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Thomas CassView profile

Professor

• Probability
• Stochastic Analysis
• Mathematical Data Science
• +5 more

Thomas Cass is a Professor of Mathematics at Imperial College London, affiliated with the Department of Mathematics within the Faculty of Natural Sciences. He directs the EPSRC Centre for Doctoral Training (CDT) in Mathematics of Random Systems, a collaboration with the University of Oxford, and leads DataSig II, an EPSRC Programme Grant focusing on streamed data analysis using rough path theory. His research bridges pure and applied mathematics, emphasizing stochastic analysis, rough path theory, and their applications in machine learning and finance. He holds a PhD from the University of Cambridge and previously worked at the University of Oxford, where he was a Fellow and Member of Christ Church’s Governing Body. His academic leadership includes editorial roles at journals like the Journal of the London Mathematical Society and the Bulletin of the London Mathematical Society. He has organized major conferences on stochastic analysis and computational data science at institutions like the Oberwolfach Research Institute and ICERM. His work on signature methods from rough path theory has advanced mathematical data science, with applications in radioastronomy and molecular biology. Recent EPSRC grants support foundational research in rough analysis and its integration with Large Language Models. Cass’s contributions span theoretical developments, open-source software tools, and interdisciplinary collaborations.

Andrew WynnView profile

Associate Professor

• Control and Optimization
• Fluid Mechanics
• Aeroservoelastic Control
• +7 more

Dr. Andrew Wynn is an Associate Professor in the Department of Aeronautics at Imperial College London, Faculty of Engineering. His research focuses on developing computational optimization methods to improve fluid mechanical systems, including active flow control, data-driven modelling, aeroservoelasticity, and semi-algebraic optimization techniques. He leads a research group addressing challenges in fluid-structure interactions, turbulence, and control systems. Wynn's affiliations include the Flow Control Research Group and the Aeroelastics Group. His work intersects interdisciplinary fields such as mechanical engineering, applied mathematics, and aerospace engineering. Key research topics include bluff body drag reduction, scaling laws for fluid flows, and estimator design for high-dimensional systems. Publications highlight contributions to fluid mechanics, optimization algorithms, and control theory. His group employs methods like semidefinite programming and dynamic mode decomposition to analyze and control complex flows. Ongoing projects involve wind farm optimization, global stability of viscoelastic fluids, and Bayesian optimization for experimental fluid dynamics. Education: Not explicitly stated in the provided text. Grants & Awards: Affiliated with Imperial College Research Fellowships (ICRF) and President's PhD Scholarships programs. Labs/Teams: Leads the Wynn Research Group, collaborating with the Flow Control and Aeroelastics Groups.

• Mathematical Physics
• Harmonic Analysis
• Dynamical Systems
• +3 more

Dr. Rui Han is an Assistant Professor at Louisiana State University, Department of Mathematics. He previously served as a Visiting Assistant Professor at Georgia Institute of Technology (2018–2020) and was a Member at the Institute for Advanced Study (2017–2018). His academic journey includes a B.S. in Mathematics from the University of Science and Technology of China (2008–2012) and a Ph.D. in Mathematics from the University of California, Irvine (2012–2017). Rui Han's research focuses on Mathematical Physics, Harmonic Analysis, and Dynamical Systems and Ergodic Theory. His work explores topics such as spectral theory, quasi-periodic operators, localization phenomena, and applications to quantum dynamics. He employs advanced analytical techniques to study problems in mathematical physics, including the analysis of Schrödinger cocycles, Lyapunov exponents, and the interplay between dynamical systems and spectral properties. His accolades include the Kovalevsky Outstanding Ph.D. Thesis Award (2017), the Von Neumann Award for outstanding performance (2016), and the Connelly Award for excellence in teaching and research (2014). He has secured significant grants, including the NSF CAREER Award (2022–2027) as Principal Investigator and the RTG grant in Topology, Representation Theory, and Mathematical Physics (2023–2028) as senior personnel. No specific laboratory or team affiliations are mentioned in the provided information.

