Wilfrid Gangbo is a Professor of Mathematics at UCLA, specializing in nonlinear analysis, partial differential equations, and calculus of variations. He received his Ph.D. from EPFL (Switzerland) in 1992 and maintains active research in mathematical physics and optimization theory. Research interests focus on: Calculus of variations and nonlinear PDEs Optimal transport theory and Wasserstein spaces Mean field games and kinetic theory Functional analysis with applications to fluid mechanics Recent publications demonstrate strong focus on: Hamilton-Jacobi equations in metric spaces Structure of Wasserstein spaces Connections between optimal transport and game theory Regularity theory for polyconvex energies Professional activities include founding EcoAfrica, an organization supporting mathematical sciences in African countries through workshops and collaborative projects since 1990. Current teaching includes advanced mathematics courses such as Math 131BH (Winter 2025).
Dr Bertrand Gauthier is a Lecturer at the School of Mathematics, Cardiff University , and a member of the Statistics and Data Science Research Group. His work bridges mathematics and data science, focusing on sampling-based approximation, spectral methods, and computational strategies for large-scale machine learning and uncertainty quantification. Current research: Sampling strategies, kernel methods, sparse approximation Past affiliations: KU Leuven (2015-2016), CNRS - Université de Nice (2012-2014), Université de Saint-Étienne (2007-2011) Research Interests include: Statistical learning and approximation theory Kernel-based modeling and integral operator approximation Low-rank matrix techniques and particle-flow methods Publications highlight his work on Nyström sampling optimization, kernel embedding of measures, and spectral decomposition for IMSE-optimal designs, spanning journals like SIAM Journal on Mathematics of Data Science and Positivity . Collaborations include researchers such as Matthew Hutchings and Kirstin Strokorb. Scientific Awards : Fellow of The Higher Education Academy Supervision includes postgraduate students Harry Bond, Alexandra Zverovich, and past supervisees like Matthew Hutchings (2024) and Michela Corradini (2024). His teaching covers Multivariate Data Analysis and Computational Statistics at undergraduate and postgraduate levels. Labs/Teams : Statistics and Data Science Research Group, Cardiff University School of Mathematics.
Alistair Sinclair is the Kikuo Ogawa and Kaoru Ogawa Professor of Computer Science in the Department of Electrical Engineering and Computer Sciences at UC Berkeley. He received his BA in Mathematics from the University of Cambridge (1982) and PhD in Computer Science from the University of Edinburgh (1988). After briefly serving on faculty at Edinburgh, he joined UC Berkeley in 1994. Sinclair has held visiting positions at DIMACS, Princeton University, Rutgers University, Microsoft Research, École Polytechnique, University of Paris-Orsay, and University of Rome III. His research explores: Randomized algorithms and Markov chain Monte Carlo methods Phase transitions in statistical physics Algorithmic applications of stochastic processes Nonlinear dynamical systems Combinatorial optimization Analysis of Sinclair's recent publications (2017-2025) reveals strong emphasis on statistical physics models (especially Ising and random-cluster systems), Markov chain dynamics, phase transitions, and algorithmic solutions for combinatorial problems. Key methodologies include spatial mixing analysis, entropy decay measurements, and deterministic approximation techniques. Scientific Awards: 1996 ACM-EATCS Gödel Prize 2006 Fulkerson Prize 2017 SIGACT Distinguished Service Prize Sinclair has advised 17+ PhD students including notable researchers in theoretical computer science and mathematics. He served as Founding Associate Director (2012-2017) of the Simons Institute for the Theory of Computing, receiving recognition for developing its research programs on probability, geometry, and computational complexity.
Olga Holtz is a Professor in the Department of Mathematics at the University of California-Berkeley, appointed in 2007. Her research spans applied mathematics, algebra, and computational complexity. Research Interests: Numerical analysis, matrix theory, algebra and combinatorics, computational complexity. Her recent publications focus on communication-efficient algorithms, matrix theory, and zonotopal algebra. Key trends include interdisciplinary work bridging theoretical mathematics with high-performance computing challenges. Contact: holtz@math.berkeley.edu . Personal website: http://www.cs.berkeley.edu/~oholtz/ .
