Wendelin Werner is a renowned mathematician and currently the Rouse Ball Professor of Mathematics at the University of Cambridge (since 2023). Previously, he held professorships at ETH Zürich (2013–2023) and the University of Paris-Sud in Orsay (1997–2013). His research focuses on probability theory, mathematical physics, and complex analysis, particularly Schramm-Loewner evolution and conformal invariance. He has received prestigious awards, including the Fields Medal (2006), SIAM George Pólya Prize (2006), and Fermat Prize (2001). His work bridges stochastic processes and geometric structures, with implications for statistical mechanics and critical phenomena. Werner studied at the École Normale Supérieure (1987–1991) and earned his PhD from Université Paris VI under J. F. Le Gall (1993). Early career positions included postdoctoral research at Cambridge (1993–1995) and roles at CNRS (1991–1993, 1995–1997). His academic contributions span stochastic geometry, critical systems, and random curves, with seminal papers on loop measures, percolation, and SLE. He has delivered major lectures globally, including at the International Congress of Mathematicians (IMC 2006) and the College de France's Cours Peccot. Werner’s honors include membership in the French Academy of Sciences (2008), the Berlin-Brandenburg Academy (BBAW), and the German National Academy Leopoldina. He is also an honorary fellow of Gonville and Caius College, Cambridge (2009). His research has advanced understanding of universal scaling limits in two-dimensional models, earning recognition as a leader in probability and mathematical physics.
Aleksandar Jeremic is an Associate Professor in both the Electrical & Computer Engineering department and the McMaster School of Biomedical Engineering at McMaster University. His research focuses on biomedical signal processing, statistical signal processing, and biometrics, with applications in healthcare and biomedical systems. He holds a Dipl. Ing. from the University of Belgrade, and an M.S. and Ph.D. from the University of Illinois at Chicago. His expertise spans physiological signal analysis (e.g., ECG/EEG), medical imaging, and machine learning for biomedical applications. He has authored over 50 technical articles and two book chapters, and has been recognized with a teaching award from the McMaster Electrical and Computer Engineering Society (2018). He supervises graduate students in both theoretical and applied research areas. Dr. Jeremic teaches advanced courses such as Biomedical Signal Modeling and Processing and Advanced Probability and Random Processes , emphasizing practical applications of signal processing in healthcare. His work includes clinical implementations like neonatal seizure monitoring software and microwave imaging for breast cancer detection.
Charles D. Sprenger is a Professor of Economics at the California Institute of Technology (Caltech), where he has served since 2020 and held the position of Executive Officer from 2022 to 2025. He is affiliated with Caltech's Division of the Humanities and Social Sciences (HSS) and holds key roles at the Ronald and Maxine Linde Institute of Economic and Management Sciences and the Center for Theoretical and Experimental Social Sciences (CTESS). His external appointments include Board of Editors for the American Economic Review and Associate Editor roles for the Journal of the European Economic Association and Quantitative Economics . His educational background includes a B.A. from Stanford University (2002), an M.Sc. from University College London (2005), and a Ph.D. from the University of California, San Diego (2011). These credentials established his foundation in economic theory and experimental methodology. Sprenger is a leading behavioral and experimental economist specializing in intertemporal decision making and choices under uncertainty. His research designs innovative experiments across diverse contexts—from food deserts in the United States to polio vaccination drives in Pakistan—to test the validity of standard economic models. His work consistently reveals significant deviations from rational choice theory, particularly regarding time inconsistency, risk preferences, and reference-dependent behaviors. He has pioneered methods for measuring time preferences and testing cumulative prospect theory, with implications for public policy and behavioral interventions. Analysis of his 15 most recent publications (2015-2024) shows a cohesive research program centered on behavioral anomalies in decision making. His work bridges laboratory precision with real-world field applications, demonstrating how psychological factors like procrastination and loss aversion manifest in high-stakes environments. Key trends include the development of tailored incentive structures, validation of rank-dependent utility models, and exploration of dynamic inconsistency across domains including health, finance, and public policy. His notable recognition includes: Sloan Foundation Fellowship (2016-2018) Sprenger actively contributes to the academic community through editorial leadership and collaborative research. His work has been featured in Caltech news for projects like "Reducing Procrastination with Tailored Incentives" (2023) and the graduate summer program "Experimental Economics in Theory and Practice" (2023). Though specific advisees aren't listed, his teaching of advanced courses like Experimental Economics (SS 212 abc) indicates mentorship of graduate researchers. He secures significant research funding through fellowships and institutional support, enabling large-scale field experiments. As a core member of CTESS, Sprenger leads a multidisciplinary team conducting cutting-edge experimental economics research. His lab integrates theoretical modeling with empirical validation, focusing on how behavioral insights can improve policy design in areas like tax compliance, vaccination programs, and financial decision making. Current work emphasizes scalable interventions for procrastination and preference-based incentive customization.
