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Michael P. Brenner is the Michael F. Cronin Professor of Applied Mathematics and Applied Physics and Professor of Physics at Harvard University’s School of Engineering and Applied Sciences. His research integrates applied mathematics and physics to solve complex problems in science and engineering, emphasizing theoretical modeling and collaboration with experimentalists. Key research areas include microfluidics for fiber production fluid mechanics of whale flippers rheology of colloidal suspensions self-assembly of magnetic materials atmospheric chemistry algorithms biological modeling of ion channels and hemoglobin neural connections in language processing Recent work leverages differentiable programming, large language models, and automatic differentiation across physical and biological systems. His lab has mentored students working on projects like quantum physics simulations and ALS disease severity analysis. Publications highlight interdisciplinary trends bridging fluid dynamics, computational biology, and AI-driven material design. The Brenner Group focuses on translating mathematical insights into engineering solutions and uncovering fundamental principles in natural and synthetic systems.

• Applied Mathematics
• Applied Physics
• Fluid Mechanics
• +5 more

Tom Chou is a Professor at the University of California Los Angeles (UCLA) in the Departments of Computational Medicine and Mathematics. He is also affiliated with the Bioengineering Department, Physiology IDP, Bioinformatics IDP, and Statistical & Biomathematical Consulting Center. Ph.D., Physics, Harvard University M.A., Physics, Harvard University S.B., Physical Chemistry, MIT His research spans applied mathematics, statistical physics, and theoretical biophysics, focusing on stochastic processes , inverse problems , and mathematical biology . Key areas include viral entry dynamics , stem cell kinetics , cancer progression , nucleation/self-assembly , membrane biophysics , and electrokinetics . Recent work involves machine learning for stochastic differential equations , overdose mortality forecasting , and ligand discrimination in immune signaling . He has received grants from the NIH, NSF, and Army Research Office, and serves as Director of the Systems & Integrative Biology Training Grant. Fellow, American Physical Society (2013–present) Simons Travel Award (2024–2029) NIH-NHLBI R01HL146552 (2019–2024)

• Applied Mathematics
• Statistical Physics
• Stochastic Processes
• +16 more

Suvrit Sra is the Esther and Harold E. Edgerton Career Development Associate Professor in the Department of Electrical Engineering and Computer Science at the Massachusetts Institute of Technology (MIT). He is a core member of the Institute for Data, Systems, and Society (IDSS) and the Laboratory for Information and Decision Systems (LIDS). Currently, he is on leave at the Technical University of Munich (TU Munich). His research centers on the mathematics of artificial intelligence , with emphases on optimization for machine learning (especially non-convex, non-Euclidean, and geometric optimization), theory of deep learning, discrete probability, sampling methods, convex geometry, and polynomials. He also explores applications in operations research, supply chains, and large language models. His recent articles demonstrate a strong focus on Riemannian optimization, convergence analysis of gradient methods, minimax optimization in geometric spaces, and invariant representations in graph learning. Key trends include theoretical advances in non-convex optimization and applications to reinforcement learning and distributional robustness. Awards and honors include: Alexander von Humboldt Professorship for AI (2024) Criteo Faculty Research Award (2017) SIAM Outstanding Paper Prize (2011) He advises PhD students (e.g., Zelda Mariet, Chengtao Li, Hongyi Zhang) and has secured grants including an NSF BIGDATA award for interpretable learning via discrete probability. His lab affiliations include the MIT ML Group, Center for Statistics, and TILOS AI Research Institute. He co-founded Pendulum, serving as Chief Scientist.

