Yakov Pesin is a Distinguished Professor of Mathematics at Pennsylvania State University, affiliated with the Department of Mathematics and serving as Director of the Anatole Katok Center for Dynamical Systems and Geometry. He holds positions in the Eberly College of Science. His research focuses on Dynamical Systems, Ergodic Theory, Riemannian Geometry, and Mathematical Physics. Born in Moscow, he earned an MS from Moscow State University (1970) and a PhD from Gorky State University (1979). He has been a member of prestigious academies, including the American Academy of Arts and Sciences and the European Academy. His work includes seminal contributions to nonuniform hyperbolicity theory and entropy analysis. His research interests emphasize the interplay between dynamics, geometry, and statistical physics, with notable studies on SRB measures, Lyapunov exponents, and geometric flows. Awards include the Michael Brin Prize in Dynamical Systems and the inaugural Fellow of the American Mathematical Society. Pesin has advised multiple students and collaborators, though specific names are not listed. He directs the Katok Center, fostering interdisciplinary research. His publications (15 most recent listed) explore topics like thermodynamic formalism, hyperbolicity, and entropy, reflecting his leadership in dynamical systems theory.
Anthony Man-Cho So is a Professor in the Department of Systems Engineering and Engineering Management at The Chinese University of Hong Kong (CUHK). He currently serves as Dean of the Graduate School and Deputy Master of Morningside College . With a BSE from Princeton University and a PhD in Computer Science from Stanford University, his career at CUHK began in 2007. Academic Leadership: Dean, Graduate School (2023–present); Deputy Master, Morningside College (2019–present) Education: BSE (Princeton), MSc/PhD (Stanford) His research focuses on optimization theory and its interdisciplinary applications in computational geometry, machine learning, signal processing, and statistics. Key projects include non-convex optimization for wireless networks, robust graph learning, and decentralized learning algorithms. His publications span high-impact journals like Mathematical Programming , SIAM Journal on Optimization , and conferences such as NeurIPS and ICML . Recent work emphasizes dynamic regret analysis , low-rank matrix recovery , and stochastic beamforming . He has authored over 50 refereed papers and a monograph on semidefinite programming. Awards include IEEE Fellow (2023), CUHK Research Excellence Award (2016–17), and multiple IEEE/INFORMS best paper and teaching accolades. He has served on editorial boards of journals like Mathematical Programming and SIAM Journal on Optimization , and as Lead Guest Editor for IEEE Signal Processing Magazine . Teaching roles include courses on optimization, discrete mathematics, and machine learning. Scientific Awards IEEE Fellow (2023) CUHK Outstanding Fellow (2019) Multiple IEEE/INFORMS Best Paper Awards (2010–2022) IEEE/UGC Teaching Awards (2008–2022) His methodology integrates theoretical rigor with practical applications, particularly in wireless communication systems, sensor networks, and financial engineering. Collaborations span institutions in Hong Kong, mainland China, and the U.S., reflecting a global academic influence.
Zsolt Patakfalvi is an Associate Professor at École Polytechnique Fédérale de Lausanne (EPFL), holding positions in the School of Basic Sciences (SB) within the Department of Mathematics (MATH). He is affiliated with the Chair of Algebraic Geometry (CAG) and the Section of Mathematics for Engineers (SMA-ENS). Additionally, he serves as Director of SMA-GE and holds roles in academic governance bodies like the Conference of Section Directors (CDS) and SB Faculty Management. His research focuses on Algebraic Geometry, particularly in birational geometry, positive characteristic methods, moduli theory, and mixed characteristic algebra. He explores topics such as Hodge theory, singularities, and applications to arithmetic geometry. Notable contributions include work on the minimal model program, test ideals, and counterexamples to classical conjectures in positive characteristics. He supervises doctoral students in areas like algebraic geometry and commutative algebra, including Jefferson Baudin, Léo Navarro Chafloque, and Linus Rösler. His past advisees include Emelie Arvidsson and Quentin Posva. Patakfalvi’s publications frequently address foundational questions in geometry, with recent work extending into perfectoid spaces and globally-regular varieties. He coordinates courses such as 'Algebra III - Rings and Fields' and 'Perfectoid spaces' at EPFL, reflecting his commitment to both research and education. His academic service includes managing educational programs within SB-SMA and contributing to institutional decision-making through CDS membership.
