Find Academic Supervisors

Luca GemignaniView profile

Professor

• Numerical Analysis
• Linear Algebra
• Structured Matrices
• +3 more

Prof. Luca Gemignani is a Full Professor of Numerical Analysis at the Department of Computer Science, University of Pisa. His research focuses on structured matrices, eigenvalue computations, nonlinear equations, and symbolic-numeric techniques. He has held academic positions since 1990, including Associate Professor at the University of Pisa's Mathematics Department before transitioning to his current role in 2012. Education: Laurea in Mathematics (University of Pisa, 1987-88). Research Interests: Fast algorithms for structured linear systems, eigenvalue problems, matrix factorizations, and applications in computational mathematics. His work integrates numerical and symbolic methods for solving algebraic and geometric problems. Teaching: Courses include Numerical Analysis, Scientific Computing, and Parallel Computing, taught across multiple academic years. He has contributed to curricula in Mathematics, Computer Science, and Engineering. Publications: Over 50 peer-reviewed articles on numerical linear algebra, matrix computations, and algorithm design. Recent work emphasizes efficient algorithms for quasiseparable matrices and polynomial root-finding.

• Computational Fluid Dynamics
• Low Reynolds Number Hydrodynamics
• Integral Equation Methods
• +3 more

Dr. Mary Catherine Kropinski is a Professor (On Leave) in the Department of Mathematics at Simon Fraser University (SFU), part of the Faculty of Science. She holds a PhD in Applied Mathematics from Rensselaer Polytechnic Institute (1993), followed by a postdoctoral appointment at the Courant Institute at NYU. Her research focuses on computational fluid dynamics and low Reynolds number hydrodynamics, with an emphasis on developing integral equation methods for solving fluid dynamics equations like Stokes and Navier-Stokes. These methods are applied to complex systems such as microfluidic flows, sedimentation processes, and biological flows. Dr. Kropinski has been recognized with the NSERC Women's Faculty Award and has contributed to peer review for journals like the Journal of Computational Physics . Her work bridges theoretical advancements in numerical methods with practical applications in engineering and environmental science. She has served on NSERC committees and has authored numerous influential papers on fluid dynamics modeling and algorithm development.

• Applied Mathematics
• Partial Differential Equations (PDE)
• Numerical Analysis
• +5 more

Nilima Nigam is a Professor of Mathematics at Simon Fraser University (SFU), part of the Department of Mathematics within the Faculty of Science. She holds a Ph.D. in Applied Mathematics from the University of Delaware (1999). Her research focuses on numerical analysis of partial differential equations (PDEs), computational electromagnetics, mathematical physiology, and biomechanics. She is also actively involved in interdisciplinary projects, such as modeling muscle-tendon complexes and spectral geometry problems. Nigam serves on editorial boards for journals like the SIAM Journal on Scientific Computing and Mathematics-in-Industry Case Studies. She supervises graduate and undergraduate students in applied mathematics, with a focus on structure-preserving discretizations, computational biology, and spectral problems. Her work includes collaborations with researchers in biomechanics, notably with James Wakeling in studying muscle mechanics and energy dynamics. She has mentored numerous students through programs like the SFU Department of Mathematics USRA competition. Her teaching spans advanced courses in numerical analysis, PDEs, and mathematical physics.

William RundellView profile

Professor

• Partial Differential Equations
• Inverse Problems
• Fractional Calculus
• +3 more

William Rundell is a Professor of Mathematics at Texas A&M University, affiliated with the Department of Mathematics in the College of Arts & Sciences. He holds a Ph.D. and B.Sc. from Glasgow University (1974 and 1971, respectively). His research focuses on inverse problems for partial differential equations, fractional calculus, and numerical methods for ill-posed systems. Notable interests include parameter identification in elliptic/parabolic equations, inverse scattering, and subdiffusion models. Education: B.Sc. (Hons) Mathematics and Natural Philosophy, Glasgow University, 1971. Ph.D., Glasgow University, 1974. Research Interests: Inverse problems for PDEs and fractional operators. Ill-conditioned problems in integral and differential equations. Fractional diffusion, subdiffusion, and numerical algorithms. Nonlinear inverse problems and iterative methods. Teaching & Mentoring: Offered Honors Calculus and ODE courses (Fall 2022). Guides undergraduate research projects in differential equations and mathematical cryptography. NSF-funded summer research opportunities for students. Labs/Teams: Engages in collaborative research on inverse problems, fractional calculus, and numerical analysis.

