگر

Mikko Parviainen is an Associate Professor at the University of Jyväskylä's Department of Mathematics and Statistics within the Faculty of Mathematics and Science. His research focuses on nonlinear partial differential equations (PDEs), particularly parabolic PDEs, stochastic game theory, calculus of variations, and nonlinear potential theory. He has contributed extensively to the analysis of regularity properties of solutions to equations like the p-Laplace and generalized p-parabolic equations, often connecting game-theoretic models with PDE theory. His work bridges stochastic processes and deterministic PDEs, with notable studies on Hölder continuity, Sobolev regularity, and dynamic programming principles. Parviainen collaborates internationally, evidenced by co-authored publications with researchers like Blanc, Manfredi, and Rossi. His research group, 'Nonlinear Partial Differential Equations,' explores advanced topics in PDE theory and applications. Parviainen's publications span high-impact journals such as Journal of Functional Analysis , Mathematische Annalen , and Annales de l’Institut Henri Poincaré . While no awards are explicitly listed, his prolific output and focus on foundational mathematical analysis underscore his scholarly contributions. No advising roles or grants are detailed in the provided texts.

• Nonlinear Partial Differential Equations
• Fluid Mechanics
• Mathematical Biology
• +۳ مورد دیگر

Yao Yao is an Associate Professor at the Department of Mathematics, National University of Singapore (NUS), and the Vice Dean of the NUS Graduate School. He holds a Ph.D. in Mathematics from UCLA (2012), with postdoctoral work at the University of Wisconsin-Madison under mentors Alexander Kiselev and Andrej Zlatoš. His research focuses on nonlinear partial differential equations (PDEs) in fluid mechanics and mathematical biology, complemented by recent work in AI applications for healthcare, including disease prediction and medical data analysis. Before joining NUS, he was an Assistant Professor at Georgia Tech's School of Mathematics. Yao Yao serves as an associate editor for several prestigious journals: Analysis and Applications , Kinetics and Related Models , Nonlinear Analysis , Nonlinearity , and SIAM Journal on Mathematical Analysis . His academic contributions span theoretical mathematics and applied AI, with a strong emphasis on interdisciplinary research bridging PDE analysis and healthcare informatics. His work in AI includes explainable deep learning models for Alzheimer’s disease prediction, graph-based patient trajectory modeling, and cybersecurity for medical survival models. He has also explored ontology-driven frameworks for personalized medicine and EHR data analysis. While no awards are explicitly listed, his editorial roles and prolific publication record highlight his influence in mathematical and computational sciences. Yao Yao’s lab focuses on mathematical modeling of biological systems and AI-driven healthcare solutions. His research has applications in cardiology, oncology, and chronic pain management, leveraging both classical PDE theory and modern machine learning techniques.