Dr. Primoz Skraba is a Professor in Applied and Computational Topology at the School of Mathematical Sciences, Queen Mary University of London. As Deputy Head of the Centre for Probability, Statistics and Data Science, he bridges theoretical topology with practical applications in data analysis, machine learning, and optimization. Education : PhD in Electrical Engineering from Stanford University (2009) Prior Roles : Positions at INRIA, France; Jozef Stefan Institute, Slovenia; University of Primorska; University of Nova Gorica His research focuses on applying topological methods to analyze complex data. Key areas include: Stability of persistence diagrams for quantitative control in finite sampling Variants of persistence (zig-zag, robustness, multiparameter) Algorithmic Complexity in computational topology Stochastic Topology for random geometric models (Poisson, Boolean) Recent publications emphasize persistent homology in random geometric complexes, universality theorems, and integrating topological methods into machine learning. He received grants from the Leverhulme Trust, EPSRC, and Alan Turing Institute for projects on topological universality and AI foundations. His advisee Gabryel Mason-Williams explores wireless sensor network applications of homology.
Daniel M. Kane is a Professor at the University of California, San Diego (UCSD), holding a joint appointment in the Department of Mathematics and the Department of Computer Science and Engineering (CSE). His research spans mathematics and theoretical computer science, with a focus on number theory, combinatorics, complexity theory, and computational statistics. He earned a Ph.D. in Mathematics from Harvard University (2011) and dual BS degrees in Mathematics with Computer Science and Physics from MIT (2007). Prior to UCSD, he was a postdoctoral researcher at Stanford University (2011–2014) on an NSF fellowship. His research interests include robust statistics, machine learning, polynomial threshold functions, and algorithmic methods for high-dimensional data. Notable achievements include co-authoring the book Algorithmic High-Dimensional Robust Statistics (Cambridge University Press, 2023) and receiving the Best Paper Award at the Conference on Computational Complexity (2013), as well as gold medals at the International Mathematical Olympiad (2002 and 2003). Current teaching includes courses such as Math 96 (Putnam Seminar), Math 154 (Graph Theory), CSE 101 (Algorithms), and CSE 203A (Randomized Algorithms). He has consulted for companies like CASPER Labs and AIble, and his work extends to cryptographic protocols, including quantum money schemes based on quaternion algebras. Key contributions include breakthroughs in robust mean estimation, list-decodable learning, and the development of efficient algorithms for statistical problems. His research often bridges foundational theory with practical applications in machine learning and data analysis.
