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Ricky Ini Liu is an Associate Professor in the Department of Mathematics at the University of Washington. Previously, he held positions at North Carolina State University, the University of Michigan, and the University of Minnesota. He earned his Ph.D. in Mathematics from MIT in 2010 under Alexander Postnikov. His research focuses on algebraic combinatorics, particularly its intersections with algebraic geometry, combinatorial geometry, and representation theory. Key interests include Schubert polynomials, polytopes, Hopf algebras, and Kronecker coefficients. He has contributed to foundational work on birational rowmotion, Gelfand-Tsetlin polytopes, and Fomin-Kirillov algebras. Liu has taught a wide range of courses at UW, including special topics in dynamical algebraic combinatorics, combinatorial theory, and problem-solving. He has also been a key instructor at the Mathematical Olympiad Summer Program since 2007 and mentored undergraduates in research programs at the University of Minnesota, Duluth. His publications span high-impact journals like Selecta Mathematica and Journal of Combinatorial Theory , with recent work addressing topics such as determinantal formulas for Schubert polynomials and applications of flow polytopes to diagonal harmonics. Though no specific awards are listed, his extensive publication record and academic roles reflect significant contributions to combinatorial mathematics.

• Algebraic Combinatorics
• Algebraic Geometry
• Combinatorial Geometry
• +5 more

Stanislav Smirnov is a Professor of Mathematics at the University of Geneva, where he has been a faculty member since 2003. He also holds a part-time position at the Chebyshev Laboratory of St. Petersburg State University. As Director of the National Centre of Competence in Research SwissMAP since 2014, he leads a major research initiative funded by the Swiss National Science Foundation. His education includes a mathematics degree from St. Petersburg State University (1992) and a PhD from the California Institute of Technology (1996). Prior to joining Geneva, he held positions at Yale University, the Institute for Advanced Study in Princeton, the Max Planck Institute in Bonn, and the Royal Institute of Technology in Stockholm. Professor Smirnov's research focuses on the mathematical foundations of statistical physics, with particular emphasis on conformal invariance in two-dimensional lattice models. His work has revolutionized our understanding of critical phenomena through rigorous mathematical approaches, establishing deep connections between probability theory, complex analysis, and statistical mechanics. He develops novel discrete complex analysis techniques to prove continuum limits of lattice models at criticality. His publication record shows consistent advancement in understanding random planar geometry, percolation theory, and the Ising model, with increasing attention to Liouville quantum gravity in recent years. His work bridges deep mathematical analysis with physical insights, often providing rigorous proofs for phenomena previously understood only through physical intuition. Fields Medal (2010) for the proof of conformal invariance of percolation and the planar Ising model in statistical physics As an advisor, Professor Smirnov has mentored doctoral students including Ekin Arikök and Nikolai Bobenko. He leads the ANALYSE, PHYSIQUE MATHÉMATIQUE ET PROBABILITÉS research group at the University of Geneva. His research is supported by major grants including the NCCR SwissMAP, European Research Council's AG COMPASP, and Swiss National Science Foundation grants 200020_143434 and 200020_141329. Professor Smirnov has organized numerous influential conferences, including the "Conformal maps from probability to physics" workshop at Monte Verità (2010) and multiple "Random Planar Geometry" meetings in Les Diablerets, fostering collaboration between mathematicians and physicists working on critical phenomena and random geometry.

• Mathematical Physics
• Statistical Physics
• Probability Theory
• +4 more

Robert Ghrist is the Andrea Mitchell University Professor at the University of Pennsylvania with dual appointments in the Department of Mathematics and the Department of Electrical and Systems Engineering. He serves as Associate Dean for Undergraduate Education for Penn Engineering. His educational background includes a B.S. in Mechanical Engineering from the University of Toledo (1991), and M.S. and Ph.D. degrees in Applied Mathematics from Cornell University (1994, 1995). Ghrist's research bridges pure and applied mathematics, focusing on applied algebraic topology , dynamical systems , and geometric methods in data science. His work extends to network theory, topological data analysis, and computational geometry, with applications spanning robotics, neuroscience, and social dynamics. Key innovations include developing sheaf-theoretic approaches for networked systems and persistence homology techniques for high-dimensional data. Analysis of his recent publications reveals a strong emphasis on lattice-theoretic frameworks , topological robotics , and network dynamics , with emerging applications in neural data interpretation and geometric computing. His research consistently integrates category theory with real-world engineering challenges. Significant scientific recognition includes: Presidential Early Career Award (PECASE, 2004) Scientific American 'Top 50' Research Leader (2007) Mathematical Association of America's Chauvenet Prize (2013) University of Pennsylvania Lindback Award for Distinguished Teaching (2015) DoD National Security Science and Engineering Faculty Fellowship (NSSEFF, 2015) Ghrist leads multiple federally funded research initiatives supported by AFOSR, DARPA, NSF, and ONR. He directs the development of educational tools including the Calculus BLUE/GREEN Project video series and custom GPTs for mathematical pedagogy. His open online courses have reached over 100,000 learners globally.

