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Hyejin KuView profile

Professor

• Mathematical Finance
• Machine Learning
• Risk Management
• +4 more

Hyejin Ku is a Full Professor in the Department of Mathematics and Statistics at York University's Faculty of Science. Her research focuses on the intersection of Mathematical Finance and Machine Learning, addressing challenges in risk measurement, portfolio optimization, and quantitative finance. She develops advanced mathematical models to enhance decision-making through reinforcement learning and data analytics. Notable projects include novel algorithms for credit rating prediction using neural networks and sequence-based clustering for credit risk assessment. Her work integrates applied mathematics with real-world financial applications, such as systemic risk reduction in multi-layer networks and option pricing under liquidity constraints. She holds a prominent position in mathematical finance, contributing to both theoretical advancements and practical solutions for financial markets. Her research trends emphasize interdisciplinary approaches, combining machine learning techniques with financial modeling to solve complex problems in risk management and asset valuation. Her publications span over two decades, showcasing contributions to portfolio optimization, derivatives pricing, and computational finance. Dr. Ku is affiliated with York University’s Department of Mathematics and Statistics, where she contributes to academic leadership and research mentorship. Her office is located in DB 2025, and she can be reached at hku@yorku.ca.

• Random Matrix Theory
• Probability Theory
• Mathematical Physics
• +3 more

Dr Nicholas Simm is a Principal Research Fellow in the Department of Mathematics at the University of Sussex, affiliated with the School of Mathematical and Physical Sciences. He has been funded by the Royal Society since October 2018 as a University Research Fellow. His research focuses on random matrix theory, probability, and mathematical physics, with applications to areas such as quantum physics and statistical mechanics. Key research interests include orthogonal polynomials, eigenvalue statistics, multiplicative chaos, and Painlevé transcendents. His work bridges pure mathematics and applied problems, leveraging tools from probability theory and integrable systems. Notable publications include studies on asymptotics of orthogonal polynomials, fluctuations in eigenvalues of random matrices, and connections to the Riemann zeta function. His recent work explores high-frequency limits in holomorphic multiplicative chaos and large deviations in elliptic random matrices. Dr Simm has secured grants from the Royal Society and Leverhulme Trust for projects on random matrices, log-correlated fields, and mesoscopic statistics. He collaborates widely, with co-authors including leading researchers in probability and mathematical physics.

• Statistical Physics
• Network Theory
• Complex Systems
• +9 more

Diego Garlaschelli is a Professor of Theoretical Physics at Leiden University, affiliated with the Leiden Institute of Physics (LION) and the Biological, Soft and Complex Systems department within the Faculty of Science. His research focuses on the structure, dynamics, and physics of complex networks in financial, economic, social, neural, and biological systems. Combining statistical physics, information theory, and data science, his group explores interdisciplinary topics such as systemic risk in financial networks, mesoscopic organization in neural systems, and mathematical modeling of networks using maximum-entropy ensembles. Garlaschelli’s work emphasizes collaboration across fields like mathematics, computer science, economics, and neuroscience. Recent grants include NWO Open Competition funding for projects on network theory and systemic risk. He advises several PhD candidates, including Alessio Catanzaro, Francesca Giuffrida, and Jingjing Wang. His publications span high-impact journals like Nature Physics , Nature Reviews Physics , and Science , addressing topics from ensemble equivalence in networks to cultural diversity models. Key research themes include: (1) statistical physics of constrained systems, (2) financial network reconstruction from limited data, (3) early-warning signals for economic instabilities, and (4) information-theoretic bounds for large data structures. Garlaschelli co-leads the Leiden Complex Network Network (LCN2), fostering Dutch network science collaboration.