Amie Wilkinson is a Professor of Mathematics at the University of Chicago since 2012, previously holding positions at Northwestern University. She specializes in dynamical systems, ergodic theory, and geometry. Her research explores actions of discrete groups, smooth dynamics, and geometric systems. She holds a Ph.D. from UC Berkeley (1995) and an A.B. from Harvard (1989). Wilkinson has received prestigious awards including the Levi L. Conant Prize (2020) and the Ruth Lyttle Satter Prize (2011). She led the NSF-funded 'Robust and Generic Mechanisms in Smooth Dynamics' ($600,000, 2014-2019). Her work spans geodesic flows, Lyapunov exponents, and rigidity phenomena in dynamical systems. Education: Ph.D. UC Berkeley (1995), A.B. Harvard (1989) Key Positions: Boas Assistant Professor (Northwestern, 1996–1999), Associate/Full Professor roles at Northwestern (1999–2011) Awards: AMS Fellow (2013), Invited Speaker at International Congress of Mathematicians (2010) Her research emphasizes the interplay between geometry and dynamics, with contributions on ergodicity, hyperbolic systems, and foliation structures.
Valentino Tosatti is a Professor of Mathematics at the Courant Institute of Mathematical Sciences, New York University. His research focuses on complex and differential geometry, geometric analysis, and partial differential equations (PDEs), with connections to algebraic geometry and dynamical systems. He explores topics such as Kähler geometry, Calabi-Yau manifolds, symplectic geometry, geometric flows, and the Monge-Ampère equations. His work often addresses the interplay between geometric structures and their analytic properties. Education: He earned his PhD in Mathematics from Harvard University in 2009 under the supervision of Shing-Tung Yau. Prior to that, he completed a Laurea (BSc) at the University of Pisa and a Minor Thesis at Harvard. Research Interests: Tosatti's work emphasizes the study of geometric flows (e.g., Kähler-Ricci flow), collapsing behavior of Calabi-Yau metrics, and canonical currents on K3 surfaces. His contributions include foundational results on the regularity of solutions to Monge-Ampère equations and the asymptotic analysis of geometric structures under degenerations. Publications and Trends: His recent articles address themes like volume regularity, collapsing metrics, and geometric flows, reflecting a deep engagement with the analytic and geometric challenges in complex geometry. He has also organized conferences and workshops on topics such as geometric analysis and complex geometry. Professional Activities: Tosatti serves on editorial boards for journals including the Canadian Journal of Mathematics and Mathematische Zeitschrift. He has contributed to organizing events like the 2026 Oberwolfach workshop on Complex Geometry and Dynamical Systems and has been involved in academic seminars at institutions like Columbia University and Northwestern University.
David E Speyer is a Professor in the Department of Mathematics at the University of Michigan . His research focuses on algebraic problems with combinatorial flavors , particularly in tropical geometry , cluster algebras , and geometry of Lie groups . He has supervised multiple PhD students, including Shelby Cox, Will Dana, and John Wiltshire-Gordon, and collaborated on projects with undergraduates like Grant Barkley and Benjamin Branman. Education: PhD in Mathematics from UC Berkeley under Bernd Sturmfels; undergraduate at Harvard. Research: Key areas include tropical geometry , cluster algebras , and flag manifolds . His work often bridges combinatorics, algebraic geometry, and representation theory. Publications: Over 40 papers, including breakthroughs in cluster algebras , affine weak order , and braid variety cluster structures . Awards: Clay Research Fellow (2005-2010). Teaching: Coordinates courses like Math 593 (graduate algebra) and Math 214 , with a focus on inquiry-based learning .