• Applied Topology
• Dynamical Systems
• Algebraic Topology
• +5 more

Kazuhiro Saitou is a Professor of Mechanical Engineering at the University of Michigan, affiliated with the College of Engineering. His research focuses on computational design synthesis, topology optimization, and manufacturing process integration. He leads the Algorithmic Synthesis Laboratory (ASL), advancing algorithms for automated design and optimization of mechanical systems. Education: Ph.D. (1996), MIT; M.S. (1992), MIT; B.Eng. (1990), University of Tokyo. He has held tenured positions since 1997, including roles as Founding CEO of Comnext, Inc. (2007–2012) and visiting professorships at École Centrale Paris and Donghua University. Research interests include multi-material topology optimization (M^3 TO), AI-driven design, and sustainable manufacturing. Key projects address additive manufacturing, composite structures, and energy-efficient production systems. He has pioneered methods for manufacturability-driven design and assembly optimization. Notable awards include IEEE Fellow (2018), ASME Kos-Ishii Award (2015), and NSF CAREER Award (1999). He serves as Editor-in-Chief for IEEE Transactions on Automation Science and Engineering and holds leadership roles in ASME and IEEE societies. Teaching includes courses on design optimization, CAD, and global product development. His lab has advised over 30 students, with alumni in academia and industry. Current research explores biomechanical modeling, traffic flow optimization, and medical image registration algorithms.

• Computational Design Synthesis
• Topology Optimization
• Manufacturing Process Optimization
• +7 more

Yang P. Liu is an Assistant Professor at Carnegie Mellon University 's Department of Computer Science . He received his PhD from Stanford University under the supervision of Aaron Sidford and previously studied at MIT . Fields of Interest : Graph Algorithms, Optimization, High-Dimensional Geometry, Additive Combinatorics, Theoretical Computer Science. His research focuses on algorithmic design and analysis for graph problems, optimization, and combinatorics, with applications in machine learning and complexity theory. Recent work includes advancements in parallel repetition games , combinatorial lines , and dynamic graph algorithms . In 2024, his research spanned FOCS , STOC , and RANDOM conferences, addressing problems in k-CSPs , min-cost flow , and hypergraph sparsification . Earlier contributions (2023) included deterministic flow algorithms and spectral hypergraph techniques. Scientific Awards : NDSEG Fellowship (2018-2021), Google PhD Fellowship (2022-2023), FOCS Best Paper (2022), STOC Best Student Paper (2022), FOCS Best Student Paper (2021). He teaches CS 15-759 , a graduate course on convex optimization theory and applications, covering gradient descent, interior point methods, and algorithmic sparsification techniques.

