Asaf Ferber is Associate Professor in Mathematics at University of California, Irvine, School of Physical Sciences. His research spans discrete mathematics including combinatorial games, random graphs, extremal hypergraph theory, and quantum computation. Research explores Hamiltonian cycles in random graphs, structural properties of pseudorandom graphs, and quantum algorithms for combinatorial problems. Recent work develops quantum approaches to graph learning and sparse recovery in random matrices. Awards: NSF CAREER Award Sloan Fellowship Distinguished Early Career Faculty Award for Research Air Force Research Grant NSF-BSF Grant Organizes conferences including SoCalDM Symposium and Desert Discrete Math Workshop, mentoring graduate students through UCI's Probability and Combinatorics Seminar.
Sharan Vaswani is an Assistant Professor in the School of Computing Science at Simon Fraser University (SFU). His research focuses on designing algorithms for sequential decision-making under uncertainty, stochastic optimization, and their interplay with machine learning generalization. He holds a PhD from the University of British Columbia (2019) and postdoctoral experiences at the University of Alberta and Mila. His academic journey includes MSc (UBC, 2015) and BTech (BITS Pilani, 2012) degrees. Education: PhD (UBC, 2019), MSc (UBC, 2015), BTech (BITS Pilani, 2012) Postdoctoral Work: University of Alberta (2020-2021), Mila (2019-2020) Teaching includes courses on Probability and Computing (CMPT 210), Optimization for Machine Learning (CMPT 409/981), and Theoretical Foundations of Reinforcement Learning (CMPT 419/983). His research group focuses on developing scalable optimization algorithms with theoretical guarantees. He advises multiple PhD and MSc students, contributing to areas like constrained MDPs, adaptive learning rates, and reinforcement learning theory. Research Highlights: Contributions to bandit algorithms, stochastic gradient methods, and reinforcement learning theory. Notable work includes global convergence analysis of policy gradients and variance-reduced optimization frameworks.
Aapo Hyvärinen is a Professor of Computer Science at the University of Helsinki , affiliated with the Helsinki Institute for Information Technology and the Helsinki Probabilistic Machine Learning Lab . He previously held the position of Professor of Machine Learning at the Gatsby Computational Neuroscience Unit, University College London (2016-2019). Education : Undergraduate Mathematics at University of Helsinki, Vienna, and Paris; Ph.D. in Information Science from Helsinki University of Technology (1997) His research focuses on machine learning and computational neuroscience , particularly: Independent Component Analysis (ICA) Natural Image Statistics Causal Representation Learning Neural Signal Processing Applications to brain imaging (MEG, CryoEM) Recent publications emphasize causal discovery , identifiable machine learning , and nonlinear ICA . Key projects include: VETURI (AI for health) DIGIMIND (AI in mental health) CIFAR grants (2022-2025) Scientific awards : Highly Cited Researcher (2010) He serves as Action Editor for the Journal of Machine Learning Research and Neural Computation , and has held Area Chair roles at NeurIPS, ICML, ICLR, AISTATS, and UAI conferences. His work bridges theoretical machine learning with neuroscience and philosophical implications of artificial intelligence .
François-Xavier Briol is an Associate Professor in the Department of Statistical Science at University College London (UCL). He co-leads the Fundamentals of Statistical Machine Learning research group and holds roles including theme lead for Computational Statistics and Machine Learning (CSML), co-director of the UCL CDT in Data-Intensive Science, and ELLIS scholar. His research focuses on merging scientific models with data through robust statistical and machine learning methods, emphasizing computational efficiency and model misspecification robustness. Education: BSc/MMORSE from the University of Warwick, PhD from the joint Warwick-Oxford CDT in Statistics. Postdoctoral roles at Imperial College London (Mathematics) and University of Cambridge (Engineering), followed by a Group Leader position at The Alan Turing Institute (2020–2023). Research interests include probabilistic numerical methods, Bayesian inference for intractable models, Stein’s method, and kernel-based approaches. His work has been recognized via awards like the AISTATS Best Paper Award and Blackwell-Rosenbluth Award, with funding from EPSRC and Amazon. Advising: Supervised PhD students in areas like Bayesian quadrature, robust inference, and Gaussian processes. Current students focus on topics such as Bayesian filtering and sensitivity analysis. Grants include EPSRC funding and an Amazon Research Award. Labs/Teams: Active in UCL’s ELLIS unit, CSML, and Turing Institute collaborations. Editorships include SIAM/ASA Journal on Uncertainty Quantification and Bayesian Analysis.