Michael J. Lindsey is an Assistant Professor in the Department of Mathematics at the University of California, Berkeley, and a Faculty Scientist at Lawrence Berkeley National Laboratory. His research focuses on computational methods driven by Numerical Linear Algebra , Optimization , and Randomization , particularly for High-Dimensional Scientific Computing in quantum many-body problems and applied probability. University : UC Berkeley (Assistant Professor since 2022) Lab Affiliation : Mathematics Group at Lawrence Berkeley National Laboratory Email : lindsey@berkeley.edu His work includes Semidefinite Relaxation for quantum and classical problems, Monte Carlo Sampling techniques, and Tensor Networks for high-dimensional functions. He has pioneered Variational Embedding theory with guaranteed energy bounds and scalable solvers for quantum systems. Recent publications span Quantum Chemistry , Machine Learning , and High-Dimensional Probability , with applications to Electronic Structure , Molecular Dynamics , and Optimal Transport . He received the 2024 Hellman Fellowship and the 2019 SIAM Student Paper Prize . Teaching includes graduate and undergraduate courses in numerical analysis and applied mathematics at UC Berkeley and New York University. He also organizes the HDSC Seminar on high-dimensional scientific computing.
Oliver Mason is a Professor at Maynooth University's Faculty of Science & Engineering , specializing in Mathematics and Statistics . His academic career includes significant contributions to systems theory, matrix analysis, and data privacy. PhD in Lyapunov Stability Theory (2004, Maynooth University) Prior work in satellite communications and Institute of Technology sector Research Interests : Systems and Control Theory for switched systems with time-delay and uncertainty Matrix Theory including D-stability and diagonal stability Mathematics of Data Privacy with focus on differential privacy Max-Algebra applications in asymptotic behavior analysis Wave Energy Converters (WECs) performance optimization Recent Publications show expertise across Ecological Modeling (2023), Control Theory (2022-2018), Linear Algebra (2018-2015), and Data Privacy (2020-2016). Teaching includes modules like Numerical Analysis, Mathematical Biology, and Computational Tools for Research. Professional Roles : Organizer of Hamilton Institute Workshops, TPC member for multiple international conferences.
Somdatta Goswami serves as Assistant Professor in Civil and Systems Engineering and Applied Mathematics and Statistics at Johns Hopkins University, with dual affiliations at the Institute for Data Intensive Engineering and Science (IDIES) and Hopkins Extreme Materials Institute (HEMI). She leads the Centrum IntelliPhysics research group developing AI-driven methodologies for scientific discovery. Her educational trajectory includes: Bachelor's in Civil Engineering from Birla Institute of Technology, Mesra (2011) Master's in Structural Engineering from Indian Institute of Engineering Science and Technology (2013) PhD in Civil Engineering and Structural Mechanics from Bauhaus University-Weimar, Germany (2020) funded by DAAD Dr. Goswami's research pioneers Scientific Machine Learning at the intersection of computational mechanics and AI, focusing on neural operator architectures that accelerate physics-based simulations. Her group develops methods for long-time horizon prediction, multiscale multiphysics modeling, and real-time inference in complex systems through latent space representations and physics-informed learning. Current emphases include cardiac digital twins, structural response under natural hazards, and RNA electrophoresis modeling. Analysis of her 2024-2025 publications reveals dominant trends in latent operator learning, physics-informed neural networks, and hybrid solvers combining traditional numerical methods with deep learning. These innovations enable breakthroughs in computational efficiency across engineering and biological domains, particularly in multiscale modeling and uncertainty-aware simulation. Her scientific recognition includes: National Science Foundation’s National Artificial Intelligence Research Resource (NAIRR) Pilot Johns Hopkins University Discovery Award 2024 Dr. Goswami mentors PhD candidates including Dibakar Roy Sarkar (Creel Family Engineering Fellow), Sharmila, and Maryam. Major research funding comprises: NSF grant for "Cardiac Digital Twins" with Kevrekidis, Trayanova, and Maggioni NSF grant for exascale AI-integrated simulations with UT Austin DOE grant for uncertainty-informed latent operators with Shields, Graham-Brady, and Kevrekidis Johns Hopkins Discovery Award for biological systems modeling The Centrum IntelliPhysics group operates within JHU's Latrobe Hall, collaborating with IDIES and HEMI on interdisciplinary projects spanning computational mechanics, materials science, and biological systems. Their work integrates high-performance computing with novel neural architectures to solve previously intractable scientific problems.