• Graph Algorithms
• Optimization
• High-Dimensional Geometry
• +3 more

Paul Goldberg is a Professor of Computer Science and Director of the MSc in Mathematics and Foundations of Computer Science (MFoCS) at the University of Oxford. He holds a BA in Mathematics from Oxford University and a PhD in Computer Science from the University of Edinburgh. His research focuses on algorithmic game theory, computational complexity, and machine learning, with notable contributions to equilibrium computation, complexity classes of total search problems, and decentralized systems. Affiliations: Department of Computer Science, Oxford; Editorial Board of ACM Transactions on Economics and Computation. Education: PhD in Computer Science (1993), University of Edinburgh MSc in Computer Systems Engineering (1989), University of Edinburgh and Université Paris-Sud BA in Mathematics (1988), Oxford University Research Interests: Algorithmic game theory, computational complexity (especially total search problems like CLS and PPAD), decentralized computation of equilibria, and applications in machine learning and AI. His work bridges theoretical computer science and economics, with a focus on algorithm design and complexity analysis. Publications and Awards: Over 120 papers, including influential work on Nash equilibrium complexity (2009), gradient descent (2023), and fair division algorithms. Notable awards include the ACM SIGecom Test of Time Award (2022) and a SIAM Outstanding Paper Prize (2011). Grants and Students: Leads EPSRC-funded projects on game theory and machine learning. Supervised 11 PhD graduates and currently advises Giannis Tyrovolas and others. Active in mentoring MSc and undergraduate projects. Labs/Teams: Part of the Algorithms and Complexity Theory group at Oxford, contributing to research on optimization, equilibrium dynamics, and fair division.

• Algorithmic Game Theory
• Computational Complexity Theory
• Machine Learning
• +3 more

Daniel Dominic Kaplan Sleator is a Professor of Computer Science at Carnegie Mellon University's School of Computer Science. He maintains an office in the Gates-Hillman Center (7205 Gates-Hillman) and teaches various courses in algorithms and theoretical computer science. Professor Sleator's research spans several areas of theoretical computer science and algorithms. His primary interests include: Algorithms and Data Structures Amortized Analysis and Competitive Analysis Persistent and Self-Adjusting Data Structures Computational Geometry and Combinatorial Optimization Combinatorial Game Theory and Mathematical Games Music Analysis using Computational Methods His extensive publication record shows a consistent focus on efficient data structures and algorithms. Over the years, his work has evolved from foundational data structures like splay trees and skew heaps to applications in diverse areas such as music analysis and combinatorial games. A notable trend in his work is the development of self-adjusting data structures that achieve excellent amortized performance without maintaining explicit structural constraints. His papers on splay trees, skew heaps, and persistent data structures have become classics in the field. Professor Sleator has made significant contributions across multiple domains of computer science. His work on competitive algorithms for paging and list update problems has been particularly influential, establishing fundamental results in online algorithms. His research extends beyond traditional computer science into interdisciplinary areas like computational music theory, demonstrating the broad applicability of algorithmic thinking. He teaches a variety of courses including Algorithms 15-451/651, Competition Programming 15-295, and specialized topics like mathematical games.

• Algorithms
• Data Structures
• Theoretical Computer Science
• +5 more

Nina Balcan is a Professor at Carnegie Mellon University and holds the Cadence Design Systems Professorship in Computer Science. She is affiliated with the School of Computer Science, specifically the Machine Learning Department (MLD) and Computer Science Department (CSD). Her research spans foundational aspects of machine learning, artificial intelligence, theoretical computer science, algorithmic game theory, and interdisciplinary connections in learning theory. Machine Learning Artificial Intelligence Theoretical Computer Science Algorithmic Game Theory Multi-Agent Systems Data-Driven Algorithm Design Her recent work focuses on advancing algorithm design through machine learning, robustness in adversarial environments, and economic modeling. Key contributions include Learning to Branch (JACM 2024), Regret Minimization in Stackelberg Games (NeurIPS 2024), and Learning Accurate Decision Trees (UAI 2024, Outstanding Student Paper Award). She has pioneered novel approaches to data-driven optimization, semi-supervised learning, and privacy-preserving clustering. Nina has received prestigious accolades including ACM Fellow AAAI Fellow Simons Investigator 2019 ACM Grace Murray Hopper Award Her teaching at CMU includes graduate courses on machine learning, advanced machine learning, and specialized topics like algorithmic game theory.

• Machine Learning
• Artificial Intelligence
• Theoretical Computer Science
• +12 more

Ewain Gwynne is a Professor of Mathematics at the University of Chicago, affiliated with the Committee on Computational and Applied Mathematics (CCAM) and the Statistics Department. He previously held postdoctoral positions at the University of Cambridge and earned his Ph.D. from MIT in 2018 under Scott Sheffield. His research focuses on probability theory, particularly random geometric structures in statistical mechanics, including Schramm-Loewner evolution (SLE), Liouville quantum gravity (LQG), and random planar maps. Education: Ph.D. in Mathematics, MIT (2018); M.Sc., MIT (2015); B.Sc., Northwestern University (2013). Research Interests: Random geometric objects in statistical mechanics Liouville quantum gravity and its metric properties Random planar maps and their scaling limits SLE and its relationship with LQG Random walks on random planar maps Percolation and permutons His recent articles explore topics such as supercritical LQG, Gaussian curvature on random maps, and harmonic balls in LQG. He has advised multiple Ph.D. students and serves as an associate editor for Probability and Mathematical Physics . His work bridges probability theory, geometry, and mathematical physics, with applications to understanding critical phenomena in random systems.