Yvain Bruned is a Professor of Mathematics at Université de Lorraine, Nancy, France, where he leads research in singular stochastic partial differential equations and related fields. He serves as Principal Investigator for the ERC Starting Grant LoRDeT (2023-2028), which focuses on advancing the theory of decorated trees and Hopf algebraic structures for solving singular SPDEs and dispersive PDEs at low regularity. Previously, he was a Lecturer at the University of Edinburgh (2019-2022) and completed postdoctoral work at Imperial College London and University of Warwick under Martin Hairer. His educational background includes: PhD in Mathematics (2012-2015), UPMC (Paris 6), on "Singular KPZ type equations" under Lorenzo Zambotti Master 2 in Probability and Statistics, ENS Cachan / Rennes 1, with honors Master 1 in Mathematics, ENS Cachan, with honors Bachelor in Mathematics and Computer Science, University of Rennes 1, with honors Student at ENS Cachan Brittany extension (2009-2013) Classes Préparatoires in Mathematics and Physics (2007-2009) Bruned's research centers on singular stochastic partial differential equations, with particular focus on Regularity Structures, renormalization theory, and their connections to Hopf algebras. His work bridges theoretical mathematics with applications in quantum field theory, wave turbulence, and numerical analysis. He has developed novel approaches using decorated trees to handle renormalization procedures for singular SPDEs and has extended these methods to dispersive PDEs with random initial data. His research program aims to establish existence and uniqueness results for quasilinear and dispersive SPDEs while developing algebraic tools through deformations of Hopf algebras. His extensive publication record demonstrates consistent contributions to the field of singular SPDEs, with a clear trajectory from foundational work on Regularity Structures to more recent applications in dispersive PDEs and numerical methods. The publications reveal a strong collaborative network with leading researchers in stochastic analysis, mathematical physics, and algebra. His work shows increasing sophistication in handling renormalization procedures through algebraic structures, with recent papers exploring connections between different mathematical frameworks. His major scientific recognition includes: ERC Starting Grant LoRDeT (2023-2028) Bruned actively supervises a large group of researchers, currently advising 4 PhD students and 2 postdoctoral researchers at Université de Lorraine, with several former PhD students having completed their degrees at the University of Edinburgh. His ERC grant has enabled him to organize multiple international workshops in Nancy, fostering collaboration between researchers in singular SPDEs, algebraic structures, and numerical analysis. The grant also supports the development of software platforms for decorated trees and their Hopf algebraic structures. As Principal Investigator of the ERC LoRDeT project, Bruned leads a vibrant research team based at the Elie Cartan Institute of Lorraine, which includes postdocs, PhD students, and visiting researchers. The team regularly organizes specialized workshops on topics including operads, symmetries for quantum field theory, and normal forms for singular dynamics, creating a dynamic research environment that bridges multiple mathematical disciplines.
Yang Li is a Royal Society University Research Fellow at the University of Cambridge, affiliated with the Department of Pure Mathematics and Mathematical Statistics within the Faculty of Mathematics. His research lies at the intersection of differential geometry, complex geometry, and mathematical physics, with a focus on foundational structures in string theory and mirror symmetry. Dr. Li's primary research interests center on Calabi-Yau metrics , special Lagrangian submanifolds , special holonomy , and gauge theory . His work explores the geometric behavior of Calabi-Yau manifolds under degeneration, addressing fundamental questions about metric collapse, diameter bounds, and the structure of singular limits. He has made significant contributions to the SYZ conjecture through constructions of explicit metrics and analysis of fibrations, while his gauge theory research examines singular connections and their topological implications. His publication record demonstrates a strong trajectory in geometric analysis, with recent work establishing uniform diameter bounds for collapsing Calabi-Yau metrics, developing non-Archimedean approaches to the SYZ conjecture, and proving uniqueness results for tangent cones of special Lagrangians. The research spans both theoretical developments in metric geometry and concrete constructions of geometric structures. Notable recognition includes the prestigious Royal Society University Research Fellowship , which supports his independent research program at Cambridge. Actively recruiting PhD students for October 2025 entry Research supported by Royal Society funding Dr. Li maintains an active research program within Cambridge's world-leading Department of Pure Mathematics and Mathematical Statistics, collaborating with leading geometers including Valentino Tosatti and Tristan Collins. His work bridges abstract geometric theory with applications to theoretical physics, particularly in understanding the geometric foundations of mirror symmetry.
Paola Cristofori is an Associate Professor in the Department of Physical, Computer and Mathematical Sciences at the University of Modena and Reggio Emilia. Her research focuses on Algebraic Topology, Differential Geometry, and Manifold Theory, with contributions to PL topology, 4-manifolds, and combinatorial structures like crystallization theory. She teaches courses in Geometry, Linear Algebra, and Algebraic Topology for undergraduate and graduate programs in Mathematics, Civil Engineering, and Strategic Sciences. Her work emphasizes topological invariants, combinatorial methods in manifold classification, and applications of colored graphs. Key areas include trisections of 4-manifolds, Kirby diagrams, and G-degree theory. She collaborates extensively on projects involving geometric topology and computational topology tools. Dr. Cristofori’s teaching spans foundational topics in linear algebra, Euclidean geometry, and advanced algebraic topology, emphasizing rigorous proofs and practical applications. Her research is published in top journals and includes over 40 articles, reflecting her deep engagement with low-dimensional topology and geometric structures.