Professor Eugene O'Brien serves as Professor of Civil Engineering within the School of Civil Engineering at University College Dublin's College of Engineering and Architecture. His research focuses on critical structural assessment methodologies for long-span bridges, with particular expertise in traffic load modeling and bridge safety evaluation. His research interests center on structural engineering challenges related to bridge infrastructure, specifically traffic load assessment for long-span bridges , structural health monitoring systems , and sustainable infrastructure management . Professor O'Brien pioneered camera-based monitoring techniques to overcome limitations of traditional Weigh-in-Motion sensors during congested traffic conditions, enabling more accurate safety assessments of aging bridge infrastructure. His work addresses the critical gap in quantifying traffic loading on bridges with spans up to 2 kilometers, where conventional methods fail during stop-and-go traffic scenarios. Analysis of his 15 most recent publications reveals a consistent research trajectory focused on probabilistic modeling of traffic loads, with increasing sophistication in handling extreme events and long-term infrastructure performance. His work spans fundamental statistical methods for load effect prediction, practical applications in real-world bridge assessments, and environmental considerations regarding infrastructure carbon footprints. The research demonstrates strong methodological evolution from basic traffic modeling to comprehensive lifetime assessment frameworks incorporating sustainability metrics. As co-founder and director of Roughan O'Donovan's subsidiary Innovative Solutions (ROD-IS), Professor O'Brien has translated research into practice through significant projects including the Malahide Railway Viaduct assessment (2009), EU-funded bridge lifespan simulation tools (2011), vibration reduction systems for bridge cables, and structural assessments for major international projects including the Ting Kau Bridge in Hong Kong, the Forth Road Bridge in Scotland, and the Chacao Channel Bridge in Chile. His consultancy work demonstrates direct application of academic research to critical infrastructure challenges worldwide. Professor O'Brien's research group operates at the intersection of structural engineering and sustainable infrastructure management, with particular emphasis on extending bridge service life through accurate safety assessment. Their work on the Chacao Channel Bridge demonstrates practical implementation of traffic monitoring systems using toll data to manage truck loads, while their environmental impact analysis shows how accurate safety assessments reduce unnecessary bridge replacements, thereby lowering the carbon footprint of transportation infrastructure through extended service life of carbon-intensive materials like concrete and steel.
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.
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.
Paata Ivanisvili is an Associate Professor at the University of California, Irvine (UCI), Department of Mathematics, School of Physical Sciences. His research focuses on Analysis, Probability, Harmonic Analysis, and Functional Analysis, with a particular emphasis on isoperimetric inequalities, functional inequalities, and discrete structures such as the Hamming cube. He has held visiting positions at institutions including the Hausdorff Research Institute for Mathematics and Princeton University. Ivanisvili has organized conferences such as the Dual Trimester Program at the Hausdorff Institute on Boolean Analysis in Computer Science (2024) and annual Summer/Fall Schools since 2021. He earned his PhD in Mathematics from Michigan State University (2015) and a BS from Saint Petersburg State University (2011). His research interests include sharp inequalities in analysis (e.g., Poincaré, Beckner, Ehrhard), hypercontractivity, and applications to discrete mathematics and probability. He has collaborated with prominent mathematicians such as Fedor Nazarov, Alexander Volberg, and Roman Vershynin. Notable awards include the NSF CAREER Award (2021–2025) and Simons Fellowship in Mathematics (2025–2026). Ivanisvili’s recent work explores the interface between harmonic analysis and discrete mathematics, including studies on additive energies, convex hulls of space curves, and learning theory. His articles frequently address foundational questions in geometric functional analysis, often using tools like Bellman functions and optimal control theory. He actively advises PhD students and has mentored visiting researchers at UCI.
Ismael Castillo is a Professor of Statistics at Sorbonne Université , affiliated with the Laboratoire de Probabilités, Statistique et Modélisation (LPSM) and its Statistics, Data, Algorithms team. He serves as Associate Editor for Annals of Statistics , Bernoulli , and co-Editor for Bayesian Analysis . Research Interests : Mathematical statistics with emphasis on Bayesian nonparametrics , inference in high-dimensional structures , uncertainty quantification , and applications in signal processing and life sciences . Recent Work spans deep neural networks with heavy-tailed weights , posterior and variational inference , fractional posteriors in semiparametric models , and deep Gaussian processes . His publications demonstrate expertise in multiple testing procedures , Spike and Slab priors , and nonparametric Bayesian methods . Awards : IMS Fellow , Honorary Fellow of Institut Universitaire de France , and Best Paper Prize (2021) for research on Pólya tree posterior distributions. Students : Supervised PhD candidates Paul Egels , Thibault Randrianarisoa , and co-supervised Bo Ning (FSMP postdoc) and Kweku Abraham (Hadamard postdoc). Grants : ANR BACKUP (2023-2027, coordinator) and ANR GAP (2021-2025, member).