• Probability Theory
• Stochastic Processes
• Mathematical Physics
• +3 more

Lisa Hartung is a Full Professor (W3) at Johannes Gutenberg University Mainz's Department of Mathematics since 2025, leading research in stochastic processes. She previously held Associate (2019-2025) and Assistant Professor (2019) positions at the same institution and served as a Courant Instructor at NYU's Courant Institute (2016-2018). Her educational background includes a PhD from Bonn University (2016) under Prof. Anton Bovier in Probability Theory. Key research interests span branching Brownian motion , Gaussian free fields , random energy models , and stochastic processes on evolving networks , with applications in statistical mechanics and mathematical physics. Her work frequently addresses extremal processes, phase transitions, and complex temperature phenomena. Analysis of her recent publications reveals dominant trends in log-correlated random fields, variable-speed branching processes, and hard-wall boundary conditions. Articles consistently explore asymptotic behavior, maximum processes, and structural properties of stochastic systems, with increasing interdisciplinary connections to computer science through data stream modeling. Scientific recognition includes: Förderpreis der Fachgruppe Stochastik (2018) Hartung secures substantial research funding as PI for multiple high-impact projects: the ANR-DFG grant Random Energy Models (2020), DFG's SPP 2265 Random Geometric Systems (2020), TRR146 Multiscale Simulation Methods (2020), and Carl-Zeiss project TOPML (2022). She also serves on the executive board of DMV Fachgruppe Stochastik (since 2023) and coordinates Jugend trainiert Mathematik for grades 8/9. Her research group participates in the DFG-funded network Stochastic Processes on Evolving Networks and collaborates internationally through workshops on interacting particle systems and stochastic population models.

Soummya KarView profile

Professor

• Decision-Making in Large-Scale Networked Systems
• Stochastic Systems
• Multi-Agent Systems
• +7 more

Soummya Kar is the Buhl Professor of Electrical and Computer Engineering at Carnegie Mellon University (CMU). He holds a Ph.D. (2010) and B.Tech (2005) in Electrical Engineering from CMU and IIT Kharagpur, respectively, and has been a Postdoctoral Research Associate at Princeton University (2010-2011). Research Interests: Decision-making in large-scale networked systems, stochastic systems, multi-agent control, and data science with applications in cyber-physical systems and smart energy grids. Scientific Awards: IEEE Fellow (2022) Dean’s Early Career Fellowship (2016) Wilton E. Scott Institute for Energy Innovation - Energy Fellow Recent Article Trends: His work focuses on distributed optimization, secure inference in sensor networks, smart grid resilience, and coded computing for fault tolerance, with keywords spanning Computer Science , Control Theory , and Energy Systems . Students & Team: He advises current and former Ph.D. students including Brian Swenson (Penn State), Javad Mohammadi (UT Austin), and Sergio Pequito (TU Delft), alongside postdoctoral researchers like Panayiotis Moutis. His research involves collaborations with labs such as CyLab Security and Privacy Institute and the Wilton E. Scott Institute for Energy Innovation.

Bixiang WangView profile

Professor

• Stochastic Partial Differential Equations
• Fractional Calculus
• Reaction-Diffusion Equations
• +3 more

Professor Bixiang Wang is a faculty member in the Department of Mathematics at New Mexico Tech. His research focuses on stochastic partial differential equations, fractional calculus, and infinite-dimensional dynamical systems with a particular emphasis on invariant measures, random attractors, and asymptotic behavior of solutions. He has taught numerous courses including Math 372 (Ordinary Differential Equations), Math 534 (Partial Differential Equations), and graduate-level topics in stochastic analysis. His work explores complex systems such as reaction-diffusion equations, wave equations, and Navier-Stokes equations under stochastic influences. Key areas of interest include fractional equations driven by superlinear noise, large deviation principles, and convergence of invariant measures in unbounded domains. He actively contributes to the theoretical foundations of stochastic dynamics and their applications in nonlinear science. Recent research trends highlight advancements in understanding long-term behavior of stochastic systems, with a focus on fractional operators and non-autonomous dynamics. His publications address critical challenges in global well-posedness, ergodicity, and bifurcation phenomena under random perturbations.

• Operator Theory
• Functional Analysis
• Mathematical Analysis
• +4 more

Dr. Josep Martinez Centelles is an Associate Professor at the Faculty of Mathematics , University of Valencia , specializing in Mathematical Analysis . His research spans operator theory, differential equations, and their applications to physics and engineering. PhD in Mathematics (1992) from University of Valencia Key research collaborations with experts in relativity, optimization, and harmonic analysis Research focuses on: Operator theory and semigroups in Banach spaces Harmonic analysis for time-frequency localization Relativistic thermodynamics and gravitational collapse modeling Computational methods in fluid dynamics and multi-objective optimization Recent work explores generalized ε-quasi solutions in set optimization (2022), enhanced LES discretization techniques (2015), and uncertainty principles in spherical mean transforms (2014). His publications appear in journals like zbMATH, Journal of Mathematical Analysis and Applications, and Computational Optimization and Applications.