Guangqu ZhengView profile

Assistant Professor

• Probability Theory
• Stochastic Analysis
• Stochastic Partial Differential Equations (SPDEs)
• +6 more

Guangqu Zheng is an Assistant Professor in the Department of Mathematics and Statistics at Boston University. He holds a BA from Wuhan University, a Master's from Université Paris-Saclay, and a Ph.D. from the University of Luxembourg. Prior to Boston University, he was a Lecturer at the University of Liverpool and conducted postdoctoral research at the University of Melbourne and the University of Kansas. Education: Ph.D. in Mathematics, University of Luxembourg (2018) Master in Probability, Université Paris-Orsay (2014) BA in Economics and Management, Wuhan University (2011) Research Interests: His work focuses on probability theory, stochastic analysis, and SPDEs. Key areas include Malliavin calculus, Stein’s method, limit theorems, and applications to stochastic partial differential equations. Recent projects involve hyperbolic Anderson models, Lévy noise-driven systems, and spatial ergodicity. Publications: His recent articles explore central limit theorems for SPDEs, hyperbolic models, and applications of Stein’s method. These contributions highlight advancements in stochastic processes and their interdisciplinary applications. Teaching & Advising: At BU, he teaches advanced courses in stochastic analysis and advises students in probability and statistics. Expectations for students include proficiency in real analysis and probability theory.

• Stochastic integrable systems
• Asymptotic analysis
• Random matrix theory
• +5 more

Evgeni Dimitrov is an Assistant Professor of Mathematics in the Department of Mathematics at the University of Southern California, housed within the USC Dana and David Dornsife College of Letters, Arts and Sciences. Before joining USC, he served as a Ritt Assistant Professor in the Mathematics Department at Columbia University. Education: PhD in Mathematics, Massachusetts Institute of Technology (MIT), advised by Alexei Borodin Undergraduate degree, Princeton University Research Interests: Dimitrov’s research sits at the intersection of probability, representation theory, and combinatorics, with a central focus on the asymptotic analysis of stochastic integrable systems . He develops hybrid techniques that blend algebraic methods from representation theory with analytic and combinatorial tools to study universal scaling limits—particularly those falling within the Kardar–Parisi–Zhang (KPZ) universality class . Key objects of study include Gibbsian line ensembles , random matrix models , log-gamma polymers , and exactly-solved stochastic particle systems such as ASEP and the six-vertex model. Publication Profile: Across 2021–2025, Dimitrov has produced a concentrated body of work addressing edge fluctuations , multi-level loop equations , and global large-deviation principles for discrete β-ensembles and related integrable systems. His papers repeatedly explore the convergence of discrete stochastic models to Airy-like universal processes, tightness questions for line ensembles, and the rigorous derivation of KPZ scaling laws, underscoring a cohesive research trajectory toward understanding universal random geometry. Scientific Awards: No awards are explicitly mentioned in the supplied material. Advising & Grants: No specific PhD students, grants, or funding details are provided in the text. Labs & Teams: No laboratory or research-group information is available from the supplied content.

Gérard Ben ArousView profile

Professor

• Probability Theory
• Stochastic Analysis
• Large Deviations
• +6 more

Gérard Ben Arous is a Silver Professor of Mathematics at New York University's Courant Institute of Mathematical Sciences, where he has served as Director and Vice Provost for Science and Engineering Development since 2011. He holds a PhD in Mathematics from the University of Paris VII (1981) and has previously taught at the University of Paris-Sud, École Normale Supérieure, and the Swiss Federal Institute of Technology in Lausanne. His research focuses on probability theory, stochastic analysis, and their applications to physics and industrial problems, particularly exploring complex systems' long-time behavior and aging phenomena in disordered media. Education: PhD in Mathematics, University Paris 7, France (1981) M.Sc. in Statistics, University Paris-Sud Orsay, France (1979) B.S. in Mathematics, École Normale Supérieure (Paris), France (1978) His research interests bridge probability with partial differential equations, dynamical systems, and statistical mechanics. Key contributions include studies on random media, random matrices, and the interplay between complexity, disorder, and aging in physical systems. He has held leadership roles in academic institutions, including directing the mathematics departments at Orsay and École Normale Supérieure, and founded Lausanne's Bernoulli Center. Notable awards include Fellow of the Institute of Mathematical Statistics and the Montyon Prize from the French Academy of Sciences. His work is published in top journals like Annals of Probability and Communications in Pure and Applied Mathematics , and he co-edits Probability Theory and Related Fields . Ben Arous has advised numerous researchers and contributed to interdisciplinary projects, including studies on machine learning landscapes and financial mathematics. His lab focuses on stochastic modeling and its applications across disciplines.