• Machine Learning
• Optimization
• Non-convex Optimization
• +6 more

Martin J. Wainwright is the Cecil H. Green Professor at the Massachusetts Institute of Technology (MIT) , affiliated with the Department of Electrical Engineering and Computer Science (EECS) and the Department of Mathematics . He is also associated with the Statistics and Data Science Center , the Laboratory for Information and Decision Systems , and the Institute for Data, Systems and Society . His research bridges machine learning , high-dimensional statistics , and information theory , with a focus on theoretical guarantees for algorithms in reinforcement learning, optimization, and graphical models. Books : High-Dimensional Statistics: A Non-Asymptotic Viewpoint (2019, Cambridge University Press), Statistical Learning with Sparsity: The Lasso and Generalizations (2015, CRC Press). Research Themes : Statistical and computational trade-offs, robustness in adaptive learning, posterior contraction rates, and decentralized estimation. His recent work explores non-asymptotic analysis , stochastic approximation , and instance-dependent guarantees in reinforcement learning and optimization. Key contributions include minimax optimality in value estimation, variance-reduced Q-learning , and adaptive inference under elliptical constraints. Awards : IMS Medallion Lecturer , COPSS Presidents' Award , Loève Prize in Probability , Fellow of the Institute of Mathematical Statistics , NIPS Outstanding Paper Award .

• Machine Learning
• High-Dimensional Statistics
• Reinforcement Learning
• +4 more

Changho Kim is an Assistant Professor in the Department of Applied Mathematics at the University of California, Merced. His academic journey includes dual Ph.D. degrees in Applied Mathematics from Brown University (advisor: George Em Karniadakis) and Chemistry from KAIST, South Korea. Prior to his current position, he worked as a postdoctoral researcher at Lawrence Berkeley National Laboratory under John B. Bell. University: University of California, Merced School: School of Natural Sciences Department: Department of Applied Mathematics Previous Affiliation: Lawrence Berkeley National Laboratory Kim's research focuses on stochastic modeling of multi-physics phenomena in fluids and soft matter at small scales. He develops stochastic multiscale simulation methodologies, combining computational fluid dynamics, molecular dynamics, kinetic Monte Carlo methods, and machine learning to study systems with multiple time and length scales. His work aims to bridge gaps at the physics-chemistry-biology interface through a unifying computational framework. Research Keywords: Stochastic Modeling, Multi-physics Systems, Soft Matter, Computational Fluid Dynamics, Molecular Dynamics, Machine Learning Methodologies: Stochastic Processes, Multi-scale Simulations, Particle-Based Methods, High-Performance Computing Kim actively contributes to academic outreach through initiatives like the Math Problem of the Month, annual MathMagic event, weekly Math Club for undergraduates, and the Merced Math Teachers’ Circle for local educators. Contact: Email: ckim103@ucmerced.edu Office: ACS 351, 5200 N Lake Road, School of Natural Sciences, Merced, CA 95343

• Stochastic Modeling
• Multi-physics Phenomena
• Soft Matter Physics
• +5 more

Yuval Peres is a Professor at the Beijing Institute of Mathematical Sciences and Applications (BIMSA). His research spans Probability Theory , Analysis , Theoretical Computer Science , and Game Theory . He has made foundational contributions to topics such as Random Walks , Markov Chains , Fractals , and Brownian Motion , with applications in network science and mathematical physics. Research Interests : Peres’ work often intersects probability and analysis, focusing on problems like phase transitions , mixing times , Gaussian analytic functions , and homogenization . His recent papers address homogenization problems, cutoff phenomena in Markov chains, and boundary value problems via stochastic methods. Advising and Mentorship : Peres has mentored over 20 PhD students and postdoctoral scholars, many of whom hold prominent positions at institutions like MIT, UC Berkeley, and Yale. His collaborations span mathematics, computer science, and physics. Awards : Linked to the David P. Robbins Prize (specific paper not named in text)

