Jacob Fox is a Professor at Stanford University, specializing in Combinatorics and Probability. His research focuses on extremal combinatorics, Ramsey theory, graph theory, and additive combinatorics. He advises students like Maya Sankar. His work explores structural and enumerative aspects of graphs, hypergraphs, and combinatorial configurations. Recent studies include advancements in Ramsey numbers, sumset theory, and probabilistic methods in discrete mathematics. Key research areas include Ramsey numbers for sparse structures, hypergraph properties, and applications of combinatorial geometry. His publications often bridge theoretical insights with algorithmic applications. No scientific awards are listed in the provided text. His advising includes Maya Sankar, with research aligned to combinatorial problems. Collaborative projects involve extremal graph theory and probabilistic combinatorics. No labs or dedicated research groups are explicitly mentioned.
Grégoire Ithier is a Senior Lecturer in Physics at the Department of Physics, Royal Holloway, University of London. His research focuses on quantum engineering, decoherence, thermalization, mesoscopic physics, and random matrix theory. He leads the 'TypDyn' project exploring typical dynamics of embedded quantum systems, and co-leads the Leverhulme Trust-funded 'Generation and detection of quantum signals' initiative. His work bridges theoretical and experimental domains, including superconducting circuits and cryogenic microwave engineering. Ithier's research tools include advanced numerical methods (e.g., exact diagonalization) and statistical techniques (e.g., random matrix theory). Key Projects: TypDyn: Studies typical dynamics in embedded quantum systems (2015–present) QSimFP: Quantum simulators for fundamental physics (2020–2024) A new statistical theory of disordered quantum systems (2020–2024) His experimental work involves superconducting qubits, Josephson devices, and nano-superfluidic cavities. Grants include STFC and Leverhulme Trust funding. Recent publications address quantum thermalization, many-body systems, and random Hamiltonian analysis.
Aise Johan de Jong is a Professor in the Department of Mathematics at Columbia University, where he teaches courses including representations of finite groups and organizes the algebraic geometry seminar. He is a leading figure in algebraic geometry with a particular focus on stacks theory and arithmetic aspects of algebraic varieties. Institution: Columbia University, Department of Mathematics Research Focus: Algebraic stacks, arithmetic geometry, moduli spaces Major Project: The Stacks Project (open-source collaborative textbook) De Jong's research primarily centers on algebraic stacks, arithmetic geometry, and the foundations of algebraic geometry. His work bridges abstract theoretical frameworks with concrete computational aspects, particularly in positive characteristic. He has made significant contributions to understanding Brauer groups, period-index problems, and the geometry of moduli spaces. His research often connects number theory with geometric structures, exploring how arithmetic properties manifest in geometric settings. His publication record shows a consistent focus on fundamental structures in algebraic geometry, with particular emphasis on stacks theory (evident in The Stacks Project), Brauer groups, rational connectivity, and arithmetic properties of algebraic varieties. The trajectory of his work demonstrates increasing sophistication in handling complex geometric structures while maintaining connections to arithmetic questions. His most recent work continues to explore the interplay between algebraic geometry and number theory, particularly through the lens of stacks and moduli spaces. De Jong actively mentors graduate students, with numerous descendants listed in the Mathematics Genealogy Project. His academic lineage includes researchers working across various subfields of algebraic geometry. He has organized multiple conferences including "Moduli spaces and moduli stacks" (2012) and "Spaces of curves and their interaction with diophantine problems" (2009), demonstrating his leadership in the field. He leads The Stacks Project, a major collaborative open-source initiative that has become an essential reference for algebraic geometers worldwide. This project provides comprehensive foundations for algebraic stacks and related concepts, with regular updates and community contributions. De Jong also maintains the Stacks Project Blog where he discusses mathematical topics related to the project and shares updates.