Associate Professor LIN Zhenhua serves as a Presidential Young Professor in the Department of Statistics and Data Science at the National University of Singapore (NUS), with additional affiliation at the Institute of Data Science since 2021. His research develops cutting-edge statistical methodologies for complex data structures across multiple domains. Dr. LIN completed his Ph.D. at the University of Toronto in 2017 under Fang Yao's supervision, following M.Sc. degrees from Simon Fraser University (2013, 2010) and a B.Sc. from Fudan University (2008). Ph.D., University of Toronto, 2017 (Advisor: Fang Yao) M.Sc., Simon Fraser University, 2013, 2010 B.Sc., Fudan University, 2008 His research program spans functional data analysis (developing techniques for curves and surfaces), non-Euclidean data analysis (statistical methods on manifolds), high-dimensional statistics (p > n problems), and constrained statistical modeling. LIN's work bridges theoretical statistics with practical applications through rigorous mathematical frameworks and computational implementations. Recent publications reveal strong emphasis on bootstrap methods for high-dimensional inference, Riemannian geometry approaches for manifold-valued data, and innovative functional data techniques. His research shows consistent output with multiple 2025 publications in top journals including Biometrika, Bernoulli, and Journal of the American Statistical Association. Professional Recognition Presidential Young Professor, NUS (2019-present) Associate Editor, Bernoulli (2022-2024) Associate Editor, Statistics (2023-present) Young Researchers Committee, Bernoulli Society (2020-2024) Professor LIN actively mentors graduate students as evidenced by numerous collaborative publications with trainees. He teaches advanced courses including ST5215 Advanced Statistical Theory, DSA4211 High-dimensional Statistical Analysis, and ST5223 Statistical Models across multiple academic years. His research group develops specialized software packages including hdanova, matrix-manifold, synfd, mcfda, and iRFDA, making advanced statistical methods accessible to practitioners.
Tomaso A. Poggio is the Eugene McDermott Professor in the Department of Brain and Cognitive Sciences at the Massachusetts Institute of Technology , an investigator at the McGovern Institute for Brain Research , a member of the Computer Science and Artificial Intelligence Laboratory (CSAIL) , and the director of both the MIT Center for Biological and Computational Learning (CBCL) and the multi-institutional Center for Brains, Minds and Machines (CBMM) . Research Interests Poggio’s research is fundamentally interdisciplinary, sitting at the intersection of computational neuroscience , machine learning , and computer vision . His work is driven by the conviction that learning is the core gateway to both biological intelligence and artificial systems. Current themes include: Mathematical foundations of statistical learning theory Engineering applications in computer vision, graphics, bioinformatics, and intelligent search Computational neuroscience of visual object recognition and the ventral stream of the visual cortex Scientific Awards & Honors Eugene McDermott Professorship, MIT Advising & Grants Over three decades, Poggio has advised a large cohort of doctoral and master’s students whose theses span machine learning, computer vision, neuroscience, and bioinformatics. Representative graduates include H. Jhuang, Stanley Bileschi, Jacob Bouvrie, Jennifer Louie, M. Kouh, Sayan Mukherjee, Ryan Rifkin, Alexander Rakhlin, Thomas Serre, Gene Yeo, and many others. His research has been continuously supported by major federal and private funding initiatives, most recently through the multi-institutional Center for Brains, Minds and Machines (CBMM) headquartered at the McGovern Institute since 2013. Laboratories & Teams Poggio directs the Poggio Lab (CBCL) at MIT, an interdisciplinary group comprising neuroscientists, computer scientists, mathematicians, and engineers. The lab collaborates closely with experimental neuroscientists to develop predictive computational theories of visual cortex function and to translate those insights into practical algorithms for computer vision and machine learning.