• Probability Theory
• Analysis
• Theoretical Computer Science
• +6 more

Dr. Samuel Shen is a Distinguished Professor at the Department of Mathematics and Statistics , San Diego State University (SDSU) and a Visiting Research Mathematician at Scripps Institution of Oceanography, UCSD. He co-founded the SDSU Big Data Analytics (BDA) MS Program and currently serves as Co-Director of the Center for Climate and Sustainability Studies . Previously, he was McCalla Professor at the University of Alberta and President of CAIMS . B.Sc. in Engineering Mechanics (1982) from East China Engineering Institute M.A. (1985) and Ph.D. (1987) in Applied Mathematics from University of Wisconsin-Madison His research spans three major areas: Climate Informatics : Developed Spectral Optimal Averaging (SOA) and Ensemble Canonical Correlation Analysis (ECCA) for climate uncertainty quantification Data Science : Created US Climate Prediction Center's operational prediction tools and filed a US patent on spectral optimal gridding Nonlinear Wave Dynamics : Advanced theoretical understanding of stochastic differential equations for climate modeling Recent publications focus on 4D climate visualization , heat stress mitigation , AI democratization in climate science , and cloud-aerosol interactions . His work has secured major grants including: $2.7M NSF AI Institute grant (2023) $1.9M California Climate Action grant (2023) $2.1M NSF EaSM-3 grant (2014) Scientific accolades include: Arthur Beaumont Distinguished Service Award (CAIMS) US National Research Council Senior Fellowship Chinese Academy of Sciences Well-known Overseas Scholar He leads the SDSU Climate Informatics Lab (founded 2006) and has been featured in New York Times , NASA Top Story , and Nature Geoscience press releases.

• Data Science
• Machine Learning
• Climate Science
• +3 more

Robert Kohn is a Professor of Mathematics at the Courant Institute of Mathematical Sciences, New York University. His research bridges applied mathematics, materials science, and mathematical physics through rigorous analysis of energy-driven systems. Specializes in partial differential equations, nonlinear elasticity, and phase transformations Active in interdisciplinary collaborations with physics and engineering disciplines Key contributions to pattern formation and structural optimization problems Research focuses on: Energy-driven pattern formation in materials (superconductors, martensites, thin films) Thin film dynamics and surface relaxation mechanisms Phase transitions in crystalline solids and microstructure analysis Optimization problems in continuum mechanics and control theory Key article trends show: Developing mathematical frameworks for metamaterials and photonic doping Connecting PDE analysis to online learning and game theory Establishing energy scaling laws for complex material systems Exploring topological constraints in mechanical and electromagnetic systems His work spans both pure mathematical analysis and applied problems in materials science, with notable contributions to: Micromagnetics and magnetic microstructures Crystal growth inhibition mechanisms Geometrically constrained elastic systems Stochastic Allen-Cahn equations and phase separation

• Applied Mathematics
• Materials Science
• Mathematical Physics
• +3 more

Hongjie Dong is a Visiting Professor of Mathematics at NYU Shanghai. He holds a PhD in Mathematics from the University of Minnesota and a Bachelor of Science in Mathematics from Fudan University. Research Interests: His work focuses on advanced mathematical methodologies, including Partial Differential Equations for modeling physical phenomena, Probability theory for stochastic systems, and Numerical Analysis for computational mathematics applications. Education: PhD, Mathematics - University of Minnesota BS, Mathematics - Fudan University

• Partial Differential Equations
• Probability
• Numerical Analysis

Ioannis Karatzas is the Eugene Higgins Professor of Applied Probability at Columbia University, holding appointments in both the Department of Mathematics and Department of Statistics. His work bridges Stochastic Analysis , Mathematical Finance , and Optimization , with a focus on utility maximization, market equilibrium, and stochastic control problems. Research Interests: Stochastic processes, financial mathematics, optimal stopping, singular control, and portfolio optimization. His contributions include foundational work on martingale methods in incomplete markets and the development of stochastic portfolio theory. Books: Co-authored Brownian Motion and Stochastic Calculus and Methods of Mathematical Finance , both standard references in stochastic analysis and quantitative finance. Teaching: Taught advanced courses like Stochastic Methods in Finance and Probability , covering topics from arbitrage theory to optimal consumption. Doctoral Students: Advised 18 PhD students, including notable researchers in stochastic control, financial economics, and optimal stopping.