Dmitriy (Tim) Kunisky is an Assistant Professor in the Department of Applied Mathematics and Statistics at Johns Hopkins University's Whiting School of Engineering. He is also affiliated with the Data Science and AI Institute, the Department of Mathematics, and the Algorithms and Complexity Group at Johns Hopkins. Dr. Kunisky received his bachelor's degree in mathematics from Princeton University, worked as a software engineer for Google, earned his PhD in mathematics from the Courant Institute at NYU under the supervision of Afonso Bandeira and Gérard Ben Arous, and was a postdoctoral associate in computer science at Yale University before joining Johns Hopkins. His research broadly concerns how probability theory and mathematical statistics interact with computational complexity and the theory of algorithms. He investigates the mathematical phenomena that govern the power and limitations of algorithms processing massive and high-dimensional inputs, drawing on asymptotic statistics, convex geometry, random matrix theory, statistical physics, and representation theory. His work includes studying convex relaxation algorithms on combinatorial optimization problems, computational intractability in high-dimensional statistics, pseudorandomness, and experimental approaches to number theory and combinatorics. His recent publications demonstrate a consistent focus on the intersection of computational complexity, statistical inference, and random matrix theory. There's a clear trajectory from theoretical foundations to practical algorithmic applications, with particular emphasis on information-computation gaps, spectral methods, and the sum-of-squares hierarchy. His work often bridges theoretical computer science with statistical physics approaches. Dr. Kunisky actively advises graduate students at Johns Hopkins, including PhD candidates in Applied Mathematics and Statistics. He has taught courses on Random Matrix Theory in Data Science and Statistics, Probability Theory, Sum-of-Squares Optimization, and Modern Probability for Theoretical Computer Science, demonstrating his commitment to both research and education in mathematical data science.
Terence Tao is an Australian-American mathematician and professor of mathematics at the University of California, Los Angeles (UCLA), where he holds the James and Carol Collins Chair in the College of Letters and Sciences. Widely regarded as one of the greatest living mathematicians, Tao has received numerous prestigious awards including the Fields Medal, the Breakthrough Prize in Mathematics, and the MacArthur Fellowship. Dr. Tao's educational background includes: Bachelor's and Master's degrees from Flinders University (1991) Ph.D. from Princeton University (1996) under Elias M. Stein Tao's research spans an extraordinary breadth of mathematical fields. He is particularly known for his work in harmonic analysis, partial differential equations, combinatorics, and analytic number theory. His research has included groundbreaking contributions to compressed sensing, the Green-Tao theorem on arithmetic progressions in prime numbers, and progress on the Navier-Stokes equations. Tao is renowned for his collaborative approach, having worked with over 60 co-authors throughout his career. Tao's publications demonstrate remarkable diversity across mathematical disciplines. His work shows strong trends in connecting seemingly disparate areas of mathematics, often bringing techniques from one field to solve problems in another. He has made significant contributions to both theoretical and applied mathematics, with applications ranging from signal processing to number theory. Among his numerous scientific achievements, Tao has received: Fields Medal (2006) Breakthrough Prize in Mathematics (2014) Royal Medal (2014) MacArthur Fellowship (2006) Crafoord Prize (2012) Princess of Asturias Award (2020) Tao has mentored numerous students throughout his career, with Monica Vișan among his doctoral students. He has secured significant research funding through prestigious awards including the Packard Fellowship, Sloan Fellowship, and Simons Investigator award. His collaborative research has been supported by multiple National Science Foundation grants. Tao maintains an active research group at UCLA and frequently collaborates with mathematicians worldwide. His blog and public lectures have made advanced mathematical concepts accessible to broader audiences, demonstrating his commitment to mathematical education and outreach.