Biyun Xie is an Assistant Professor in the Department of Electrical and Computer Engineering at the University of Kentucky's Stanley and Karen Pigman College of Engineering. Her research focuses on kinematically redundant robots, fault-tolerant robotics, and human-robot interaction, with applications in dangerous environments and collaborative systems. Education : Ph.D. in Electrical Engineering from Colorado State University (2019), Ph.D. in Mechanical Engineering from Beijing University of Technology (2015), and B.S. in Mechanical Engineering and Automation from Beijing University of Technology (2009). Research Interests : Kinematically Redundant Robots Fault Tolerant Robots Collaborative Robots Human-Robot Interaction Publications (2025–2023) highlight advancements in real-time fault-tolerant motion planning for redundant robots, neural network-based motor health monitoring, human-like motion algorithms, and collision-free trajectory optimization. These works intersect robotics, artificial intelligence, and mechanical/electrical engineering. Contact : Biyun.Xie@uky.edu | 859-562-2557
Sebastian Pokutta is a Professor at Technische Universität Berlin, Vice President at the Zuse Institute Berlin (ZIB), and Chair of the Cluster of Excellence MATH+ and MODAL. His research lies at the intersection of Artificial Intelligence, Optimization, and Machine Learning, with applications in sustainability, quantum computing, and mathematical discovery. Research Interests: Development of novel optimization algorithms, particularly Frank-Wolfe and Conditional Gradient methods. Integration of machine learning with decision-making and combinatorial optimization. AI for Science (AI4Science), including applications in quantum mechanics and ecology. AI and creativity, human-AI co-creativity, and social science modeling using multi-agent LLMs. His recent publications (2025) demonstrate a strong focus on scalable optimization, interpretability, and algorithmic foundations. The work spans theoretical advances in convergence analysis, practical implementations in Julia (FrankWolfe.jl), and real-world deployments in biomass estimation and quantum certification. Scientific Awards: Gödel Prize (2023) STOC Test of Time Award (2022) Science Prize of the Association for Pediatric Orthopedics (2025) Google Research Awards (2021, 2020) NSF CAREER Award (2015) He advises a vibrant research group, with former students and postdocs securing faculty positions at institutions like Inria, Carlos III University, and James Madison University. His group has received funding from Google, DFG, and Math+, and he leads major collaborative efforts such as the Thematic Einstein Semester on Mathematical Optimization for Machine Learning. Labs and Teams: Interactive Optimization and Learning Lab at TU Berlin and ZIB. Leadership in MODAL and MATH+ research clusters, fostering interdisciplinary collaboration in mathematical optimization and AI.
Professor Tony Shardlow is affiliated with the Department of Mathematical Sciences at the University of Bath , UK. His research spans Stochastic Differential Equations , Bayesian Inverse Problems , Statistical Shape Modelling , and Numerical Analysis , with applications in data science, medical imaging, and computational physics. Labs/Teams : IMI (Institute for Mathematical Innovation), Prob-L@b (Probability Laboratory at Bath), SAMBa (EPSRC Centre for Doctoral Training in Statistical Applied Mathematics). Recent Research Trends : Focus on geometric shape analysis using flow fields, stochastic PDEs for particle dynamics, and Bayesian inference techniques in industrial and medical contexts. Collaborative work bridges computational mathematics with applications in hip dysplasia assessment and pesticide delivery systems. Advising : Supervised Fengpei Wang's PhD thesis on dimension reduction and Sinkhorn algorithms. Collaborates with researchers like N. D. F. Campbell and C. Poon. Teaching : Offers MA30170 - Numerical Solution of Elliptic PDEs.
Mehrtash Tafazzoli Harandi is an Associate Professor in the Department of Electrical and Computer Systems Engineering at Monash University, part of the Faculty of Engineering. His research focuses on machine learning and computer vision, particularly visual data analysis, with contributions to geometric deep learning, continual learning, and medical imaging. He holds editorial roles at IET Computer Vision , Frontiers in Imaging , and Journal of Imaging . Education & Previous Affiliations: Prior to Monash, he worked at NICTA (Canberra & Queensland Research Labs) and CSIRO-Data61. His Erdős number is 4 via a collaboration path through Richard Hartley. Research Interests: His work spans geometric learning, diffusion models, medical image analysis, and sustainable AI applications. Key areas include unlearning mechanisms in AI, 3D reconstruction compression, and robust MRI reconstruction using contrastive learning. Grants & Projects: He leads projects funded by ARC, US Air Force, and industry collaborations, including 'Can Machines Unlearn?' (ARC, A$790k) and 'Exploiting Geometries of Learning' (ARC, A$420k). His work addresses challenges in lifelong learning, model adaptation, and trustworthy AI from limited data. Awards: Recipient of Best Recognition Paper (IEEE DICTA 2013), NICTA Impact Award (2015), and multiple outstanding reviewer recognitions at top conferences. Teaching: Teaches courses on neural networks, computer vision, and advanced data analysis at Monash University. Supervises PhD students with a focus on mathematical and computational proficiency. Labs/Teams: Collaborates with the Australian Center for Robotic Vision (ACRV) and contributes to interdisciplinary projects at CSIRO-Data61. His research group explores cutting-edge AI applications in healthcare, manufacturing, and environmental sustainability.
