Joël Merker is a Professor in the Department of Mathematics at Université Paris-Saclay, affiliated with the Faculty of Sciences. His research spans complex geometry, algebraic hyperbolicity, and CR structures. Key Research Areas: Complex analysis, differential algebra, geometric invariants, and philosophical foundations of mathematics. Notable Collaborations: Contributions to the Green-Griffiths and Kobayashi hyperbolicity conjectures. Teaching: Developed extensive materials in linear algebra, integration theory, and Fourier analysis. Students: Advised Samuel Pocchiola, focusing on Cartan equivalences for CR manifolds. Conferences: Active in international workshops on complex geometry and dynamical systems (2006-2014). His work integrates geometric proofs with computational methods, emphasizing synthetic reasoning and algorithmic constructions.
Alfonso Di Bartolo is a Researcher at the Department of Mathematics and Computer Science , University of Palermo. His work focuses on algebraic structures, particularly Lie and Leibniz algebras, algebraic groups, and number theory. He holds regular office hours on Thursdays from 3:00 PM to 5:00 PM in Studio 107, Via Archirafi 34, Palermo. Role: Researcher Department: Mathematics and Computer Science Contact: alfonso.dibartolo@unipa.it Office Hours: Thursdays 3:00 PM–5:00 PM His research spans topics like solvable/nilpotent algebras, biderivations, affine mappings, and p-adic integers. Publications highlight classifications of Lie algebras, transformations groups, and applications to number theory. No scientific awards or student advisement details are provided in the text.
Gregorio Baldi is a Research Fellow (Chargé de Recherche) at the French National Center for Scientific Research (CNRS), affiliated with the Institut de Mathématiques de Jussieu - Paris Rive gauche. His research centers on Arithmetic Geometry , with expertise in Shimura varieties, variational Hodge theory, and the Zilber-Pink conjecture. He also investigates connections between homogeneous dynamics, Teichmüller dynamics, and geometric Diophantine problems. Baldi has authored significant publications in premier mathematical journals, including: Annals of Mathematics (2023, 2025) Inventiones mathematicae (2024) Ergodic Theory and Dynamical Systems (2025) His recent work (2023–2025) focuses on Hodge loci distribution, non-arithmetic ball quotients, and o-minimality in Hodge theory, demonstrating consistent contributions to foundational problems in geometry and number theory.
Chenzi Jin serves as a Veblen Research Instructor at Princeton University and the Institute for Advanced Study, a prestigious postdoctoral fellowship supporting independent research at the intersection of complex differential geometry and algebraic geometry. His appointment reflects exceptional promise in mathematical research, with full institutional backing from two leading centers for theoretical mathematics. He completed his Ph.D. at the University of Maryland, College Park under Prof. Yanir Rubinstein, establishing foundational expertise in geometric analysis. This training directly informs his current investigations into stability conditions and asymptotic methods. Dr. Jin's research program focuses on the interface of complex differential and algebraic geometry, emphasizing differential, convex, and discrete geometric techniques. He examines Kähler metrics, stability thresholds, and geometric structures through asymptotic analysis and PDE methods, addressing core problems like Tian's stabilization conjecture and Okounkov body asymptotics. His work reveals profound connections between combinatorial structures and continuous geometric phenomena. His 2024-2025 publications demonstrate cohesive progress across geometric analysis, with recurring emphasis on stability conditions for algebraic varieties and asymptotic behavior in metric geometry. These works collectively advance understanding of how discrete combinatorial frameworks govern continuous geometric structures. Funded entirely by the Veblen Instructorship, Dr. Jin operates with dedicated research support from Princeton and IAS. While not independently advising students, he collaborates with senior faculty including Prof. Rubinstein and Prof. Tian, offering engagement pathways for graduate researchers through joint projects and seminar participation.
Sung Gi Park is a Veblen Research Instructor jointly affiliated with Princeton University and the Institute for Advanced Study. Previously, he completed his graduate studies at Harvard University under the supervision of Mihnea Popa. His academic appointments bridge two prestigious institutions with a focus on advanced mathematical research. His research specializes in: Birational geometry and applications of Hodge theory Analysis of singular varieties using Hodge modules Hyperbolicity phenomena and geometric structures Classification and properties of singularities in algebraic varieties Recent work emphasizes the intersection of Hodge theory with birational geometry, developing novel approaches to longstanding problems. Publication analysis reveals two dominant themes: Advanced algebraic geometry (5 preprints on singularities/Hodge theory) Contributions to commutative algebra and graph theory (2 peer-reviewed papers) This demonstrates both specialized depth in geometric methods and interdisciplinary versatility. Teaching experience includes: Upcoming instruction in Riemann Surfaces (MAT 531) and Multivariable Calculus (MAT 201) at Princeton Previous teaching roles in Multivariable Calculus (Math 21A) and Algebraic Topology (Math 231A) at Harvard His instructional responsibilities span foundational to advanced graduate-level mathematics courses.
