Find Academic Supervisors

Isabelle GreffView profile

Lecturer

• Finite Element Modeling
• Multiscale Analysis
• Homogenization
• +3 more

Dr. Isabelle Greff is a Lecturer at the University of Pau and the Pays de l'Adour, affiliated with the Laboratory of Mathematics and their Applications. Her research focuses on finite element modeling, multiscale analysis, homogenization techniques, and computational methods for partial differential equations. Greff develops innovative numerical approaches for problems involving composite materials and random media. Her work on box-schemes for elliptic problems provides efficient computational frameworks for complex physical systems. She has collaborated on interdisciplinary projects connecting mathematical theory with engineering applications. Greff has supervised graduate research on variational principles and fractional dynamics, and contributes to mathematics education outreach through initiatives encouraging girls' participation in STEM fields.

• Partial Differential Equations
• Nonlinear Analysis
• Mathematical Analysis
• +3 more

Guillaume Warnault is a Lecturer at the University of Pau and Pays de l'Adour since September 2010, affiliated with the Analysis, Geometry and Applications research team within the Laboratory of Mathematics and their Applications (LMAP). Previously, he held a Temporary Teaching and Research Associate position at François Rabelais University, Tours (2009-2010) following doctoral studies at the University of Picardie Jules Verne. Education: Doctorate in Mathematics, University of Picardie Jules Verne, Amiens (2006-2009). Thesis: "Stable solutions for semilinear elliptical PDEs involving the biharmonic operator". Research Focus: His work centers on nonlinear partial differential equations , particularly elliptic and parabolic systems , biharmonic operators , and problems with variable exponents . Key contributions include blow-up analysis for quasilinear systems, regularity theory for fractional Laplacian equations, and bifurcation phenomena in singular biharmonic problems. His research bridges pure analysis with applications in fluid dynamics and mathematical physics. Publication Trends: Recent work (2016-2022) shows deepening specialization in p(x)-homogeneous operators and fractional calculus , with increasing collaboration intensity. Over 80% of his publications involve co-authors Giacomoni and Arora, reflecting sustained research partnerships. His output demonstrates methodological consistency in applying variational techniques to nonlinear PDEs while expanding into emerging areas like doubly nonlinear systems. Academic Leadership: Warnault serves as program director for the Master's in Mathematics, Modeling and Simulation (MMS) since 2023 and previously directed the MAISHS undergraduate program (2019-2023). He has supervised six Master's theses and co-directed Rakesh Arora's PhD (2017-2020). As an elected laboratory council member since 2012 and former committee of experts member (2012-2020), he actively shapes institutional research strategy. Research Environment: Based at the IPRA Building in Pau, he contributes to LMAP (UMR 5142), a CNRS-affiliated laboratory fostering interdisciplinary work in mathematical analysis and its applications through the Analysis, Geometry and Applications team.

• Geometric Analysis
• Partial Differential Equations
• Mathematical Physics
• +6 more

Antonella Marini is a Professor of Mathematics at Yeshiva College, Yeshiva University. She holds a PhD in Mathematics from the University of Chicago, specializing in boundary value problems in gauge theories. Previously, she held positions at the Courant Institute of NYU as a Postdoctoral Fellow, the University of Utah as a full-time Instructor, the University of L'Aquila as a tenured Researcher and Associate Professor. Her research focuses on Geometric Analysis, Partial Differential Equations, and Mathematical Physics, with particular emphasis on gauge theories, Hodge-Frobenius equations, and quantum field theories. Education: PhD in Mathematics (University of Chicago), MS in Mathematics (University of Chicago), Laurea in Matematica (University of Rome 1 'La Sapienza'). Research interests include nonlinear PDEs, boundary value problems in gauge theories (e.g., Yang-Mills, Yang-Mills-Higgs), and applications to quantum mechanics, Hodge theory, and elliptic-hyperbolic equations. She has taught courses such as Advanced Calculus, Partial Differential Equations, and Mathematical Modeling at both undergraduate and graduate levels. Her work has been recognized with an Instructorship Award at the University of Utah. Recent publications include studies on quantum gauge theory, semi-classical methods in bosonic field theories, and Hodge-Frobenius equations. She collaborates on research involving topological solutions and geometric analysis.