Lothar ReichelView profile

Professor

• Numerical Analysis
• Scientific Computing
• Ill-Posed Problems
• +3 more

Lothar Reichel is Professor in the Department of Mathematical Sciences at Kent State University, specializing in Numerical Analysis and Scientific Computing. His research focuses on developing computational methods for large-scale problems in linear algebra and inverse problems. Research domains include: Algorithms for ill-posed inverse problems and image restoration Large-scale eigenvalue and singular value computations Iterative methods for linear systems Orthogonal polynomials and quadrature rules Structured matrix computations Professor Reichel founded the first electronic mathematics journal, Electronic Transactions on Numerical Analysis (ETNA), featuring innovative publication approaches including interactive numerical experiments. He has organized numerous conferences on numerical linear algebra and inverse problems.

• Partial Differential Equations
• Probability Theory
• Stochastic Homogenization
• +3 more

Charles Smart is a Professor of Mathematics at Yale University, located at Kline Tower - 219 Prospect Street, New Haven, CT 06511. His research focuses on partial differential equations, probability theory, and stochastic homogenization, with notable contributions to elliptic equations, statistical physics, and discrete models such as the Abelian sandpile. He holds a prominent academic position within the Department of Mathematics at Yale's Faculty of Arts and Sciences. Smart's work bridges theoretical mathematics with applications in physics and engineering, addressing topics like unique continuation properties, localization phenomena, and scaling limits. His recent publications explore advanced concepts such as large-scale analyticity in elliptic systems, free boundary problems, and the interplay between singular integrals and mixing flows. He has contributed to foundational studies in stochastic homogenization, including quantitative error estimates and convex integral functionals. His research also extends to geometric analysis, such as convex hull peeling and facet formation in free boundary problems. No specific grants or awards are listed in the provided information. His advising record remains unspecified, though his academic standing suggests significant mentorship potential.

Eliza O’ReillyView profile

Assistant Professor

• Mathematics of Data Science
• Stochastic and Convex Geometry
• Point Processes
• +4 more

Eliza O’Reilly is an Assistant Professor in the Department of Applied Mathematics & Statistics at Johns Hopkins University (JHU) and a member of the Data Science and AI Institute. Her research focuses on the mathematical foundations of data science, combining stochastic and convex geometry, high-dimensional probability, and statistical learning theory. She develops geometric models and algorithms for analyzing complex data structures, with applications in machine learning, optimization, and high-dimensional statistics. Dr. O’Reilly holds a B.S. in Mathematics from the University of Pittsburgh (2013) and a Ph.D. in Mathematics from the University of Texas at Austin (2019), where she was advised by François Baccelli. She completed a postdoctoral fellowship at the California Institute of Technology (2019–2022), sponsored by Venkat Chandrasekaran and Joel Tropp. Her work has been supported by NSF fellowships, including a NSF Graduate Research Fellowship and a NSF Postdoctoral Research Fellowship. Her research interests include: Randomized partitioning algorithms (e.g., Mondrian forests) Regularization in statistical inference and inverse problems Convex and nonconvex optimization methods Determinantal point processes and repulsive models High-dimensional geometry of random convex sets Her publications span theoretical guarantees for machine learning algorithms, optimization frameworks for data-driven problems, and geometric analysis of stochastic processes. Notable contributions include minimax analyses for random tessellation forests and spectral methods for convex regression. She actively collaborates with researchers in applied mathematics, statistics, and computer science.

• Multiscale Problems
• Probabilistic Partial Differential Equations
• Numerical Analysis
• +3 more

Assoc Prof Viet Ha Hoang is an Associate Professor and Assistant Chair (Academic) in the School of Physical and Mathematical Sciences (SPMS), Division of Mathematical Sciences at Nanyang Technological University (NTU). He holds a PhD in Mathematics from the University of Cambridge and has extensive research experience in multiscale modeling and numerical analysis. His work focuses on stochastic processes, partial differential equations, and applications in computational science. Education: Bachelor of Mathematics, University of Wollongong, 1996 PhD in Mathematics, University of Cambridge, 2000 Research interests include multiscale problems , probabilistic PDEs , and numerical methods , with contributions to homogenization theory, Bayesian inversion, and computational fluid dynamics. Current grants support his work on multiscale systems and stochastic modeling. Key achievements include pioneering finite element methods for multiscale equations and developing Bayesian frameworks for inverse problems. His lab collaborates on interdisciplinary projects in mathematical physics and biomedical engineering.