Aad van der Vaart is a Professor of Stochastics at Leiden University's Mathematical Institute. He was awarded the prestigious NWO Spinoza Prize in 2015 for groundbreaking work in mathematical statistics, particularly Bayesian methods applied to medical imaging, genetic data, and complex models. His research bridges pure mathematical theory with applied domains like neuroscience and astronomy. Research Interests : Van der Vaart focuses on infinite-dimensional Bayesian statistics, nonparametric models, and statistical genetics. His work emphasizes rigorous mathematical analysis of prior distributions and their impact on data-driven conclusions. Applications include gene network modeling and PET scan image reconstruction. Key Contributions : Authored influential books on estimation theory; pioneered modern Bayesian approaches to high-dimensional data. His Spinoza Prize funds will support interdisciplinary research and hiring new talent in statistical methods. Awards : NWO Spinoza Prize (2015), recognized as a global leader in statistical theory. Future Directions : Expanding into astronomical data analysis and medical applications, leveraging Bayesian frameworks for big datasets.
Professor Ben Goldys is a distinguished academic at The University of Sydney's School of Mathematics and Statistics, where he conducts research at the intersection of pure mathematics and applied sciences. His work spans multiple disciplines including stochastic analysis, partial differential equations, and financial mathematics, with significant contributions to both theoretical frameworks and practical applications in science and finance. Goldys' research interests center on stochastic (ordinary and partial) differential equations and their applications. His specific focus areas include stochastic partial differential equations, stochastic geometric PDEs, stochastic boundary value problems, stochastic fluid dynamics, ergodic theory of infinite-dimensional diffusions, and applications in financial mathematics such as interest rate derivatives, credit risk, and stochastic volatility. His work bridges pure mathematical theory (Functional Analysis, PDEs, Ergodic Theory) with complex real-world problems across multiple domains. His research aligns with the University of Sydney Faculty of Science Research Strengths including Understanding the Universe, Fundamental Laws of Nature, Complex Systems, and Next Generation Materials. Professor Goldys has secured multiple significant research grants from the Australian Research Council, including recent projects such as 'Mathematics for future magnetic devices' (2024), 'Mathematics for breaking limits of speed and density in magnetic memories' (2019), and 'Novel Approaches for Problems with Uncertainties' (2015). His current research projects focus on geometric stochastic partial differential equations and applications in micromagnetism, mean field games in finance, stochastic boundary value problems, and stochastic Navier-Stokes equations on the rotating sphere. He maintains extensive international collaborations with institutions in Germany (University of Tuebingen), Italy (LUISS University), Poland (Institute of Mathematics Polish Academy of Sciences), and the United Kingdom (University of York), working on projects involving optimal control, stochastic systems with memory, and geometric stochastic PDEs. Goldys is an active member of the Applied Mathematics Research Group and The University of Sydney Nano Institute, contributing to interdisciplinary research initiatives that connect mathematical theory with cutting-edge technological applications.
Padhraic Smyth is a Distinguished Professor and Hasso Plattner Endowed Chair in Artificial Intelligence at the University of California, Irvine (UCI), holding joint appointments in the Department of Computer Science and Department of Statistics. He leads the DataLab research group, focusing on machine learning, AI, and their applications in climate science, healthcare, and education. His research spans probabilistic modeling, deep learning, and human-AI collaboration. Education: PhD in Electrical Engineering from the California Institute of Technology (1988), MSEE (1985), and BEng (1984). Prior to UCI, he worked at NASA's Jet Propulsion Laboratory (1988–1996). Research Interests: Machine learning, AI, pattern recognition, Bayesian methods, climate science applications, algorithmic fairness, and human-AI interaction. He has published over 200 papers and co-authored textbooks like Modeling the Internet and the Web . Awards: ACM Fellow, IEEE Fellow, AAAI Fellow, AAAS Fellow, and ACM SIGKDD Innovation Award recipient. He has held leadership roles in UCI's Center for Machine Learning and Data Science. Key Projects: Human-AI collaboration frameworks, robustness in deep learning, climate modeling using spatio-temporal data, and AI fairness with missing attributes. Collaborates with institutions like NASA and industry partners (e.g., Google, eBay). Labs/Teams: Director of UCI’s Data Science Initiative and HPI Research Center in Machine Learning. Supervises a vibrant PhD program with over 30 alumni in academia and industry.