Rina Foygel Barber is the Louis Block Professor in the Department of Statistics at the University of Chicago, where she also serves as Co-chair of the Committee on Community, Diversity, and Inclusion (CCDI) and is a member of the Committee on Computational and Applied Mathematics (CCAM). Her educational background includes: PhD in Statistics, University of Chicago (2012), advised by Mathias Drton and Nati Srebro MS in Mathematics, University of Chicago (2009) ScB in Mathematics, Brown University (2005) NSF postdoctoral fellow, Stanford University Department of Statistics (2012-13), supervised by Emmanuel Candès Professor Barber's research focuses on the theoretical foundations of statistical problems in estimation, prediction, and inference, particularly in high-dimensional settings where classical methods may not be reliable. She specializes in distribution-free inference methods such as conformal prediction, multiple testing methods, algorithmic stability, and shape-constrained inference. Her work also extends to modeling and optimization problems in medical imaging reconstruction. Her recent publications demonstrate a strong focus on distribution-free inference, with particular emphasis on conformal prediction, false discovery rate control, and algorithmic stability. Her work bridges theoretical statistics with practical applications, especially in the medical imaging domain. Professor Barber has received numerous prestigious awards: Elected to National Academy of Sciences (2025) MacArthur Fellowship (2023) IMS Fellow (2023) COPSS Presidents' Award (2020) Peter Gavin Hall Early Career Prize (2020) She actively mentors students and collaborators, with many co-authored publications across statistics, machine learning, and medical imaging. Her research has been supported by significant grants that enable her work on theoretical foundations of statistical inference and practical applications in medical imaging. Professor Barber also co-organizes the International Seminar on Selective Inference. Her research group focuses on developing and analyzing estimation, inference, and optimization tools for structured high-dimensional data problems. They work on false discovery rate control, distribution-free inference, and applications in medical imaging reconstruction.
Anna Levina is an Assistant Professor for Computational Neuroscience at the University of Tübingen , affiliated with the Department of Computer Science under the Faculty of Science. Her research focuses on the self-organization of neuronal activity, critical dynamics in neural networks, and the excitation/inhibition balance in cortical circuits. Current positions: Assistant Professor (since 2018), Group Leader (2017-2018), Equality Officer (Computer Science) Previous roles: IST Fellow (2015-2017), Associated Researcher (2011-2015), Postdoc/PI (2011-2015), Postdoc (2008-2011) Her research integrates mathematical modeling , statistical physics , and computational neuroscience to study criticality phenomena, neural avalanches, and adaptive network dynamics. Key interests include: Self-organized criticality in neural systems Excitation/Inhibition balance mechanisms Network topology and dynamics Timescale analysis in neural processing Stochastic modeling of neural activity Recent publications reveal trends in understanding critical dynamics across biological and artificial networks, with applications to memory systems, sensorimotor integration, and disease modeling. She has received recognition as an IST Fellow .
Pelin Bolat is an Associate Professor in the Department of Fundamental Sciences at Istanbul Technical University (ITU), College of Maritime Studies. She is actively engaged in maritime cybersecurity, risk assessment, and maritime safety research. Her work is supported by multiple BAP and EU-funded projects, with ongoing research extending into 2025. Her research interests include cybersecurity in maritime navigation systems, dynamic positioning, port state control, and GHG emissions in the maritime sector. She applies advanced methodologies such as fuzzy FUCOM, CORAS framework, and association rule mining to analyze cyber and operational risks. Her recent publications (2023–2025) reflect a strong trend in maritime cybersecurity, focusing on ECDIS, RADAR, ransomware, and cyber hygiene. These works span high-impact journals in maritime engineering and technology, demonstrating interdisciplinary engagement with computer science, safety engineering, and policy analysis. She serves as a principal investigator on several key projects, including cyber risk assessment of bridge navigation equipment and system dynamic modeling of maritime GHG emission measures. She also mentors 15 theses in progress, indicating her active role in student supervision. Her collaborative network includes researchers like Gökhan Kayişoğlu and international partners. While no formal awards are listed, her leadership in EU and BAP projects underscores her academic prominence.