• Combinatorics
• Graph Theory
• Discrete Mathematics
• +3 more

Dr. Ku Cheng Yeaw is a Senior Lecturer at the Division of Mathematical Sciences, School of Physical and Mathematical Sciences, Nanyang Technological University (NTU). He holds a PhD from Queen Mary, University of London (2005) and has held prior academic positions at the California Institute of Technology (Harry Bateman Research Instructor, 2005–2008) and the National University of Singapore (Visiting Fellow/Lecturer/Senior Lecturer, 2008–2016). His research focuses on combinatorics, graph theory, and discrete mathematics, with notable contributions to eigenvalue analysis, intersecting families, and extremal set theory. He has an extensive publication record spanning over two decades, addressing topics such as hypergraphs, permutation patterns, and spectral properties of graphs. Dr. Ku has not been explicitly noted for awards or student advisement in the provided text.

Karl-Mikael PerfektView profile

Professor

• Operator Theory
• Complex Analysis
• Harmonic Analysis
• +2 more

Professor Karl-Mikael Perfekt holds a Chair in mathematical analysis at the Department of Mathematical Sciences, NTNU. His research focuses on operator theory, complex analysis, harmonic analysis, potential theory, and spectral theory. He earned his Ph.D. from Lund University in 2013 under Alexander Aleman. Prior roles include Associate Professor at NTNU (2021–2023), Lecturer at the University of Reading (2017–2021), and postdoctoral positions at the University of Tennessee and NTNU. Perfekt has secured major grants including the Zemánek Prize (2018), EPSRC Research Grant (2019–2021), and a Norwegian Research Council project (2023–2027). He mentors postdocs like Carlos Mudarra and Marta de León-Contreras and supervises PhD students such as Athanasios Kouroupis. His editorial work includes roles at Analysis and Mathematical Physics . His recent publications explore Dirichlet series, Hardy kernel matrices, plasmonic eigenvalues, and spectral theory. Research highlights include contributions to functional analysis, PDEs, and applied mathematics, often addressing boundary value problems and operator properties in complex geometries.

Olga MulaView profile

Associate Professor

• Numerical Analysis
• Partial Differential Equations
• Scientific Machine Learning
• +3 more

Olga Mula is an Associate Professor of Mathematics at TU Eindhoven and will transition to a Full Professor position at the University of Vienna starting September 2025. Her research focuses on numerical analysis of Partial Differential Equations (PDEs) combined with data-driven methodologies, including Scientific Machine Learning, High-Dimensional Approximation, and Optimal Transport. She leads the Computational PDEs group and has held roles at institutions like INRIA Paris and Paris Dauphine University. Mula's work addresses challenges in computational science, such as pollution modeling, epidemiology, and nuclear engineering. She currently seeks PhD and Postdoc candidates for projects on Wasserstein Gradient Flows and singularly perturbed PDEs. Education: PhD in Applied Mathematics (Sorbonne University, 2014), French Habilitation (Paris Dauphine University, 2021) Research Interests: Machine Learning for PDEs, Data Assimilation, Numerical Optimal Transport Her contributions include advancements in PINN frameworks, model order reduction, and sensor placement strategies. She teaches courses on Numerical Analysis, Optimization, and Mathematics of Neural Networks at TU Eindhoven.

Chi-Wang ShuView profile

Professor

• Numerical Solutions of Conservation Laws
• Computational Fluid Dynamics (CFD)
• Discontinuous Galerkin Methods
• +5 more

Chi-Wang Shu is the Stowell University Professor of Applied Mathematics and former Chair of the Division of Applied Mathematics at Brown University. His research focuses on high-order numerical methods for convection-dominated partial differential equations, including ENO/WENO schemes, discontinuous Galerkin methods, and spectral methods. He has contributed extensively to computational fluid dynamics, semiconductor modeling, and Hamilton-Jacobi equations. Shu has held academic positions at Brown since 1987 and has received accolades such as the SIAM/ACM Prize in Computational Science (2007) and SIAM/AMS Fellowships (2009, 2012). His work bridges theoretical development and practical applications in fluid dynamics, astrophysics, and materials science. Education: B.S., University of Science and Technology of China (1982); Ph.D., UCLA (1986). Affiliations: Brown University (1987–present), Chair of Applied Mathematics (1999–2005). Research interests span numerical methods for conservation laws, with emphasis on shock-capturing algorithms and their application to multiphysics problems. Notable contributions include the development of WENO schemes and DG methods for complex flows. He has authored over 400 publications and advised numerous students in computational mathematics and engineering. Key awards: NASA Public Service Group Achievement Award (1992), Feng Kang Prize (1995), ISI Highly Cited Author (2004).