Matthew JenssenView profile

Associate Professor

• Discrete Probability
• Probabilistic/Extremal Combinatorics
• Statistical Physics
• +5 more

Matthew Jenssen is a Reader in Probability at King's College London and a UKRI Future Leaders Fellow. He holds a BA and MMath from the University of Cambridge (2012–2013) and a PhD from the London School of Economics (supervised by Jozef Skokan and Julia Boettcher). His research focuses on the intersection of combinatorics, statistical physics, and theoretical computer science, particularly on large-scale structure formation in systems with local interactions. Notable contributions include advancements in sphere packing, Ramsey numbers, and random matrix theory. Jenssen has held postdoctoral positions at the University of Oxford and the University of Birmingham before joining King’s in 2023. His research group at King’s explores discrete probability, extremal combinatorics, and algorithms, with applications to statistical physics and high-dimensional geometry. Key achievements include a groundbreaking improvement on sphere packing lower bounds and resolving extremal questions in graph theory. Jenssen’s work often bridges combinatorial theory with computational methods, yielding impactful results in probabilistic combinatorics. Scientific awards include the UKRI Future Leaders Fellowship (2020). His grants include a 2023–2026 project on statistical physics methods in combinatorics and geometry. Jenssen collaborates widely, with notable co-authors including Will Perkins, Jozef Skokan, and Felix Joos. He is actively involved in the Probability Group at King’s and contributes to international conferences and arXiv publications.

Riddhipratim BasuView profile

Associate Professor

• Probability Theory
• First Passage Percolation
• Last Passage Percolation
• +5 more

Riddhipratim Basu is an Associate Professor at the International Centre for Theoretical Sciences (ICTS-TIFR) in Bengaluru, India, since September 2017. Previously, he was a Szegö Assistant Professor of Mathematics at Stanford University (2015–2017) and a Ph.D. graduate in Statistics from UC Berkeley (2015), supervised by Allan Sly. Research focuses on Probability Theory, with emphasis on First/Last Passage Percolation, Interacting Particle Systems, Large Deviations, and Random Matrix Theory. Key collaborators include Allan Sly, Shirshendu Ganguly, Mahan Mj, and Manan Bhatia. Publications span journals like Communications on Pure and Applied Mathematics , Annals of Probability , and Comm. Math. Phys. His work explores geodesic structures in percolation models, scaling exponents in KPZ universality, and geometric properties of stochastic processes. Recent studies include Liouville Quantum Gravity and Airy process fluctuations.

Arijit ChakrabartyView profile

Professor

• Random Matrix Theory
• Heavy-Tailed Distributions
• Large Deviations
• +5 more

Arijit Chakrabarty is a Professor at the Theoretical Statistics and Mathematics Unit of the Indian Statistical Institute, Kolkata, India. His research focuses on random matrix theory, heavy-tailed distributions, large deviations, and long-range dependence. He can be reached via email at arijit.isi@gmail.com. Research Interests: Random matrix theory, Heavy-tailed distributions, Large deviations, Long-range dependence, Spectral analysis, Stochastic processes Publications Trends: His 15 most recent articles span random matrix theory, large deviations, Gaussian processes, and free probability. Key topics include eigenvalue analysis in random graphs, excursion lengths in Gaussian processes, and clustering of extremes in memory regimes. Lecture Notes: He has produced educational materials on Measure Theoretic Probability, Martingale Theory, and Probability Theory, partially in collaboration with Arup Bose and Rajat Hazra. These notes are accessible online and reflect his teaching contributions.