Prof. Martin Haenggi is the Frank M. Freimann Professor of Electrical Engineering and Concurrent Professor in the Department of Applied and Computational Mathematics and Statistics at the University of Notre Dame. He holds a Dr.sc.techn. (Ph.D.) from ETH Zurich and has been at Notre Dame since 2000. His research focuses on stochastic geometry and wireless networks, including cellular, heterogeneous, vehicular, and millimeter-wave systems. He has held sabbaticals at UCSD (2007–2008), EPFL (2014–2015), and ETH Zurich (2021–2022). Education: Dipl.-Ing. (M.Sc.), ETH Zurich, 1995 Dr.sc.techn. (Ph.D.), ETH Zurich, 1999 Research interests emphasize stochastic geometry for analyzing network performance, including coverage, interference, and reliability in wireless systems. Key areas include meta distributions, spatial-temporal analysis, and network optimization. His work has been recognized with IEEE Fellow status, Clarivate Highly Cited Researcher distinction, and NSF CAREER Award (2005). Grants and Awards: NSF Award (Deep Stochastic Geometry: 2020–2023) NSF Award (Toward a Stochastic Geometry for Cellular Systems: 2015–2019) Rice Prize (2017), Best Survey Paper Award (2017), and Best Tutorial Paper Award (2010) from IEEE Communications Society Teaching includes advanced courses on stochastic geometry, wireless networks, and signal processing. His lab focuses on theoretical and applied aspects of network modeling, with collaborations in industry and academia.
Sanghamitra Dutta is an Assistant Professor at the University of Maryland, College Park researching trustworthy machine learning systems for social good, focusing on explainability, efficiency, robustness, and ethics in AI through mathematical frameworks. Education: Ph.D. from Carnegie Mellon University Research Interests: Dr. Dutta's work centers on Trustworthy Machine Learning with core emphases on Explainable AI , Fairness in AI , Robust Machine Learning , and Privacy-Preserving Machine Learning . She develops solutions using Information Theory , Probability Theory , Causal Inference , and Optimization to address reliability challenges, extending to Efficient Machine Learning (model compression) and Distributed Machine Learning (coded computing). Scientific Awards and Honors: NSF Career Award JPMorgan Faculty Award Northrop Grumman Seed Grant Simons Institute Fellowship (Causality Program, 2022) K&L Gates Presidential Fellowship in Ethics and Computational Technologies A G Milnes Outstanding Thesis Award Grants and Research Support: Her group receives funding from an NSF Career Award, JPMorgan Faculty Award, and Northrop Grumman Seed Grant. Research has been adopted by JPMorgan for fair lending model review and featured in New Scientist. Advising and Team: Actively recruiting graduate students through the UMD Graduate Program; prospective students must mention her name in applications while current students should contact directly with transcripts.
Leslie Ann Goldberg is a Senior Research Fellow at St Edmund Hall and Professor of Computer Science at the University of Oxford. She currently serves as Head of the Department of Computer Science (on sabbatical 2025-26) and focuses on foundational problems in Algorithms and Complexity Theory , particularly randomised algorithms for network communication, machine learning, and statistical physics models. Her research includes solving Aldous' 1987 conjecture on backoff protocol instability (with John Lapinskas), developing rigorous mathematical analysis frameworks for algorithmic efficiency, and advancing approximate counting techniques via Markov Chain Monte Carlo methods (with Andreas Galanis and collaborators). Key projects involve graph homomorphisms , Moran process dynamics , and #BIS complexity class analysis. Recent publications (2023-2024) span topics like Sybil defense mechanisms, low-temperature sampling on random graphs, and parameterised subgraph counting modulo 2. Her work demonstrates cross-disciplinary impact in computational biology, statistical physics, and database theory. Scientific Awards include Best Paper Prizes at ICALP 2016, ICALP 2010, and IPEC 2017. She supervises PhD student Paulina Smolarova and collaborates extensively with researchers in Oxford and beyond.