Sean Howe is an Assistant Professor in the Department of Mathematics at the University of Utah, where he has been employed since July 2019. His research is supported by NSF grants DMS-2201112 and DMS-2501816. In the academic year 2023-2024, he was a Friends of the Institute for Advanced Study Member at the special year on p-adic arithmetic geometry at the Institute for Advanced Study. Dr. Howe received his PhD from the University of Chicago in 2017 under the supervision of Matt Emerton. Prior to his position at Utah, he was an NSF Postdoctoral Scholar at Stanford University from September 2017 to June 2019. He earned a joint master's degree from Leiden University and Universite Paris-Sud 11 through the ALGANT program in 2012 and completed his undergraduate studies at the University of Arizona. Dr. Howe's research spans arithmetic and algebraic geometry, representation theory, and number theory, with a particular focus on p-adic aspects. His work often explores the connections between geometry and number theory through the lens of p-adic methods, including p-adic Hodge theory, perfectoid spaces, and the Langlands program. He has made significant contributions to understanding cohomological structures in mixed characteristic settings, the geometry of moduli spaces, and the statistical properties of L-functions. His extensive publication record demonstrates a strong trajectory in advancing p-adic geometry and its applications. Recent work shows increasing focus on cohomological smoothness in mixed characteristic, p-adic periods, and the interplay between random matrix theory and arithmetic statistics. His research often bridges abstract theoretical frameworks with concrete computational approaches. NSF Postdoctoral Scholar NSF grants DMS-2201112 and DMS-2501816 Dr. Howe is an active mentor, currently advising five PhD students: Minhua Cheng, Madison Delmoe, Shea Engle, Abhay Goel, and Suo Jun Tan. He has successfully graduated two PhD students: Matthew Bertucci (2025) and Hanlin Cai (2024). He also regularly mentors undergraduate researchers, with notable projects including Emil Geisler's work on stable multiplicities in configuration space cohomology and Daniel Koizumi's software for computing braid monodromy of cubic surfaces. His teaching portfolio includes advanced courses in algebraic topology, number theory, and algebra, reflecting his broad expertise across pure mathematics. He has taught courses such as Math 6950 (Topics in Algebraic Topology), Math 4400 (Introduction to Number Theory), and Math 6320 (Modern Algebra II).
Satish Rao is a Professor in the Computer Science Division at the University of California, Berkeley. He is affiliated with the Simons Institute for the Theory of Computing and the Center for the Theoretical Foundations of Learning, Inference, Information, Intelligence, Mathematics and Microeconomics at Berkeley (CLIMB). His research focuses on algorithms, combinatorial optimization, graph theory, and theoretical computer science with applications to computational biology and machine learning. Rao has held teaching roles for courses such as CS 70 (Discrete Mathematics and Probability Theory) and CS 270 (Spring 2024). He has been recognized with prestigious awards including ACM Fellow (2013), the Delbert Ray Fulkerson Prize (2012), and the Okawa Research Grant (1999). Research Interests: Algorithm design, graph algorithms, combinatorial optimization, computational biology, and machine learning. Key Contributions: Pioneering work on metric embeddings, approximation algorithms, and network flow problems. Notable publications include foundational papers on tree metrics, distributed object location, and electrical flow-based optimization. Rao’s work bridges theoretical computer science with practical applications, including contributions to phylogeny estimation, anomaly detection, and parallel computing frameworks like the BSP model.