• Stochastic Analysis
• Mathematical Finance
• Probability Theory
• +3 more

Andreas Buttenschoen is an Assistant Professor in the Department of Mathematics and Statistics at the University of Massachusetts Amherst. His research bridges applied mathematics, computational biology, and biophysics, focusing on collective cell behaviors across biological systems. His research interests include Mathematical Biology , Non-local PDEs , Reaction-Diffusion Equations , Computational Biology , and Cell-Based Models . He develops theoretical frameworks and computational tools to study tissue mechanics, cell adhesion, and migration in contexts such as wound healing and cancer metastasis. Nonlocal adhesion and migration models Continuum and agent-based simulations Biomechanics and biochemical signaling Applications in embryogenesis and fibrosis His recent publications highlight advancements in non-local partial differential equations for modeling cell clustering, biomechanical drivers of fibrotic scarring, and computational frameworks for individual cell dynamics. No scientific awards or advisory roles are documented in available sources.

• Mathematical Biology
• Non-local PDEs
• Numerical and analytical bifurcation theory
• +5 more

Minghan Chen is an Associate Professor in the Computer Science Department at Wake Forest University and a Z. Smith Reynolds Foundation Faculty Fellow. Her research bridges computational methods with biological applications, focusing on developing innovative algorithms for understanding complex biological systems and diseases. Education: PhD from Virginia Tech (2019) Dr. Chen's research interests center on multiscale modeling, knowledge-guided machine learning, and parameter optimization algorithms specifically designed for bio-related applications. Her work particularly focuses on Alzheimer's disease progression, where she develops computational frameworks to model the spatiotemporal dynamics of neuropathological events. She integrates techniques from computational biology, bioinformatics, and machine learning to create models that can simulate and predict disease progression at multiple scales. Her recent publications demonstrate a strong trend toward increasingly sophisticated computational approaches to Alzheimer's disease research, with particular emphasis on brain network analysis, single-cell genomics, and multimodal data integration. Her work combines graph theory, deep learning, and multiscale modeling to create comprehensive frameworks that capture the complexity of neurodegenerative processes. Awards and Recognition: Z. Smith Reynolds Foundation Faculty Fellow Dr. Chen actively mentors students in her research group, currently recruiting both graduate and undergraduate students interested in computational biology and machine learning. She has successfully guided students to recognition, including Jingwen who received the best poster award at ACM-BCB in 2022. Her research is supported by stipend funding for students engaged in rigorous, publication-quality research. Dr. Chen has organized significant academic events including the Biological Modeling and Mining workshop (BMM, 2021) and the Virtual Deep Learning Bootcamp (May 2022), demonstrating her commitment to advancing computational methods in biological sciences.

• Computational Biology
• Bioinformatics
• Machine Learning
• +5 more

Samuel Stechmann is a Professor in the Department of Mathematics at the University of Wisconsin–Madison, with significant affiliations as Faculty in both the Department of Atmospheric & Oceanic Sciences and the Center for Climatic Research. His work bridges applied mathematics and atmospheric science through the development of mathematical models for climate and weather phenomena. Stechmann's research focuses on the mathematical modeling of atmospheric dynamics, particularly tropical intraseasonal oscillations, moist convection, and cloud processes. His work combines partial differential equations, stochastic modeling, and computational methods to address fundamental questions in climate science. Key areas include the Madden-Julian Oscillation, precipitating quasi-geostrophic systems, and the mathematical representation of phase changes in atmospheric models. His approach often integrates theoretical analysis with numerical simulation to develop physically consistent models. Analysis of his recent publications reveals a strong emphasis on developing novel mathematical frameworks for atmospheric phenomena, with particular attention to moist processes, phase changes, and multiscale interactions. His work frequently bridges pure mathematical theory with practical applications in climate modeling, featuring innovative approaches to radiative transfer, sea ice dynamics, and tropical weather systems. The publications show consistent collaboration with leading atmospheric scientists while maintaining a strong mathematical foundation. Stechmann teaches a range of mathematics courses from undergraduate calculus to advanced graduate topics in applied mathematics, including specialized courses on data-driven dynamical systems and stochastic modeling. His research program appears to be well-funded through collaborations with atmospheric science groups, though specific grant details aren't provided in the available information. He maintains active research software projects including Singular Spectrum Analysis with Conditional Predictions (SSA-CP) for real-time state estimation and Multiscale Eddy Simulation (MES) for moist atmospheric convection, demonstrating his commitment to developing practical computational tools alongside theoretical advances.