• Probability Theory
• Liouville Quantum Gravity
• Random Planar Maps
• +7 more

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.

• Combinatorics
• Probability
• Graph Theory
• +3 more

Pierre Vandergheynst is a Full Professor at the Swiss Federal Institute of Technology Lausanne (EPFL) in the Department of Electrical Engineering, with a courtesy appointment in Computer and Communication Sciences. He serves as EPFL’s Vice-Provost for Education since 2015 and leads the Signal Processing Laboratory 2 (LTS2). His research spans harmonic analysis, sparse approximations, mathematical data processing, and applications in signal/image processing, computer vision, machine learning, and graph-based data analysis. PhD in Mathematical Physics (1998), Université catholique de Louvain Postdoctoral Researcher at EPFL (1998-2001) Assistant Professor at EPFL (2002-2007) His research explores geometry/symmetry in high-dimensional data, redundant dictionaries for dimensionality reduction, and computational harmonic analysis on manifolds. Recent work focuses on protein structure modeling, geometric deep learning, and graph-based signal processing. Key article trends include graph neural networks for protein analysis, geometric deep learning in neuroscience, and structured knowledge priors in neural models. His 2023-2025 publications emphasize interpretable AI, long-range dependencies in graphs, and molecular representation learning. Scientific Awards: IEEE Signal Processing Magazine Best Paper Award (2023) Signal Processing Society Best Paper Award (2022) Apple ARTS Award (2007) De Boelpaepe Prize, Royal Academy of Sciences of Belgium (2009-2010) He has supervised over 30 PhD theses and contributed to foundational work in graph signal processing, compressive sensing, and geometric deep learning. His lab develops tools for data science on non-Euclidean structures, with applications in medicine, astronomy, and wireless systems.

• Data Science
• Machine Learning
• Computational Harmonic Analysis
• +6 more

Mark D. Haiman is a Professor at the University of California, Berkeley, Department of Mathematics, with research interests spanning algebra, combinatorics, and algebraic geometry. His work connects symmetric function theory with geometric objects like Hilbert schemes and algebraic structures such as Cherednik algebras and Hecke algebras. Appointed: 2001 Contact: mhaiman@math.berkeley.edu Teaching: Math 256B—Algebraic Geometry (Spring 2025), Math 249—Algebraic Combinatorics (Spring 2024), and others in calculus and Lie groups. Research Interests : Haiman's research focuses on Macdonald polynomials, LLT polynomials, Hilbert schemes of points in the plane, and their combinatorial and geometric implications. His work includes resolving the Macdonald positivity conjecture and the n! conjecture through algebraic geometry. Publications : Haiman has contributed to foundational papers in combinatorial and algebraic structures, including generalizations of the shuffle theorem and positivity results for LLT polynomials. His articles often bridge representation theory, symmetric functions, and geometric methods. Students : He has supervised numerous PhD students, including Magda Hlavacek (2023), Foster Tom (2022), Jeremy Meza (2021), Maryam Farahmand-Asil (2018), Maria Monks Gillespie (2016), and others working on combinatorial algebraic geometry and related fields.

• Algebra
• Combinatorics
• Algebraic Geometry
• +12 more

Ştefan Tohăneanu is a Professor in the Department of Mathematics and Statistical Science at the University of Idaho , affiliated with the College of Science. His academic journey includes a Ph.D. in Mathematics from Texas A&M University (2007), and M.S. degrees in Algebra (2001) and Analysis (2001) from the University of Bucharest, where he also earned a B.S. in Mathematics (1997). Research Focus: Commutative Algebra, Hyperplane Arrangements, Matroid Theory, and applications to Coding Theory, including generalized Hamming weights, Orlik-Terao algebras, and homological properties of ideals. Publications: Recent work explores Betti numbers, Jacobian ideals, logarithmic derivations, and connections between algebraic invariants and coding theory problems like minimum distance computation and error correction. Collaborations: Engages with global research networks through affiliations with institutions such as Texas A&M University, University of Bucharest, and University of Idaho.