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.
Maxim Evgenievich Beketov is a Research Fellow at the Faculty of Computer Science of the National Research University Higher School of Economics (HSE), where he has been working since 2020. He is affiliated with the Institute of Artificial Intelligence and Digital Sciences and the International Laboratory of Stochastic Algorithms and Multidimensional Data Analysis, contributing to cutting-edge research in computational methods and artificial intelligence. His educational background includes: Master's degree (2017) in Applied Mathematics and Physics from Moscow Institute of Physics and Technology Bachelor's degree (2015) in Applied Mathematics and Physics from Moscow Institute of Physics and Technology Beketov's research spans multiple interdisciplinary fields with a strong mathematical foundation. His primary interests include topological data analysis, machine learning, mathematical and Bayesian statistics, differential geometry, and computational neuroscience. He applies these methods to problems in dimensionality reduction, variety assessment, and graph neural networks. His work bridges theoretical mathematics with practical applications in artificial intelligence and neuroscience, particularly in understanding cognitive processes through topological approaches. An analysis of his recent publications reveals a strong focus on topological methods in machine learning, with increasing emphasis on applications to neuroscience and cognitive mapping. His work demonstrates a progression from theoretical mathematical foundations toward practical implementations in spiking neural networks, traffic control systems, and music information retrieval. The interdisciplinary nature of his research connects computer science, mathematics, and neuroscience through topological approaches. His scientific achievements include: High Professional Potential Group (HSE Personnel Reserve), Category: New Researchers (2025) Beketov has been actively involved in academic teaching, offering courses including Introduction to Discrete Differential Geometry and Mathematical Analysis. His research is supported through the HSE University Basic Research Program, as acknowledged in his publications. He collaborates with researchers across multiple institutions, as evidenced by his co-authorship on papers with numerous collaborators. He is a core member of the International Laboratory of Stochastic Algorithms and Multidimensional Data Analysis, where he contributes to projects involving topological data analysis, machine learning, and computational neuroscience. His work in the laboratory focuses on developing advanced mathematical methods for analyzing complex data structures, with applications ranging from cognitive neuroscience to transportation systems.
Charles Louis Fefferman is the Herbert E. Jones, Jr. '43 University Professor of Mathematics at Princeton University, where he has held a faculty position since 1977. Previously, he served as a full professor at the University of Chicago from 1971 to 1977, becoming the youngest full professor in U.S. history at age 22. His academic journey began at the University of Maryland, College Park, where he earned his undergraduate degree at 17 before completing his PhD at Princeton under Elias Stein at age 20. Fefferman's research spans mathematical analysis with particular emphasis on harmonic analysis, partial differential equations, and complex analysis. His groundbreaking work on singular integrals, Hardy spaces, and the Bergman kernel revolutionized these fields, leading to his Fields Medal in 1978. More recently, he has made significant contributions to Whitney extension problems, fluid dynamics singularity formation, and mathematical aspects of topological materials. His publication record shows remarkable consistency over five decades, with recent work (2019-2023) focusing on manifold learning, smooth function interpolation, and quantum systems. These publications demonstrate both theoretical depth and increasing connections to data science applications, maintaining his position at the forefront of mathematical research. Fefferman's scientific honors form an exceptional constellation of recognition: Fields Medal (1978) Alan T. Waterman Award (1976, inaugural recipient) Salem Prize (1971) Bergman Prize (1992) Bôcher Memorial Prize (2008) Wolf Prize in Mathematics (2017) BBVA Foundation Frontiers of Knowledge Award (2021) As an advisor, Fefferman has mentored numerous doctoral students who have become leaders in their fields, including Matei Machedon, Luis Seco, and Michael Christ. His research group continues to explore fundamental questions in analysis while developing mathematical frameworks for emerging applications in data science and quantum physics. Fefferman remains actively engaged in research, with publications through 2023 demonstrating his continued intellectual vitality and mathematical creativity.