Hailiang LiuView profile

Professor

• Computational and Applied Mathematics
• Deep Learning
• Mathematical Biology
• +7 more

Hailiang Liu is a Professor of Mathematics at Iowa State University, with a courtesy appointment in Computer Science and membership in the Translational AI Center (TrAC). He also serves as the Data Science/AI Coordinator in the Department of Mathematics. Currently, he is on leave as a Program Director in the Applied Mathematics Program at the National Science Foundation (NSF). Dr. Liu holds a Ph.D. in Applied Mathematics from the Chinese Academy of Sciences (1995), an M.Sc. from Tsinghua University (1988), and a B.Sc. in Math Education from Henan Normal University (1984). His research focuses on computational and applied mathematics, including mathematical modeling via PDEs, numerical methods, scientific computing, and their intersections with deep learning and data-driven approaches. Key areas include: Deep Learning: Neural ODEs, selection dynamics, and optimal transport-based algorithms Mathematical Biology: Population dynamics, ecological dispersal, and aggregation modeling Computational Methods: Discontinuous Galerkin (DG) schemes, energy-stable numerical methods, and Gaussian beam methods Kinetic Theory: Photon scattering, Bose-Einstein condensation, and hyperbolic balance laws His work has been supported by the NSF and DOE. Notable contributions include structure-preserving numerical schemes, critical threshold analysis in hyperbolic systems, and data-driven optimal control frameworks.

• Calculus of Variations
• Partial Differential Equations
• Probability Theory
• +5 more

Role: Professor of Mathematics at Purdue University, Department of Mathematics. Serves as faculty in the College of Science. Specializes in applied analysis, stochastic processes, and mathematical modeling in materials science. Active in teaching graduate courses on dynamical systems and partial differential equations. Education: Bachelor of Science in Electrical Engineering, Purdue University (1991) PhD in Mathematics, Princeton University (1996) Research Interests: Focuses on calculus of variations, PDEs, probability theory, and applied mathematics with applications to materials science. Explores topics like geometric flows, stochastic differential equations, and mathematical modeling of crystal growth and dislocation dynamics. Organizes conferences on stochastic analysis and industrial mathematics applications. Awards: Recipient of Purdue's prestigious Book of Great Teachers (2018), Ruth and Joel Spira Teaching Award (2005), and Teaching for Tomorrow Award (2003). Recognized for excellence in both teaching and mentoring. Students & Mentoring: Advised PhD students in topics ranging from surface diffusion to neural networks. Notable advisees include Fang Wan (2010), Chaoqun Huang (2012), and Yuqing Li (2021). Also mentored undergraduate researchers in projects like car parking problems and random walks in random media. Recent Activities: Organized workshops like the Mathematics and Statistics in Industry Workshop (2025) and Stochastic Analysis conferences (2023, 2025). Active in interdisciplinary research, including collaborations on optical manipulation of colloids and privacy vulnerabilities in mobility data.

Milena StanislavovaView profile

Professor

• Dynamical Systems
• Partial Differential Equations
• Nonlinear Equations
• +3 more

Milena Stanislavova is a Professor of Mathematics and Chair of the Economics Department at the University of Kansas. She holds a M.S. from Sofia University (1993) and a Ph.D. from the University of Missouri (2000). Previously, she served as Director of Graduate Studies in the Mathematics Department (2012–2017). Her research focuses on Dynamical Systems and Partial Differential Equations, particularly the stability and long-time behavior of solutions to nonlinear equations of mathematical physics. She explores Hamiltonian PDEs, coherent structures like solitons and periodic waves, and their applications in modeling physical processes. Stanislavova has been continuously funded by NSF since 2004 and teaches undergraduate courses in differential equations, complex analysis, and graduate courses in analysis, dynamical systems, and PDEs. She is a faculty fellow in KU’s Honors Program, teaching freshman seminars, and received the Don and Pat Morrison Award for Excellence in Teaching Mathematics. Her research spans spectral stability, energy decay rates, and nonlinear phenomena in systems like the Klein-Gordon-Zakharov, Lugiato-Lefever, and Schrödinger equations. Her work emphasizes spectral analysis of periodic and traveling waves, stability of standing waves in Hamiltonian systems, and applications in nonlinear optics and fluid dynamics. She has published extensively in journals like Nonlinearity, Physica D, and SIAM Journal of Applied Math, with contributions to both theoretical and applied mathematical physics.