Anders Rantzer is a Professor of Automatic Control at the Department of Control Engineering, Faculty of Engineering, Lund University, Sweden. He has held visiting positions at Caltech (2004–2005) and the University of Minnesota (2015–2016) as the Taylor Family Distinguished Visiting Professor. His academic journey began with a PhD from KTH Stockholm in 1991, followed by a postdoc at the Institute for Mathematics and its Applications (IMA), University of Minnesota. His research interests center on modeling, analysis, and synthesis of control systems , with a strong focus on scalability, adaptation, and applications in energy networks . He is particularly known for foundational work in positive systems and integral quadratic constraints (IQCs) . These theoretical frameworks are critical in analyzing stability and robustness of large-scale interconnected systems. His work bridges mathematical rigor with practical engineering applications, especially in sustainable energy and networked systems. The recent publications and lecture materials reflect a consistent trajectory in scalable and robust control, optimization, and distributed systems. Themes such as large-scale convex optimization , nonlinear and stochastic control , and network dynamics dominate his scholarly output, indicating a sustained commitment to advancing control theory for complex, real-world systems. Scientific honors include: Fellow of IEEE Member of the Royal Swedish Academy of Engineering Sciences (IVA) Chairman of the Swedish Scientific Council for Natural and Engineering Sciences Chairman of the Royal Physiographic Society of Lund Rantzer has supervised numerous students and contributed extensively to academic leadership and education. He has been involved in major national and international research initiatives such as WASP (Wallenberg AI, Autonomous Systems and Software Program) and ELLIIT. His work includes developing educational tools and courses in control, optimization, and machine learning. He leads and contributes to research projects on autonomous systems, cloud control, and smart energy networks. He is affiliated with the Control Lab at LTH and participates in collaborative efforts such as the Nordic University Hub on Industrial Internet of Things (HI2OT). His work integrates theoretical advances with practical implementations in robotics, biomedical systems, and industrial automation.
David Jerison is a Professor of Mathematics at the Massachusetts Institute of Technology (MIT), where he conducts research in Fourier analysis and partial differential equations. His work focuses primarily on free boundary problems and, more recently, on internal Diffusion Limited Aggregation (internal DLA), a stochastic growth model. He maintains an active research program with numerous publications in leading mathematical journals. Professor Jerison's research spans several interconnected areas of mathematical analysis. His primary interests include Fourier analysis and partial differential equations, with particular emphasis on free boundary problems. In recent years, he has expanded his research to include internal Diffusion Limited Aggregation, a stochastic growth model that has connections to probability theory and mathematical physics. His work often bridges geometric analysis, spectral theory, and probabilistic methods, demonstrating the deep connections between different branches of mathematics. Analysis of Professor Jerison's recent publications reveals a consistent focus on geometric aspects of partial differential equations, particularly free boundary problems. His research shows progression from classical PDE theory toward more stochastic and probabilistic approaches, as evidenced by his work on internal DLA. The publications demonstrate interdisciplinary connections between mathematical analysis, probability theory, and mathematical physics, with applications ranging from geometric measure theory to quantum mechanics. Professor Jerison is actively involved in teaching and mentoring at MIT. He has taught courses including Differential Equations (18.03), Fourier Analysis and Applications (18.103), and Differential Analysis (18.155). He also directs the Summer Program for Undergraduate Research (SPUR), which is exclusively for MIT undergraduates, and organizes the mathematics section of the Research Science Institute (RSI) for high school students. His teaching materials are available through MIT's Open Courseware platform, indicating his commitment to educational outreach and accessibility.
Jonathan A. Kelner is a Professor of Applied Mathematics in the Department of Mathematics at the Massachusetts Institute of Technology (MIT) and a member of the MIT Computer Science and Artificial Intelligence Laboratory (CSAIL). His research focuses on applying techniques from pure mathematics to solve fundamental problems in algorithms and complexity theory, with the goal of developing practical algorithms for real-world questions. Dr. Kelner received his undergraduate degree from Harvard University and his Ph.D. in Computer Science from MIT in 2006. Before joining the MIT faculty, he spent a year as a member of the Institute for Advanced Study. His educational background has provided a strong foundation for his interdisciplinary research spanning mathematics and computer science. His research interests include combinatorial optimization, mathematical programming, spectral graph theory, distributed computing, machine learning, computational geometry and topology, computational biology, signal processing, and random matrix theory. Kelner's work demonstrates how deep theoretical insights can lead to practical algorithmic improvements, particularly in graph algorithms and optimization problems. His approach often involves connecting seemingly disparate areas of mathematics to create novel algorithmic techniques. Analysis of his recent publications reveals a strong focus on spectral graph theory, optimization algorithms, and the Sum-of-Squares method. His work frequently addresses fundamental questions in theoretical computer science with practical implications for algorithm design. A notable trend in his research is the development of nearly-linear-time algorithms for various graph problems, which represents significant improvements over previous approaches. NSF CAREER Award Alfred P. Sloan Research Fellowship NEC Award for Research in Computers and Communication Sprowls Doctoral Dissertation Award Best Student Paper Award at STOC 2004 Best Paper Award at STOC 2011 Kokusai Denshin Denwa Junior Faculty Chair 2008 Harold E. Edgerton Faculty Achievement Award 2011 School of Science Award for Excellence in Undergraduate Education 2012 Professor Kelner has been actively involved in mentoring students and has received recognition for his teaching excellence, including the School of Science Award for Excellence in Undergraduate Education in 2012. His research has been supported by prestigious grants including the NSF CAREER Award. He has collaborated extensively with researchers across multiple institutions, often working with other leading figures in theoretical computer science to produce groundbreaking results in algorithm design. At MIT, Kelner is part of both the Mathematics Department and CSAIL, positioning him at the intersection of theoretical mathematics and practical computer science. This dual affiliation reflects the interdisciplinary nature of his work, which bridges pure mathematical theory with concrete algorithmic applications.