• Commutative Algebra
• Algebraic Geometry
• Hyperplane Arrangements
• +4 more

Afonso S. Bandeira is a Professor in the Department of Mathematics (D-MATH) at ETH Zurich, where he conducts research at the intersection of mathematics, statistics, and computer science. He maintains strong affiliations with several interdisciplinary research centers including the Institute For Operations Research (IFOR), the Max Planck ETH Center for Learning Systems, the ETH Foundations of Data Science, and the ETH AI Center, with a courtesy appointment at D-ITET. Bandeira actively teaches courses including Mathematics of Signals, Networks, and Learning, and Mathematics of Data Science. His research focuses on High Dimensional Probability, Random Matrices, Mathematical Statistics, Theoretical Computer Science, Combinatorics, and Mathematical Optimization. Bandeira's work often explores the theoretical foundations of data science, examining phase transitions in statistical problems, computational barriers, and the geometry of high-dimensional spaces. He maintains a research group blog called Randomstrasse101 that emphasizes open problems in his field. Analysis of his recent publications reveals a strong focus on matrix and tensor concentration inequalities, synchronization problems, nonconvex optimization landscapes, and computational-statistical tradeoffs in high-dimensional inference. His work bridges theoretical mathematics with practical applications in machine learning and data analysis, particularly examining where computational limitations arise in statistical problems. Bandeira actively mentors students and researchers, currently supervising several doctoral candidates including Daniil Dmitriev, Konstantin Donhauser, Anastasia Kireeva, Kevin Lucca, Chiara Meroni, Gil Kur, Petar Nizic-Nikolac, and Almut Roedder. He emphasizes that students working with him should participate in the DACO seminar and group meetings, and encourages prospective students to have completed his Mathematics of Signals, Networks, and Learning or Mathematics of Data Science courses. He leads a research group focused on the mathematics of data science, with regular group meetings and seminars. Bandeira has developed comprehensive lecture notes including 'A Tour Through the Mathematics of Signals, Learning, and Networks' (2025) and 'Mathematics of Machine Learning' (2021), and previously authored 'Ten Lectures and Forty-Two Open Problems in the Mathematics of Data Science' (2015).

• High Dimensional Probability
• Random Matrices
• Mathematical Statistics
• +3 more

Professor Jelena Grbic is a distinguished mathematician serving as Professor of Mathematics within the School of Mathematical Sciences at the University of Southampton since 2012. Her academic journey began with a B.Sc. in Mathematics from the University of Belgrade, Serbia in 1997, followed by a Ph.D. in Algebraic Topology from the University of Aberdeen in 2004. Prior to her current position, she held academic appointments at the University of Manchester (2007-2012) as Lecturer and Senior Lecturer, and at the University of Aberdeen (2004-2006) as Lecturer. Professor Grbic's research spans multiple interconnected domains of pure mathematics, with a primary focus on modern homotopy theory, particularly unstable homotopy theory, and its applications across topology, algebra, and geometry. Her work centers on decompositions and exponent problems in homotopy theory, homotopy aspects of Toric Topology, Hopf algebras, and geometric problems related to cobordisms and string topology. This research bridges abstract mathematical theory with potential applications in data science and computational topology. Analysis of her recent publications (2020-2025) reveals a consistent research trajectory in algebraic topology with increasing interdisciplinary connections. Her work demonstrates sophisticated mathematical techniques applied to complex topological structures, particularly moment-angle complexes and polyhedral products. Notably, her 2022 paper 'Aspects of topological approaches for data science' indicates growing interest in applying topological methods to contemporary data analysis problems, suggesting an expanding research horizon beyond pure mathematics. Professor Grbic actively supervises PhD students, including current student Salvatore Elia in Mathematical Sciences, and teaches modules covering algebraic topology and homotopy theory. She serves as a reviewer for prestigious journals including Homology, Homotopy and Application (2019), Transactions of the London Mathematical Society (2021), and The LMS Newsletter (2017), contributing to the scholarly community through peer review and academic service.

• Topology
• Algebra
• Combinatorics
• +6 more