Dan MikulincerView profile

Assistant Professor

• High-Dimensional Geometry
• Probability Theory
• Statistics
• +10 more

Dan Mikulincer is the Brian and Tiffinie Pang Assistant Professor at the University of Washington in the Department of Mathematics, College of Arts and Sciences. He previously held a postdoctoral Instructor position at MIT Mathematics and earned his Ph.D. from the Weizmann Institute of Science under Ronen Eldan. He completed his B.Sc. in Mathematics and Computer Science at Ben-Gurion University, where he also studied Cognitive Neuroscience. B.Sc.: Ben-Gurion University (Mathematics, Computer Science, Cognitive Neuroscience) Ph.D.: Weizmann Institute of Science, Faculty of Mathematics Postdoc: MIT Mathematics Current: Assistant Professor, University of Washington, Department of Mathematics His research lies at the intersection of high-dimensional geometry, probability, statistics, information theory, and data science. He is particularly focused on normal approximations, Stein's method, stochastic analysis, and dimension-free phenomena. His work explores foundational aspects of learning theory, random matrices, transportation inequalities, and neural networks, often using probabilistic and analytic tools to derive sharp, robust results in high dimensions. The recent publications reflect a consistent focus on probabilistic methods in high-dimensional settings. Key themes include normal approximation via Stein's method, optimal transport, concentration and anti-concentration inequalities, random graph models, and theoretical aspects of machine learning such as learnability and neural network expressivity. The work spans both pure mathematics (e.g., GAFA, PTRF) and top-tier computer science venues (e.g., COLT, STOC, NeurIPS), highlighting interdisciplinary impact. Although no formal scientific awards are listed in the provided text, his publications in premier journals and conferences (Annals of Probability, STOC, NeurIPS, COLT) indicate significant recognition in the theoretical community. Dan Mikulincer has advised or collaborated with several researchers including Yair Shenfeld, Max Fathi, Ronen Eldan, and Sébastien Bubeck. He has served as a TA for 18.650: Statistics for Applications at MIT and taught programming courses (Java, Python, JavaScript) at the Interdisciplinary Center Herzliya. He is also a senior lecturer at WeCode, a nonprofit providing free programming education to underrepresented youth in Israel, indicating a strong commitment to education and outreach. He has been affiliated with research groups at MIT Mathematics, Weizmann Institute, and Microsoft Research AI, where he spent the summer of 2019 hosted by Sébastien Bubeck. These collaborations span theoretical machine learning, stochastic processes, and algorithmic foundations.

Firas Rassoul AghaView profile

Professor

• Probability Theory
• Statistical Mechanics
• Stochastic Processes
• +7 more

Firas Rassoul-Agha is a Professor of Mathematics in the Department of Mathematics at the University of Utah, College of Science. His academic work centers on the rigorous mathematical analysis of probabilistic models arising in statistical mechanics and complex systems. His research interests include: Probability theory and its applications Random walks in random environments Random polymer measures (random paths in random potentials) Large deviations and Gibbs measures Random growth models and disordered systems Interplay between probability and mathematical physics His research seeks to uncover the mathematical laws governing systems with complex interactions, such as particles in disordered media, traffic flow, crystal growth, and biological interfaces. He has developed theoretical frameworks to understand the long-term behavior and fluctuations in such stochastic systems, often drawing analogies to foundational results like the Law of Large Numbers. There are no articles listed in the provided text, so no trend analysis can be performed. He has no listed scientific awards in the provided material. Firas Rassoul-Agha has been actively involved in academic service, notably as an organizer of the Frontier Probability Days 2018 conference at Oregon State University, supported by the National Science Foundation. He has advised numerous graduate and undergraduate courses in probability, statistics, and stochastic processes, indicating a strong commitment to teaching and mentorship. While specific grants are not detailed, his research has likely been supported through conference funding and institutional resources. There is no explicit mention of labs or research teams in the provided text, though his collaborative work on conferences suggests engagement with broader research networks.