Jianfeng LuView profile

Professor

• Mathematical Analysis
• Algorithm Development
• Computational Physics
• +5 more

Jianfeng Lu is the James B. Duke Distinguished Professor of Mathematics at Duke University, with affiliations in the Department of Mathematics, Chemistry, and Physics within the Trinity College of Arts & Sciences. He holds a Ph.D. from Princeton University (2009). His research focuses on mathematical analysis, algorithm development for computational physics, theoretical chemistry, materials science, and machine learning, particularly in high-dimensional PDEs, quantum dynamics, and multiscale modeling. Notable achievements include the 2024 James B. Duke Distinguished Professorship. His work spans interdisciplinary collaborations, with grants from the National Science Foundation (NSF) and Department of Energy (DOE), addressing challenges in quantum computing, electronic structure theory, and AI-driven material design. Research interests include quantum Markov processes, reinforcement learning theory, and numerical methods for PDEs. He advises numerous graduate students and postdocs, mentoring in applied mathematics and computational physics. His labs and teams contribute to projects like the Duke Materials Initiative and the ELSI collaboration for electronic structure theory.

James H. NolenView profile

Professor

• Probability
• Partial Differential Equations
• Stochastic Dynamics
• +3 more

James H. Nolen is a Professor of Mathematics at Duke University's Trinity College of Arts & Sciences. He holds a Ph.D. from the University of Texas, Austin (2006) and a B.S. from Davidson College (2000). His research focuses on probability, partial differential equations, and stochastic dynamics, with applications to biological models and reaction-diffusion systems. Notable awards include the 2025 Bass Chair and an NSF Postdoctoral Fellowship (2006-2008). Leadership: Directed Duke's Mathematics Postdoctoral Training Program (2019-2023) Grants: Co-PI for NSF RTG grant on Analysis and Applications (2021-2026), PI for NSF CAREER award on stochastic dynamics (2014-2020) His work bridges theoretical analysis with applications, including studies on random walks, interacting particle systems, and front propagation in heterogeneous media. He serves as an Associate Editor for SIAM Journal on Mathematical Analysis and SIAM Multiscale Modeling and Simulation.

Jun KitagawaView profile

Associate Professor

• Optimal Transportation
• Partial Differential Equations
• Differential Geometry
• +1 more

Jun Kitagawa is an Associate Professor in the Department of Mathematics at Michigan State University (MSU). His research focuses on fully nonlinear elliptic PDEs, particularly Monge-Ampère equations, with applications in optimal transport, geometric optics, and fluid dynamics. Key interests include regularity properties of solutions and numerical methods for these equations. He has held a sabbatical during the 2022 semesters. Research Interests: Optimal Transportation, Partial Differential Equations, Differential Geometry, Numerical Methods. Publications: His work spans theoretical advancements and numerical algorithms in optimal transport, including studies on stability, geometric optics, and semi-discrete transport. Recent trends include exploring transport on manifolds, perturbative approaches, and algorithmic innovations like Newton methods for transport problems. Awards: None explicitly listed. Advising & Grants: Advised graduate students (e.g., Minh Nguyen) and postdocs (e.g., Farhan Abedin). Former students include Chamila Malagoda Gamage (U Florida) and Mohit Bansil (UCLA Ph.D., D.E. Shaw Analyst). Labs/Teams: Active research group at MSU focusing on optimal transport and PDEs.

Keith S PromislowView profile

Professor

• Partial Differential Equations
• Functional Analysis
• Polymer Materials
• +4 more

Keith S Promislow is a Professor in the Department of Mathematics at Michigan State University (MSU), specializing in partial differential equations (PDEs) and functional analysis with applications to coherent structures in nonlinear systems. His research focuses on stability, bifurcation, and geometric evolution in materials science, particularly in polymer electrolyte membranes and energy conversion devices like fuel cells and batteries. His educational and professional trajectory emphasizes advanced mathematical approaches to material behavior. Key research themes include the development of pore networks in amphiphilic polymers, curvature-driven phenomena in nonlinear systems, and the interplay between geometry and dynamics in materials. Notable contributions span the functionalized Cahn-Hilliard equation, parametric nonlinear Schrödinger systems, and models for ion transport in polymer membranes. His work bridges applied mathematics and engineering, addressing challenges in energy storage and environmental science. While no formal advisees are listed, his collaborative projects suggest active participation in interdisciplinary research teams. His research has been funded through grants focusing on multi-scale modeling and data-driven approaches in STEM.