• Atmospheric Science
• Climate Modeling
• Partial Differential Equations
• +5 more

Assad Anshuman Oberai is a Professor at the University of Southern California's Viterbi School of Engineering . His research spans computational mechanics, inverse problems, and machine learning applications in nuclear engineering and biomedical imaging. Key Research Areas: Multi-fidelity modeling, Bayesian inference in mechanics, conditional GANs for posterior estimation, ultrasonic sensing of nuclear fuel canisters, mechano-microscopy for tumor elasticity imaging Methodological Contributions: Novel operator network architectures, dimension-reduced Bayesian frameworks, active noise cancellation techniques, and error estimation in variational multiscale methods Recent work focuses on non-invasive nuclear fuel inspection using ultrasonic sensing and machine learning, achieving significant signal-to-noise ratio improvements with active noise cancellation. In biomedical domains, his team develops cGAN-enhanced elasticity imaging for cancer differentiation, outperforming traditional algebraic methods. Methodologically, he pioneered VarMiON (Variationally Mimetic Operator Networks) with rigorous error analysis, demonstrating superior performance over DeepONet in partial differential equation approximations. His publications address high-dimensional inverse problems through generative models, with applications in epidemic modeling (capturing algebraic decay via behavioral feedback), 3D traction microscopy accounting for cell-induced matrix degradation, and compressible phase change simulations using discontinuous finite element methods. Co-authors include researchers from mechanical engineering, biomedical informatics, and nuclear safety domains.

• Computational Mechanics
• Machine Learning
• Inverse Problems
• +7 more

Professor Hung Vinh Tran is a faculty member in the Department of Mathematics at the University of Wisconsin Madison, where he maintains his office in Van Vleck Hall (715 Van Vleck). His research focuses on partial differential equations, with particular expertise in Hamilton-Jacobi equations, viscosity solutions, and homogenization theory. Professor Tran actively contributes to the mathematical community through editorial work, seminar organization, and mentoring of graduate students and postdocs. Professor Tran's research spans multiple areas of partial differential equations, with primary contributions to Hamilton-Jacobi equations and their applications. His work explores viscosity solutions, homogenization theory, and connections between PDEs and dynamical systems. He has made significant advances in understanding large time behavior of solutions, optimal convergence rates in homogenization, and mathematical analysis of mean curvature flows. His research bridges pure and applied mathematics with applications in mathematical physics, control theory, and geometric analysis. Professor Tran serves as an associate editor for the SIAM Journal on Mathematical Analysis and Advances in Continuous and Discrete Models. He was previously editor for Minimax Theory and its Applications (2020-2025). His editorial contributions reflect his standing in the mathematical community and expertise in nonlinear PDEs. Professor Tran has advised numerous PhD students who have gone on to prestigious positions: Yeoneung Kim (Ph.D. 2019) - Now Assistant Professor at SeoulTech Son Nguyen Thai Tu (Ph.D. 2022) - Now Visiting Assistant Professor at Michigan State University Yuxi Han (Ph.D. 2024) - Now Golomb Visiting Assistant Professor at Purdue University Jiwoong Jang (Ph.D. 2024) - Now Novikov postdoctoral fellow at University of Maryland His research is supported by multiple prestigious awards: Vilas Faculty Early-Career Investigator Award (2022-2024) Simons Fellowship (academic year 2021-2022) NSF CAREER grant DMS-1843320 Distinguished paper award from ICCM (2017) Current NSF grant DMS-2348305 (2024-2027) Professor Tran actively organizes mathematical events including the Virtual Analysis and PDE Seminar (VAPS) connecting researchers worldwide. He has organized multiple Summer Meetings in PDE in Vietnam (Gặp Gỡ Mùa Hè) fostering international collaboration. His 'Diễn Giải Toán Học' series and 'toan0mau' YouTube channel featuring discussions with prominent mathematicians like Ngo Bao Chau have made advanced mathematical concepts accessible to Vietnamese-speaking audiences.

• Partial Differential Equations
• Hamilton-Jacobi Equations
• Viscosity Solutions
• +3 more