Dr. Yariv Aizenbud is an Assistant Professor in the Department of Applied Mathematics at Tel Aviv University's School of Mathematical Sciences. His academic journey includes a Ph.D. in Applied Mathematics from Tel Aviv University and a Gibbs assistant professorship at Yale University's Applied Math Program. Research Focus: Statistical recovery of geometric structures Applications: Latent tree variable models, Manifold Learning, Randomized Algorithms in Numerical Linear Algebra Academic Roles: Organizes the Applied Math Seminar at Tel Aviv University Contact: Office: 108 Schreiber Building, Department of Mathematics, Tel Aviv University, Israel, 69978.
Olivier Lévêque is a Senior Scientist at the École Polytechnique Fédérale de Lausanne (EPFL), affiliated with the School of Computer and Communication Sciences. He conducts research at the Laboratory of Information Theory (LTHI) and holds teaching responsibilities in both Communication Systems (SSC) and Computer Science (SIN) sections. Additionally, he contributes to the Interface EPFL-Gymnases initiative. Lévêque obtained his Physics diploma (1995) and PhD in Mathematics (2001) from EPFL, with a visiting lectureship at Stanford University's Electrical Engineering Department in 2005-2006. His research explores fundamental aspects of information theory , random matrices , and stochastic calculus , with applications in wireless communications and network theory. Key interests include capacity scaling laws in ad hoc networks, diversity-multiplexing tradeoffs, and mathematical frameworks for communication systems. Recent publications demonstrate broad interdisciplinary engagement, spanning computational thinking assessment (2022), digital education frameworks (2019), satellite positioning systems (2018), and theoretical advances in probability (2018). His work consistently integrates mathematical rigor with practical communication challenges, particularly in wireless network optimization and information-theoretic security. He has supervised four doctoral students at EPFL and teaches courses including Information, Computation, Communication , Markov Chains and Algorithmic Applications , and Cryptography . Lévêque leads research activities within the Laboratory of Information Theory, focusing on theoretical foundations of modern communication systems.
Dr. Akwum Onwunta is a researcher affiliated with the Max Planck Institute for Dynamics of Complex Technical Systems and holds a Ph.D. in Applied Mathematics from Otto von Guericke University, Magdeburg, Germany . His work bridges computational mathematics and quantitative finance. Research Focus: Uncertainty Quantification, Stochastic PDEs, Optimal Control, Numerical Linear Algebra, Tensor-based Algorithms, and Credit Risk Modeling. Onwunta's publications emphasize low-rank methods for solving high-dimensional problems in fluid dynamics and financial risk assessment. His expertise includes stochastic Galerkin systems and preconditioning techniques for unsteady PDEs with random inputs. Notable collaborations include work with Peter Benner and Martin Stoll on computational frameworks for uncertainty propagation in fluid mechanics. His academic output spans both theoretical and applied domains.
Andreas Buttenschön is an Assistant Professor in Applied Mathematics at the University of Massachusetts Amherst, affiliated with the Department of Mathematics and Statistics. His research bridges mathematical analysis with biological systems, focusing on cellular dynamics, non-local modeling, and computational methods. Key research areas: Applied Mathematics, Mathematical Biology, Computational Biology, and Non-local Modeling Teaches advanced courses like Math 456 (Mathematical Modeling) , Math 725 (Functional Analysis) , and Math 545 (Applied Linear Algebra) His publications explore interdisciplinary topics such as cell polarization, collective migration, and cytoskeletal dynamics. Notably, his work integrates numerical methods with biological applications, including finite difference schemes for partial differential equations and stochastic simulations for cellular processes. Recent projects emphasize continuum models for cell clusters and bifurcation analysis in polarized systems.