Brett Kolesnik is a Research Fellow in the Department of Statistics at the University of Warwick. His research focuses on probability theory, random structures, bootstrap percolation, and interactions with combinatorics. He has held postdoctoral fellowships at UC Berkeley, San Diego, and the University of Oxford, and was a Senior Demy at Magdalen College. His work includes organizing workshops on bootstrap percolation and collaborating with leading researchers in probability and combinatorics. Education: PhD in Mathematics from the University of British Columbia (advised by Omer Angel). Notable awards include the NSERC Postdoctoral Fellowship and the Florence Nightingale Bicentennial Fellowship in Statistics. Research interests span bootstrap percolation models, random graph dynamics, and stochastic processes. Recent work includes studies on Brownian map geometry, tournament score sequences, and Coxeter group structures. Selected articles explore topics such as critical beta-splitting processes, Catalan percolation, and random walks on algebraic structures. His publications appear in top journals like Electronic Journal of Probability and Annals of Applied Probability . Awards include the Florence Nightingale Fellowship and NSERC Postdoctoral Fellowship. Professional involvement includes organizing the 2024 BIRS workshop on Bootstrap Percolation and contributing to interdisciplinary collaborations in probability and combinatorics.
Sanjit A. Seshia is the Cadence Founders Chair Professor in the Department of Electrical Engineering and Computer Sciences at the University of California, Berkeley . He is affiliated with the Group in Logic and the Methodology of Science and participates in centers like the Industrial Cyber-Physical Systems Center , Berkeley AI Research , and the Simons Institute for the Theory of Computing . Research interests include formal methods for automated verification and synthesis of dependable systems, with applications to cyber-physical systems , AI-based autonomy , and computer security . His work spans SMT solving, model counting, syntax-guided synthesis, and algorithmic improvisation, with tools like UCLID5 , VerifAI , and Scenic for verifying autonomous systems and educational platforms like CPSGrader . Students and collaborators include notable researchers such as Dorsa Sadigh (Stanford), Daniel Fremont (UC Santa Cruz), and Hazem Torfah (Chalmers). He has co-founded startups like Decyphir and 20ⁿ Labs based on his research.
Yiping Lu is an Assistant Professor in the Department of Industrial Engineering and Management Sciences at Northwestern University's McCormick School of Engineering. His research focuses on developing interdisciplinary approaches combining domain knowledge (differential equations, stochastic processes), machine learning, and experiments. Key interests include scientific machine learning (AI4Science), stochastic simulation, and robust machine learning. Education: Ph.D. in Applied and Computational Mathematics, Stanford University (2023) B.S. in Computational Mathematics, Peking University (2019) Research Highlights: Hybrid research integrating ML with scientific domains like PDEs and inverse problems Development of Physics-Informed Learning frameworks Contributions to deep learning theory (ResNets, neural collapse) Advances in kernel operator learning and adversarial robustness Awards: CPAL Rising Star Award (2024) University of Chicago Data Science Rising Star (2022) Stanford Interdisciplinary Graduate Fellowship (2021) Labs/Teams: SCALE Lab (Scientific Computation and Learning at Northwestern) Collaborations with NYU's Courant Institute and Stanford
Karin Melnick is a Full Professor in Mathematics at the University of Luxembourg, Faculty of Science, Technology and Medicine, Department of Mathematics. She heads the research group Group Actions, Geometric Structures, and Smooth Dynamics . Previously, she held positions at the University of Maryland (2009–2023), where she advanced from Assistant Professor to Professor and Associate Chair for Faculty Affairs, and at Yale University (2006–2009). Education: PhD and Master's degrees from the University of Chicago Research Interests: Her work spans differential-geometric rigidity, Lorentzian geometry, conformal pseudo-Riemannian structures, parabolic Cartan geometries, and smooth dynamical systems. She investigates symmetries of geometric structures, classification of manifolds with prescribed curvature properties, and dynamics of group actions on differentiable manifolds. Publications: Melnick's 15 most recent articles (2011–2025) predominantly explore rigidity phenomena in geometric structures, conformal/Lorentzian geometry, and dynamical systems. Key themes include automorphism groups of parabolic geometries, embedding theorems for tractor bundles, non-existence results for quasihomogeneous metrics, and applications of Frobenius-type theorems to Cartan geometries. Academic Leadership: She organizes major conferences including the upcoming BeNeLux Mathematical Congress (2026) and Lorentzian, Affine, and Hyperbolic Geometry: In Memory of Todd Drumm (2025). She frequently delivers invited talks at institutions like IHES Paris, Isaac Newton Institute, and Universität Hamburg. Teaching: Currently instructs Géométrie des courbes et des surfaces (Summer 2025).