Nicolò ForcilloView profile

Visiting Assistant Professor

• Partial Differential Equations
• Free Boundary Problems
• Calculus of Variations
• +5 more

Nicolò Forcillo is a Visiting Assistant Professor at the Department of Mathematics, Michigan State University (MSU), since August 2023 under Prof. Russell Schwab. Previously, he held postdoctoral positions at Università di Roma Tor Vergata (2022–2023) and Università degli Studi di Bologna (2021–2022), where he completed his PhD under Prof. Fausto Ferrari's supervision. He also served as a junior fellow at the Institut Mittag-Leffler (2022) and collaborated internationally at institutions like the University of Pittsburgh and Columbia University. His research focuses on Partial Differential Equations (PDEs) , particularly in free boundary problems , sub-Riemannian geometry , and calculus of variations . Key topics include regularity theory for degenerate elliptic/parabolic PDEs, monotonicity formulas (e.g., Alt-Caffarelli-Friedman), and applications in geometric analysis and nonlinear phenomena. His work often explores non-Euclidean settings like Carnot groups and Heisenberg geometry. Notable contributions include counterexamples to monotonicity behaviors in sub-Riemannian spaces, Lipschitz regularity of almost minimizers, and analysis of the ∞-Laplacian in non-Euclidean frameworks. His research bridges theoretical PDEs with geometric control theory, emphasizing both analytical rigor and interdisciplinary applications.

Willie Wai-Yeung WongView profile

Associate Professor

• Partial Differential Equations
• Differential Geometry
• General Relativity
• +3 more

Willie Wai-Yeung Wong is an Associate Professor in the Department of Mathematics at Michigan State University (MSU), located in D303 Wells Hall. His research focuses on the intersection of geometry and partial differential equations (PDEs), particularly hyperbolic PDEs and geometric problems in general relativity. He has contributed to studies on shock formation in wave equations and time-like minimal submanifolds in Minkowski space. Wong’s academic work includes collaborations on topics such as stable shock formation, minimal surface equations, and geometric flows. He teaches advanced courses like MTH 490 (Directed Studies) and MTH 990 (Reading in Mathematics). Office hours are by appointment via his personal website or D2L platform. His research interests emphasize applying geometric viewpoints to PDE analysis and solving geometric problems through PDE frameworks. Notable publications include works on shock formation in 3D quasilinear wave equations and the stability of minimal submanifolds in Minkowski space. No specific grants or awards are explicitly listed in the provided information.

Andrew EberhardView profile

Professor

• Optimization Theory
• Machine Learning
• Convex Analysis
• +7 more

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.

• Applied Mathematics
• Data Science
• Partial Differential Equations
• +7 more

Dejan Slepčev is a Professor and Associate Dean for Faculty and Graduate Affairs at the Mellon College of Science, Department of Mathematical Sciences, Carnegie Mellon University. His research focuses on applied analysis, with applications to data science, collective behavior of particle systems, and energy-driven systems. He holds a Ph.D. from the University of Texas at Austin. His research interests include partial differential equations, calculus of variations, optimal transportation, and their applications to machine learning tasks such as clustering and classification. He develops mathematical frameworks for analyzing variational and PDE-based problems on random data samples, improving algorithms for data science. He also studies systems of interacting particles, particularly those modeling collective behavior in biological and physical systems, and energy-driven systems involving pattern formation and interface evolution. His work bridges theoretical analysis and computational methods, addressing challenges in data analysis, image processing, and the dynamics of complex systems. Key contributions include studies on spectral clustering, graph-based semi-supervised learning, and the continuum limits of discrete variational problems. Education: Ph.D., University of Texas at Austin.

Mark KonView profile

Professor

• Applied Mathematics
• Probability and Statistics
• Machine Learning
• +4 more

Mark Kon is a Professor at Boston University's College of Arts & Sciences, affiliated with the Department of Mathematics and Statistics. He is a core member of the Applied Mathematics and Probability and Statistics research groups. His office is located in CDS 523, with office hours on Tuesdays 11-12:15 PM and Thursdays 2-3:15 PM. Professor Kon's research focuses on interdisciplinary applications of mathematics and statistics, including machine learning, quantum computing, biomedical informatics, and statistical analysis. He integrates computational methods with biological systems to address challenges in genetics, cancer classification, and medical data analysis. His work bridges theoretical foundations (e.g., stochastic processes, functional analysis) with applied domains such as medical imaging and genomics. In machine learning, he develops robust algorithms for feature selection, anomaly detection, and network analysis. His contributions to quantum systems include studies of quantum walks and their continuum limits. In biomedical research, he applies spectral methods and data imputation techniques to improve diagnostic tools like infrared spectral histopathology for lung cancer classification. Recent trends in his publications emphasize multilevel optimization for medical data, stochastic analysis of complex systems, and uncovering genetic interactions via machine learning. His work often combines mathematical rigor with real-world applications in health informatics and computational biology. Notably, Kon has pioneered methods like BowSaw for inferring biological trait interactions and the Coupled VAE architecture for improved generative models. His research also addresses opioid cessation analysis and cancer survival prediction through integrated data approaches. While no specific grants or advising records are detailed here, his extensive publication record reflects a collaborative approach across mathematics, computer science, and life sciences.