Kurt Johansson is a Full Professor of Mathematics at KTH Royal Institute of Technology, Sweden. His academic journey includes roles as Associate Professor at KTH (1993-2001) and Uppsala University (1988-1993), alongside research funded by the Swedish Natural Science Research Council (1998-2003). His primary affiliations are within the Department of Probability, Mathematical Physics & Statistics at KTH, where he also coordinates courses on Differential Equations and Fourier Analysis. Education: BSc in Physics (1982) and PhD in Mathematics (1988), both from Uppsala University. Research interests focus on Probability Theory , Mathematical Physics , and Random Matrix Theory , with contributions to stochastic models, determinantal processes, and universality in statistical mechanics. Key Awards: Wallenberg Prize (1995), Rollo Davidson Prize (2000), Göran Gustafsson Prize (2002), Fellow of the American Mathematical Society (2012), and multiple Wallenberg Scholar grants (2011-2023). Grants: Major funding from the Swedish Research Council (VR), K&A Wallenberg Foundation, and others. His research group explores Random Matrices, Stochastic Models, and Analysis , with notable work on the Arctic Circle Theorem and KPZ universality class. Recent publications analyze Brownian directed percolation and domino tilings of the Aztec diamond, reflecting his focus on interdisciplinary applications of probability and mathematical physics.
Gil Kalai is a Professor of Mathematics at the Hebrew University of Jerusalem since 1992, where he holds the Henry and Manya Noskwith Chair. He also serves as an Adjunct Professor of Mathematics and Computer Science at Yale University since 2004 in a long-term part-time visiting position. His academic career includes visiting positions at prestigious institutions including MIT, Cornell, IAS Princeton, Berkeley, Bell-labs, IBM, and Microsoft. Professor Kalai's research spans multiple areas within mathematics and theoretical computer science. His work in combinatorics encompasses geometric, probabilistic, and topological approaches. He has made significant contributions to the study of convex sets and polytopes, linear programming, and theoretical computer science. His influential 1988 paper with Kahn and Linial on Boolean functions pioneered applications of Fourier analysis in theoretical computer science. Kalai's research has evolved to include the application of Fourier analysis to thresholds, influences, symmetries, noise, percolation, and social choice. He has developed theories in algebraic shifting and studied face-numbers and other combinatorial invariants of polytopes. His work on the diameter of polytopes and randomized simplex algorithms has been influential in optimization theory. In 1993, his collaboration with Kahn produced a groundbreaking counterexample to Borsuk's Conjecture in 1325 dimensions. Professor Kalai's publications reveal a consistent focus on the intersection of combinatorics, geometry, and theoretical computer science. His work shows a progression from foundational combinatorial geometry to increasingly sophisticated applications of harmonic analysis in discrete mathematics. The recurring themes across his 30+ year career include Boolean functions, polytope theory, and probabilistic methods in combinatorics, demonstrating remarkable coherence in his research trajectory. 2016 European congress of Mathematics, plenary speaker 2013 ERC advanced grant 2012 Rothschild Prize 1994 International Congress of Mathematicians invited section talk, Zurich 1994 Fulkerson Prize 1993 Erdos Prize 1992 Polya Prize Though specific details of his advising are not provided in the source material, Kalai has written over 70 scientific papers and maintains an active research blog entitled "Combinatorics and More." His 2013 ERC advanced grant indicates significant research funding for his work. His extensive collaborations with researchers across multiple institutions suggest a robust research program with numerous PhD students and postdoctoral researchers, though specific names are not mentioned in the provided texts. Professor Kalai maintains active research connections across multiple institutions including Hebrew University, Yale, and various research centers worldwide. His work bridges pure mathematics and theoretical computer science, creating a unique interdisciplinary research environment that influences both fields.