Daniel Balasubramanian is an Adjunct Associate Professor of Computer Science and Research Scientist at Vanderbilt University's School of Engineering. His research focuses on cybersecurity, software verification, and cyber-physical systems, with expertise in symbolic execution, code analysis, and formal methods. He contributes to advancing secure systems through frameworks like RAMPART for adversarial defense and Syntheto for formal verification. His work intersects edge computing, hardware security, and autonomous systems resilience. Research Interests: Cybersecurity (including ethical hacking, network defense), formal methods (verification, theorem proving), edge computing (tinyML, cloud integration), and cyber-physical systems (emulation, testbeds). His recent work emphasizes assurance provenance in software documentation and adversarially robust autonomous systems. Publications since 2019 highlight contributions to cybersecurity testbeds, reinforcement learning for penetration resistance, and hardware security against rowhammer attacks. He has explored domain-specific languages (Syntheto), incremental modeling techniques (differential-formula), and cloud-edge service resilience against adversarial perturbations. Labs/Teams: Affiliated with the Institute for Software-Integrated Systems (ISIS), focusing on integrating software with physical systems through model-driven approaches and cybersecurity innovations.
Vladimir Spokoiny is a Professor at the Departments of Mathematics and Economics of the Humboldt University of Berlin and Head of the Research Group "Stochastic Algorithms and Nonparametric Statistics" at the Weierstrass Institute for Applied Analysis and Stochastics (WIAS) in Berlin, Germany. His research spans multiple areas of statistics, machine learning, and financial mathematics, with significant contributions to nonparametric statistics, high-dimensional data analysis, and statistical methods in finance. Spokoiny received his M.Sc. in applied mathematics from the Moscow Institute of Railway Engineering in 1981 and his Ph.D. in mathematics from Lomonosov Moscow State University in 1988. He completed his Habilitation at Humboldt University in 1996. His academic career includes positions at the All-Union Institute of Railway Transport in Moscow, the Institute for Information Transmission Problems in Moscow, and the Institute for Applied Analysis and Statistics in Berlin before joining the Weierstrass Institute and Humboldt University where he has been a professor since 2002. Spokoiny's research focuses on adaptive nonparametric smoothing and hypothesis testing, high dimensional data analysis, statistical methods in finance, image analysis with applications to medicine, classification, and nonlinear time series. His work often addresses the challenges of nonstationarity in time series data and develops innovative methods for volatility estimation and risk management. He has made significant contributions to the development of adaptive weights smoothing procedures, which have applications in image processing, community detection, and manifold learning. His recent work has expanded into high-dimensional statistics, Bayesian inference, and optimization methods for machine learning, with publications demonstrating novel approaches to Gaussian approximation, Laplace methods, and statistical inference in non-Euclidean spaces. Spokoiny has supervised numerous PhD students including Oliver Reiss, Danilo Mercurio, Ying Chen, Elmar Diederichs, and Mstislav Elagin, whose research has focused on mathematical finance, time series analysis, and statistical methods. He serves as an Associate Editor for The Annals of Statistics (since 2004) and Statistics and Decisions (since 2002), and has previously served on the editorial board of the Journal of Statistical Planning and Inference. His professional activities include reviewing for major statistical journals including Annals of Statistics, Bernoulli, Econometrica, and Journal of American Statistical Association, as well as reviewing grant proposals for the National Science Foundation (USA), German Research Foundation, and Netherlands Organisation for Scientific Research. Spokoiny is a member of several professional societies including the International Statistical Institute, American Statistical Association, Institute of Mathematical Statistics, and Bernoulli Society. He is fluent in Russian (mother tongue), English, and German, and has good knowledge of French. His research group at WIAS focuses on developing novel statistical methodologies with applications across various scientific domains, particularly emphasizing adaptivity and robustness in complex data environments. The group's work has significant implications for financial risk management, medical imaging, and machine learning applications, with recent publications addressing fundamental questions in high-dimensional statistics and nonparametric inference.