Daniel ThompsonView profile

Professor

• Ergodic Theory
• Dynamical Systems
• Symbolic Dynamics
• +3 more

Prof. Daniel Thompson is a Professor in the Department of Mathematics at The Ohio State University, where he has served since 2012. He holds a PhD from the University of Warwick (2009) and previously worked as a Chowla Research Assistant Professor at Penn State. His research focuses on ergodic theory and dynamical systems, particularly entropy theory and its connections to geometry. Thompson is a core member of OSU's Ergodic Theory group and currently leads the NSF-funded project 'Advances in Ergodic Geometry' (2024–2027). Education: PhD in Mathematics (2009, University of Warwick) Roles: Editor of Ergodic Theory and Dynamical Systems , Ohio State Senate member (2021–present), NSF grant PI His research interests include symbolic dynamics, thermodynamic formalism, and applications to geometric problems. Notable contributions involve equilibrium states in non-positive curvature and Gibbs measures for CAT(-1) spaces. Thompson teaches advanced courses such as Ergodic Theory II and Real Analysis, and advises PhD students in dynamical systems. Grants funded include NSF CAREER Award (2015–2021) and multiple NSF standard grants. He has organized major conferences like the Midwest Dynamical Systems Conference and workshops on thermodynamic formalism. Publications focus on equilibrium states, geodesic flows, and symbolic dynamics, with collaborations on geometric applications and bioinformatics projects (e.g., coding sequence density estimation).

• Partial Differential Equations
• Zero Dispersion Limits
• Whitham Equations
• +3 more

Fei-Ran Tian is a Professor of Mathematics at The Ohio State University, holding positions since 1995. His research focuses on Partial Differential Equations, Zero Dispersion Limits, Whitham Equations, and Modulation of Dispersive Oscillations. He earned his Ph.D. from New York University's Courant Institute in 1991, advised by Peter D. Lax. Education: Ph.D. Mathematics, Courant Institute, NYU (1991) M.S. Mathematics, Clarkson University (1987) B.S. Mathematics, Jinan University, China (1984) Research Interests: His work addresses integrable systems, zero dispersion limits, Whitham equations, and free boundary problems. He has contributed to understanding modulation equations for KdV and Camassa-Holm hierarchies, and their phase transitions. Grants: NSF DMS-0404931 (2004-2007) NSF DMS-0103849 (2001-2004) NSF DMS-9622810 (1996-1999) Awards: John Simon Guggenheim Fellowship (2003) Wilhelm T. Magnus Prize (1992) Honorary Professorship at Huang-Gang Normal University (2007) Labs/Teams: Collaborations include work with T. Grava on equilibrium measures and V. Pierce on Whitham equations. His research frequently involves numerical studies and asymptotic analysis of dispersive systems.

Dmitry KorshunovView profile

Professor

• Probability Theory
• Stochastic Processes
• Markov Chains
• +3 more

Professor Dmitry Korshunov is a Professor of Probability at the School of Mathematical Sciences, Lancaster University. He maintains an active research profile in probability theory with a focus on stochastic processes and related areas. His educational background includes: School No 25 in Akademgorodok (Novosibirsk, Russia), 1973-1983 Novosibirsk State University, Mathematical Department, M.S. (1983-1988), Thesis: "Transition phenomena for Markov chains and their applications", advisor Professor A. A. Borovkov Institute of Mathematics (Novosibirsk, Russia), Candidate of Sciences degree (PhD equivalent), 1988-1990, Thesis: "Transition phenomena for real-valued Markov chains", advisor Professor A. A. Borovkov Institute of Mathematics (Novosibirsk, Russia), Doctor of Sciences degree (DSc), October 13, 2004, Thesis: "Large deviation probabilities for asymptotically space homogeneous stable Markov chains" Professor Korshunov's research focuses on advanced probability theory with particular expertise in Tail asymptotics for perpetuities in continuous time and Heavy-tailed analysis of linear functionals of random walks and Levy processes . His work bridges theoretical probability with applications in various domains requiring sophisticated stochastic modeling. He has made significant contributions to understanding Markov chains, particularly those with asymptotically zero drift, as evidenced by his upcoming 2025 monograph on Lamperti's problem. Professor Korshunov serves on the editorial boards of prestigious journals including Stochastic Processes and their Applications and Queueing Systems , demonstrating his standing in the probability community. His current research projects include: FP7 Marie Curie People Action - Risk Analysis, Ruin and Extremes (since 2012) DFG - Lamperti type limit theorems for stochastic processes with asymptotically zero drift (since 2012) RFBR - Limit Theorems in Probability Theory and Their Applications (since 2011) Professor Korshunov maintains an active PhD supervision program within the School of Mathematical Sciences at Lancaster University, continuing the academic tradition established by his advisor, Professor A. A. Borovkov, and following in the footsteps of his father, Aleksey Korshunov, who was a mathematician at the Sobolev Institute of Mathematics in Novosibirsk.