• Infinite dimensional geometry
• Mathematical physics
• Decision science

Jean-Pierre Magnot serves as an Associate Researcher at LAREMA (Laboratoire Angevin de Recherche en Mathématiques), University of Angers, France, while concurrently holding a High-School Teacher position at Lycée Jeanne d'Arc, Clermont-Ferrand, France. He maintains additional affiliation with the Lepage Research Institute in Slovakia. His research integrates three core domains: Infinite dimensional geometry (focusing on diffeological structures, vector pseudobundles, and Grassmannians), Mathematical physics (specializing in Kadomtsev-Petviashvili hierarchies and pseudo-differential operators), and Decision science (developing geometric frameworks for pairwise comparison matrices and inconsistency reduction). This interdisciplinary approach frequently bridges gauge theory concepts with decision-making algorithms, exemplified by Yang-Mills formulations applied to ranking problems. Recent publications (2024-2025) reveal a cohesive trajectory where diffeological methods unify mathematical physics and decision theory. Key trends include the geometrization of pairwise comparisons through quantum gravity analogies, well-posedness analysis of generalized KP systems, and optimization frameworks for infinite-dimensional spaces. His work demonstrates consistent application of abstract geometric constructs to both theoretical physics and practical decision systems. No scientific awards were documented in the provided sources. Information regarding students advised, doctoral supervision, or research grant acquisitions was not present in the source material. Magnot actively collaborates with international institutions including the Union of Czech Mathematicians and Physicists, University of Prešov (Slovakia), Eötvös Loránd University (Hungary), Italian Society for General Relativity and Gravitation, Transilvania University of Brasov (Romania), VŠB-TU Ostrava (Czech Republic), and Lodz University of Technology (Poland). These partnerships span mathematical physics, differential geometry, and decision science applications.

Tobias NeckelView profile

Associate Professor

• Uncertainty Quantification
• Random Differential Equations
• Numerical Algorithms
• +4 more

Tobias Neckel is an Associate Professor at the Institute for Informatics at the Technical University of Munich (TUM), where he leads research projects and coordinates academic programs. He has been the project team leader of the IGGSE Project ExaNIML since 2018, main coordinator of the Ferienakademie since 2014, and Program Coordinator of the Bavarian Graduate School of Computational Engineering (BGCE) since 2009. Diploma in Technomathematik from TU München (2005) Dr. rer. nat. in Informatics from TU München (2009) Neckel's research focuses on Uncertainty Quantification, Random Differential Equations, and High Performance Computing. His work develops efficient numerical algorithms using hierarchic and adaptive methods such as octrees/spacetrees and sparse grids, with applications in fluid-structure interactions and incompressible fluid flow simulation. His research bridges theoretical mathematics with practical computational science, emphasizing robust and efficient implementations. His recent publications demonstrate a strong trajectory in multi-fidelity modeling, uncertainty quantification, and high-performance computing. Neckel has made significant contributions to scalable hierarchical approximation methods, dynamic resource management in HPC, and the application of machine learning techniques to computational science problems. His work spans diverse application domains including plasma physics, hydrology, and computational engineering. Lehrfonds prize of the TUM (2014) Ernst Otto Fischer prize of the TUM (2011) Promotionspreis des Bunds der Freunde der TU München (2009) Neckel has supervised numerous graduate students and has been actively involved in curriculum development and teaching innovation. His book "Bits and Bugs: A Scientific and Historical Review of Software Failures in Computational Science" (2019) represents a significant contribution to understanding software reliability in scientific computing. He has organized minisymposia at major conferences including SIAM CSE and SIAM UQ, and serves on program committees for various computational science conferences. As coordinator of the Ferienakademie and the BGCE, Neckel plays a central role in advanced computational engineering education in Bavaria. His research group develops software for exascale computing and contributes to the Transregional Collaborative Research Centre 89 on Invasive Computing. Neckel also maintains international collaborations, with research stays at institutions including the Australian National University and Tokyo Institute of Technology.

László KozmaView profile

Assistant Professor

• Data Structures
• Algorithms
• Algorithmic Adaptivity
• +7 more

László Kozma is an Assistant Professor at the Theoretical Computer Science group of the Institute of Computer Science (Freie Universität Berlin). He obtained his PhD from Saarland University under Raimund Seidel, followed by postdoctoral positions at Tel Aviv University and TU Eindhoven. His research focuses on self-adjusting data structures , adaptive algorithms , and combinatorial optimization with applications to problems like the Traveling Salesman Problem, binary search trees, and geometric data structures. Academic Affiliation: Freie Universität Berlin (since 2018) Education: PhD in Computer Science (Saarland University, 2016); postdoc at Tel Aviv University and TU Eindhoven. His work explores the intersection of data structures, combinatorial algorithms, and geometric methods. He has made significant contributions to problems involving pattern-avoidance in inputs, saddlepoint detection , and self-adjusting heaps . Key areas include: Adaptive algorithms for pattern-avoiding inputs Optimal tree and heap structures Geometric and stochastic approaches to optimization Complexity analysis of classical algorithms Recent publications highlight efficient solutions for exponential cut problems (ESA 2025), balanced TSP partitioning (EuroCG 2025), and randomized saddlepoint algorithms (ESA 2024). His research often bridges theory and practice, exemplified by the smooth heap implementation and fun projects like Recursi and Cuckoo Hashing visualization.