Joseph Kileel is an Assistant Professor in the Department of Mathematics at the University of Texas at Austin, with additional appointments as a Core Faculty Member of the Oden Institute for Computational Engineering and Sciences and as a member of the Machine Learning Laboratory. His academic journey includes a Ph.D. in Mathematics from UC Berkeley (2017) under Bernd Sturmfels and a postdoctoral fellowship at Princeton University (2017-2020) with Amit Singer. Professor Kileel's research spans applied mathematics, mathematical data science, and computational algebra, with particular expertise in inverse problems for imaging science, tensor methods, and non-convex optimization. His work has important applications in cryo-electron microscopy, 3D reconstruction, and mathematical theory for machine learning algorithms. His research program is supported by the NSF, DOE, and Sloan Foundation. His publication record demonstrates consistent high-impact contributions across multiple venues including IEEE Transactions, SIAM journals, Foundations of Computational Mathematics, and NeurIPS. His recent work shows a strong trend toward developing algebraic and geometric methods for data science problems, with increasing focus on tensor decompositions and their applications to molecular imaging. His research bridges theoretical mathematics with practical computational methods. Charles Chui Young Researcher Best Paper Award Bernard Friedman Memorial Prize for Best Thesis in Applied Mathematics Professor Kileel currently advises six doctoral students and postdocs, maintaining an active research group that combines theoretical depth with practical applications. His students work on diverse projects spanning tensor methods, optimization theory, and applications to imaging science. The group benefits from strong connections with the Oden Institute and Machine Learning Laboratory at UT Austin, providing access to interdisciplinary collaborations and resources. His research group focuses on developing mathematical foundations for data science problems, particularly those involving algebraic structure. Current projects include tensor decomposition algorithms, geometric methods for 3D reconstruction, and theoretical analysis of non-convex optimization landscapes. The group maintains active collaborations with researchers at Princeton, Berkeley, and international institutions, reflecting the interdisciplinary nature of his work.
Renaud Raquépas is a Phillip Griffiths Assistant Research Professor in the Department of Mathematics at Duke University, where he has been working since 2025 under the mentorship of Professor Jonathan C. Mattingly. Prior to his position at Duke, he was a Courant Instructor in the Mathematics Department of the Courant Institute at New York University (2022-2025), hosted by Professor Lai-Sang Young, and a postdoctoral researcher at CY Cergy Paris Université (2021-2022), working with Professor Armen Shirikyan. His educational background includes a PhD in Mathematics from McGill University and Université Grenoble Alpes (2017-2020), where he was jointly supervised by Professors Vojkan Jakšić and Alain Joye. His doctoral thesis focused on "Tools and results in the study of entropy production." He also earned an MSc in Mathematics and Statistics from McGill University (2016-2017) under the supervision of Professor Vojkan Jakšić, with a thesis on "Heat full statistics and regularity of perturbations in quantum statistical mechanics." His undergraduate studies were completed at McGill University, where he also earned his Master's degree over a period of approximately five years. Raquépas's research primarily focuses on mathematical physics, with particular emphasis on time-dependent aspects of statistical mechanics and entropy production in both quantum and classical systems. His work bridges several mathematical disciplines including probability theory (particularly large deviations and stochastic differential equations), dynamical systems and ergodic theory (covering recurrence, mixing, theory of C*-algebras, and random dynamical systems), and operator theory (focusing on spectra, resolvents, perturbation theory, and one-parameter semigroups). His research addresses fundamental questions about nonequilibrium statistical mechanics, quantum information, and the mathematical foundations of thermodynamics. The most recent publications by Raquépas demonstrate a consistent focus on entropy production, large deviation principles, and the mathematical structure of statistical mechanical systems. His work spans both classical and quantum domains, with particular attention to the connections between information theory, probability, and physics. A significant portion of his research examines return times, waiting times, and their relationship to entropy estimators, while other papers explore quantum measurement processes, fermionic systems, and diffusions with various types of noise. His publications appear in prestigious journals including Communications in Mathematical Physics, Annales Henri Poincaré, and Journal of Mathematical Physics. Raquépas has presented his research at numerous international conferences and seminars, including the IEEE International Symposium on Information Theory, the International Congress of Mathematical Physics, and various departmental seminars at institutions worldwide. His work has been featured at specialized workshops on entropy, dynamical systems, and mathematical physics. As an educator, Raquépas has taught a variety of undergraduate mathematics courses at multiple institutions. At Duke University, he is scheduled to teach Probability in the Fall 2025 semester. Previously at NYU, he taught courses including Ordinary Differential Equations, Introduction to Mathematical Modeling, Linear Algebra, and Applied Complex Variables. He has also taught mathematics courses in French at CY Cergy Paris Université and Université Grenoble Alpes, demonstrating his bilingual capabilities (French is his first language, with fluency in English). Raquépas was born in the 1990s in the Province of Québec and has been involved in mathematical outreach activities, including service on the committee of the Seminars in Undergraduate Mathematics in Montréal and work on the website of the French-language mathematics magazine Accromath.