• Information Theory
• Network Security
• Coding Theory
• +7 more

Venkat Anantharam is a Professor in the Department of Electrical Engineering and Computer Sciences at the University of California, Berkeley . His research spans Information Theory , Network Security , Coding Theory , and Stochastic Processes , with a focus on theoretical foundations and applications in communication systems, game theory, and data compression. He has supervised numerous PhD and Master’s students , including Soham Phade, Payam Delgosha, and Sudeep Kamath, and hosted postdoctoral fellows such as Lei Yu and Charles Bordenave. His recent publications address advanced topics like hypercontractivity in Boolean functions, universal compression of graphical data, and game-theoretic models for security. Articles from 2019-2021 highlight work on entropy power inequalities, error bounds for Markov chains, and distributed compression techniques. Venkat's research often bridges theoretical insights with practical applications, including LDPC decoders, network coding, and risk-sensitive control.

Fraydoun RezakhanlouView profile

Professor

• Probability theory
• Partial differential equations
• Mathematical analysis
• +7 more

Professor Fraydoun Rezakhanlou is a faculty member at the Department of Mathematics , University of California, Berkeley , where he has been since 1991. His academic roles include serving as an Equity Advisor in the university's Senate Faculty and teaching advanced courses such as Math 270: Topics in Symplectic Geometry , Math 219: Dynamical Systems , and Math 279: Stochastic PDEs . He has also contributed to lecture notes on Symplectic Geometry, Dynamical Systems, and Stochastic PDEs. Research Interests: Rezakhanlou's work spans probability theory , partial differential equations , and their intersections with mathematical physics . His research includes coagulation-fragmentation models , kintetic theory , stochastic processes , and homogenization . He has explored applications to Navier-Stokes equations , Hamilton-Jacobi equations , and symplectic geometry . Article Trends: His publications focus on stochastic PDEs , Hamilton-Jacobi equations , and coagulation models . Recent articles analyze random tessellations , metastability in zero-range processes , and kintetic descriptions of scalar conservation laws. Older works address equilibrium fluctuations , Smoluchowski equations , and random walks in random environments . Advising: He has advised multiple PhD students, including Haotian Gu (2023), Kyeongsik Nam (2020), and Chanwoo Oh (2019), among others since the 1990s.

Deepak GuptaView profile

Assistant Professor

• Statistical mechanics
• Stochastic thermodynamics
• Nonequilibrium phenomena
• +3 more

Deepak Gupta serves as an Assistant Professor in the Department of Physics at the Indian Institute of Technology Indore (IIT Indore), India. His research centers on theoretical frameworks for non-equilibrium systems and stochastic thermodynamics in small-scale physical processes. Academic Trajectory: Ph.D. from Raman Research Institute (Bangalore) under Prof. Sanjib Sabhapandit: Focused on probability density functions and large deviation theory for stochastic observables (heat, work, entropy production) using Fokker-Planck/master equations and numerical simulations. Postdoctoral Researcher at University of Padova (2019-2021): Investigated emergent ecological properties (species coexistence, catastrophic events) via statistical physics tools and entropy production frameworks for unidirectional transitions. Postdoctoral Researcher at Simon Fraser University (2021-present): Designs control protocols minimizing dissipation in F1-ATPase molecular motors during ATP synthesis. Research Focus: His work bridges stochastic energetics , fluctuation theorems , and molecular biophysics , emphasizing thermal fluctuations in nanoscale systems. Current projects target energy optimization in biological motors through stochastic control theory, with methodology spanning analytical solutions to computational modeling of entropy production. Based in office D-133(LRC) at IIT Indore, he maintains active international collaborations with research groups in Italy and Canada while leading theoretical physics initiatives within the Department of Physics.