• Free Probability Theory
• Random Matrix Theory
• Operator Algebras
• +3 more

Ping Zhong is an Associate Professor in the Department of Mathematics at the University of Houston. He previously served as an Assistant Professor at the University of Wyoming (2018-2024). He earned his Ph.D. in Mathematics from Indiana University Bloomington under the supervision of Hari Bercovici. His research focuses on free probability theory, random matrix theory, operator algebras, and their applications to high-dimensional statistics and machine learning. He actively mentors graduate and undergraduate students interested in these areas. Dr. Zhong's work bridges pure mathematics and applied domains, with contributions to spectral analysis, quantum information, and stochastic processes. His recent research explores outlier eigenvalues, Brown measure analysis, and superconvergence phenomena in free probability. He organizes the Analysis Seminar at UH and has been involved in conferences like the RMMC Summer School 2022 on free probability and applications. While no specific awards are mentioned, his extensive publication record indicates sustained academic contribution. He seeks motivated students for collaborative research projects in his areas of expertise.

Życzkowski KarolView profile

Professor

• Quantum Information Theory
• Quantum Chaos
• Random Matrices
• +7 more

Karol Życzkowski is a Professor of Physics at the Smoluchowski Institute of Physics, Jagiellonian University, Cracow, Poland, and also holds a position at the Center for Theoretical Physics, Polish Academy of Sciences, Warsaw. He has held visiting positions at the Perimeter Institute for Theoretical Physics (2004/05) and previously served as Director of the Quantum Information Center in Gdańsk (2019-2023). Since January 2023, he has been President of the Cracow Branch of the Polish Academy of Sciences. Education Ph.D. in Theoretical Physics (1987, Jagiellonian University) Habilitation in Physical Sciences (1994, Jagiellonian University) Życzkowski’s research spans Quantum Information Theory , with a focus on quantum entanglement, geometry of quantum states, random matrices, and operator theory. His work bridges theoretical physics, mathematics, and interdisciplinary applications, including voting systems (e.g., Jagiellonian Compromise) and scientometrics. He has authored or co-authored over 150 papers and the influential monograph Geometry of Quantum States (Cambridge University Press, 2006, second edition 2017). His recent publications emphasize quantum thermodynamics (e.g., Jarzynski equality for stochastic maps), entanglement detection via uncertainty relations, and mathematical structures in quantum theory. Collaborations span 134 scientists from 19 countries, and his work has been cited over 6500 times (Google Scholar) with an H-index of 40. Scientific Awards Award of Rector of Jagiellonian University (2013) for work on quantum information foundations Award of Rector of Jagiellonian University (2007) for the monograph Geometry of Quantum States Prime Minister Award (1995) for habilitation thesis Humboldt Fellowship (1989), Fulbright Fellowship (1997/98) Życzkowski has supervised 5 Ph.D. and 16 Master’s theses, with 3 Ph.D. students currently in progress. He coordinated 9 research grants, participated in 10 others, and led the Polish node of a German SFB-Transregio project. He serves on editorial boards for journals including Open Systems & Information Dynamics and Journal of Physics A . He co-organized workshops (including Oberwolfach and Marseille) and founded research networks like the European Research Network Krzyżowa Initiative for Quantum Information. His Jagiellonian Quantum Information Team (JQIT) and involvement in the Mark Kac Complex Systems Research Centre highlight his collaborative leadership.

Zhiyi ChiView profile

Professor

• Stochastic Processes
• Probability Theory
• Statistical Inference
• +2 more

Zhiyi Chi is a Professor in the Department of Statistics at the University of Connecticut (Storrs Campus). His research focuses on stochastic processes with large jumps, random sampling techniques, stochastic optimization, and large-scale hypothesis testing. He holds an office in AUST 336 and can be reached at zhiyi.chi@uconn.edu. Key contributions include works published in Journal of Theoretical Probability (2018) and Annals of Applied Probability (2016), addressing topics such as multivariate renewal theorems and eigenvalue properties of Markov matrices. His scholarship bridges theoretical foundations and applied methodologies in probability and statistics.