Anna MaltsevView profile

Senior Lecturer

• Probability
• Mathematical Physics
• Quantitative Biology
• +6 more

Dr. Anna Maltsev serves as a Senior Lecturer in Applied Probability and Royal Society University Research Fellow at Queen Mary University of London's School of Mathematical Sciences. She joined Queen Mary in August 2017 after holding positions at Bristol University (2013-2017) and the University of Bonn (2010-2012). Dr. Maltsev earned her Ph.D. from the California Institute of Technology in 2010 under Barry Simon's supervision. Her research spans probability, mathematical physics, quantitative biology, and analysis with specific focus on random matrix theory, calcium signaling in cardiac cells, spectral theory, and quantum graphs. Her doctoral work examined spectral theory of Schrödinger operators on the half-line. Her interdisciplinary publications reveal consistent application of advanced mathematical techniques to cardiac physiology, particularly in understanding calcium signaling mechanisms in pacemaker cells. Dr. Maltsev's work demonstrates how mathematical principles like phase transitions and random matrix theory govern fundamental biological processes in the heart. Her significant research funding includes: Royal Society University Research Fellowship renewal (2023-2026): £370,397 Royal Society Enhanced Research Expenses (2023-2026): £246,000 Royal Society University Research Fellowship (2017-2023): £475,484 Leverhulme Early Career Fellowship (2013-2017) Dr. Maltsev supervises PhD students including Olivia Auster (researching eigenvalue distributions of random matrices) and collaborates with Dr. Mohammed Osman. Her work is conducted within Queen Mary's Centre for Complex Systems, where she bridges mathematical theory with biological applications to advance understanding of cardiac function.

• Quantum Information Science
• Quantum Optics
• Theoretical Condensed Matter Physics
• +3 more

Steven M. Girvin is the Sterling Professor of Physics and Professor of Applied Physics at Yale University. He is a Founding Director of the Co-Design Center for Quantum Advantage at Brookhaven National Laboratory and has served as Yale’s Deputy Provost for Research (2007–2017). His research focuses on quantum information science, quantum optics, and theoretical condensed matter physics, particularly in the development of circuit QED as an industrial standard for superconducting quantum computing. Girvin holds an honorary degree from Chalmers University and is a member of the US National Academy of Sciences and the Royal Swedish Academy of Sciences. Education: BS in Physics (Bates College), MS (University of Maine), PhD in Theoretical Physics (Princeton University, 1977). His work includes seminal contributions to the fractional quantum Hall effect (Oliver E. Buckley Prize, 2007) and quantum error correction. Current research emphasizes scalable quantum architectures and fault-tolerant systems. Students include Shraddha Singh (current graduate student) and alumni Baptiste Royer (Université de Sherbrooke) and Yaxing Zhang (Google Quantum). Key affiliations include the Yale Quantum Institute, YINQE, and the Department of Applied Physics. Recent research trends span quantum error correction codes, hybrid architectures, and lattice gauge theory simulations. Awards: Oliver E. Buckley Prize, Honorary Degree (Chalmers), National Academy memberships Grants/Leadership: Co-Design Center Director, DOE-funded initiatives Labs: Yale Quantum Institute, Condensed Matter Theory Group

Antti HannukainenView profile

Associate Professor

• Numerical Analysis
• Partial Differential Equations
• Inverse Problems
• +3 more

Antti Hannukainen is an Associate Professor at the Department of Mathematics and Systems Analysis, Aalto University, within the School of Science. His research focuses on numerical analysis, partial differential equations, inverse problems, and scientific computing. He leads projects like FAME (Flagship of Advanced Mathematics for Sensing, Imaging and Modelling) and contributes to computational methods for biomedical imaging, material science, and engineering applications. Education: Doctor of Science (Technology), Aalto University Affiliations: Numerical Analysis research group, Aalto University Research Interests: His work spans numerical methods for PDEs, finite element analysis, inverse problems in medical imaging, and optimization of engineering systems. Recent projects include modeling light transport in neonatal heads and topology optimization of magnetic materials. Teaching: He teaches courses in numerical analysis, matrix theory, and optimal experimental design at bachelor’s, master’s, and doctoral levels. Supervises theses in computational mathematics and engineering. Collaborations: Active in international collaborations, including the FAME initiative and work on eddy current problems in electromagnetism.