Andrea Marchese is an Associate Professor in the Department of Mathematics at the University of Trento since 2019. He holds a PhD from the University of Pisa (2013) and has held academic positions at the University of Pavia, University of Zurich, and Max Planck Institute for Mathematics in the Sciences (Leipzig). His research focuses on Geometric Measure Theory, Calculus of Variations, and Optimal Transport, with contributions to minimal surfaces, branched transportation networks, and singular measure analysis. Education: PhD in Mathematics, University of Pisa (2013), Thesis: 'Two applications of the theory of currents' Master's in Mathematics, University of Milan (2008), Thesis: 'Singular points for coverings of Banach spaces' Bachelor's in Mathematics, University of Milan (2006), Thesis: 'Weak topologies and their metrizability in Banach spaces' Research Interests: Geometric Measure Theory, Calculus of Variations, Optimal Transport, Minimal Surfaces, and their applications to real analysis and geometric flows. Awards & Grants: Habilitation for Full Professor in Mathematical Analysis (2022) ERC Starting Grant (2020, ranked A with a score of 484/3272 proposals) Coordinated multiple INdAM/GNAMPA projects totaling over €25,000 Awarded grants from the University of Pavia, SNF, and Marie Sklodowska-Curie Actions Teaching: Has taught courses on Mathematical Analysis, Geometric Measure Theory, and Functional Analysis at the University of Trento, Pavia, Zurich, and Freiburg. Professional Activities: Organized international workshops, served as referee for over 30 journals (e.g., Inventiones Mathematicae , Journal of Differential Geometry ), and contributed to conferences worldwide.
Peter McGrath is an Assistant Professor in the Department of Mathematics at North Carolina State University (NC State), part of the College of Sciences. His research focuses on geometric analysis, minimal surfaces, and partial differential equations. He holds a PhD in Mathematics from Brown University (2017). His expertise spans Ordinary Differential Equations, Partial Differential Equations and Analysis, and Topology, Geometry, and Mathematical Physics research groups. McGrath’s work explores advanced topics such as spectral geometry, free boundary problems, and geometric flows. His recent research emphasizes minimal surface constructions, topological asymptotics, and applications of eigenvalue optimization. Notable contributions include studies on free boundary minimal surfaces in the unit ball and advancements in understanding the Canham problem in biomembrane modeling. McGrath is affiliated with NC State’s Department of Mathematics, located at 2108 SAS Hall, Raleigh, NC. His contact information includes pjmcgrat@ncsu.edu and office SAS Hall 3248.
Jason Li is an Assistant Professor in the Department of Computer Science at Carnegie Mellon University's School of Computer Science. He teaches advanced algorithms courses including 15-754 Spectral Graph Theory (Spring 2025), 15-451 Design and Analysis of Algorithms (Fall 2024), and 15-850 Advanced Algorithms (Spring 2024). His research focuses on fast graph algorithms , particularly solving longstanding open problems through modern algorithmic techniques. Key research themes include preconditioning and locality , which serve as reductions from worst-case to well-behaved and local instances respectively. His work has produced breakthroughs in deterministic global minimum cut algorithms, all-pairs minimum cut (Gomory-Hu trees), and near-optimal parallel shortest path algorithms. Analysis of his recent publications reveals a consistent trend toward almost-linear time algorithms for fundamental graph problems, with significant contributions to dynamic graph algorithms, minimum cut variants, and parallel computation. His work frequently appears in top venues including STOC, FOCS, and SODA, often with multiple best paper recognitions. EATCS Distinguished Dissertation Award (2021) Best Paper Award at SODA 2024 Invited to HALG 2024 Invited to TALG and JACM for SODA 2024 paper Machtey Best Student Paper at FOCS 2019 Professor Li actively advises graduate students including Henry Fleischmann and George Li. His research is supported by collaborations with leading institutions and frequent invitations to present at major conferences. He maintains an open-door policy for CMU students and collaborators, though notes the high volume of research inquiries he receives weekly.
Vadim Lozin is a Professor of Mathematics at the University of Warwick, affiliated with the Department of Mathematics within the School of Mathematics. His research interests span graph theory, combinatorics, and discrete mathematics, focusing on areas such as clique-width, Ramsey numbers, and structural graph theory. He has held visiting positions at institutions including the Université Paris-Dauphine, EPFL, and KAUST. Lozin has received several accolades, including the Best Paper Award for 'Linear Ramsey numbers' in 2018 and a 2024 award at the International Symposium on Algorithms and Computation. His work involves collaborations with global researchers and contributions to conferences like IWOCA and WG. Lozin serves on editorial boards for journals such as Discrete Applied Mathematics and Electronic Notes in Discrete Mathematics . His research explores foundational problems in graph theory, with applications in algorithm design and complexity analysis. Lozin’s publications include studies on union-closed sets, functional graph properties, and algorithmic approaches to graph parameters. He has also contributed to books like Words and Graphs , bridging formal language theory with graph structures. His grants focus on clique-width and stability in graphs, reflecting his commitment to advancing theoretical and applied discrete mathematics.
Urs Lang is a Full Professor at the Department of Mathematics, ETH Zurich, where he has held a professorship since 2001. His academic journey began with mathematics studies at the University of Berne and the University of Freiburg i.Br., culminating in a doctoral degree focused on hyperbolic geometry and minimal surfaces. Education: University of Berne (undergraduate) University of Freiburg i.Br. (doctoral degree) Lang's research lies at the intersection of differential geometry , metric geometry , and geometric group theory . His work explores non-positive curvature spaces, geometric measure theory, and large-scale Lipschitz analysis. Recent publications examine combinatorial hyperbolicity, isoperimetric inequalities, and rank-rigidity phenomena. The publications overview reveals a consistent focus on geometric structures, including: Higher-rank hyperbolicity in singular spaces Convex geodesic bicombings Injective hulls in geometric group theory Nonlinear potential theory on hyperbolic metric spaces Lipschitz extension problems Curvature comparison theorems Lang actively contributes to academic discourse through editorial roles at journals like Geometry and Topology and Analysis and Geometry in Metric Spaces , as well as organizing major conferences including the 2025 Metric Analysis Trimester Program at Bonn's Hausdorff Institute.
Markus Land is a Tenure Track Professor at the Department for Mathematics, Ludwig Maximilian University of Munich, affiliated with the Algebraic Geometry Working Group. His research bridges algebraic topology, homotopy theory, and K-theory, focusing on their interplay with L-theory and manifold topology. He has held postdoctoral positions at the University of Copenhagen (supported by an EU Marie Curie Fellowship and DFG Grant) and the University of Regensburg, and earned his PhD at the University of Bonn under Wolfgang Lück. Education : PhD in Mathematics (University of Bonn, 2016) Positions : Postdoctoral (University of Copenhagen, 2019–2022; University of Regensburg, 2016–2019) Research Interests : Markus Land explores algebraic topology and homotopy theory , particularly algebraic K-theory , hermitian K-theory , and their connections to L-theory and manifold topology . His work often applies infinity-categories to foundational problems in operator K-theory and geometric topology. Recent Publications : His research includes groundbreaking studies on chromatic localization in K-theory, Grothendieck-Witt groups in ring theory, and additivity in cobordism categories . Collaborations with leading mathematicians like Georg Tamme and Ulrich Bunke highlight interdisciplinary approaches to algebraic and geometric problems. Awards & Grants : EU Marie Curie Individual Fellowship (2020–2022) DFG Individual Research Grant (2019–2020) Teaching & Academic Leadership : He has designed advanced courses including Topology I–V , Algebraic K-Theory , and Condensed Mathematics , emphasizing homotopy theory and infinity-categories . Future initiatives include student seminars on arithmetic and algebraic geometry to foster academic collaboration. Working Group : As part of the Algebraic Geometry Working Group at LMU Munich, he contributes to seminars and research projects on arithmetic geometry , manifold classification , and spectral algebra .
