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Piotr ZwiernikView profile

Associate Professor

• Statistical Computing
• Theoretical Statistics
• High-dimensional Statistics
• +7 more

Piotr Zwiernik is an Associate Professor in the Department of Statistical Sciences at the University of Toronto's Faculty of Arts and Science, with a cross-appointment in the Department of Mathematics. Currently on leave from the University of Toronto, he is based in Barcelona following his return in July 2025. His academic journey includes a PhD in Statistics from the University of Warwick (2011), research positions at prestigious institutions including Mittag Leffler Institute, IPAM, TU Eindhoven, UC Berkeley, and the University of Genoa, and an Assistant Professorship at Universitat Pompeu Fabra in Barcelona (2016-2021). His research spans the intersection of statistics, mathematics, and computational methods, with particular emphasis on graphical models, covariance matrix estimation, convex analysis, tensors, and algebraic and combinatorial methods in statistics. Zwiernik's work demonstrates a consistent focus on high-dimensional statistics, mathematical statistics, and elegant theoretical frameworks that bridge abstract mathematics with practical statistical applications. His recent publications reveal a deepening exploration of tensor analysis, algebraic statistics, and the geometric properties of statistical models. Zwiernik serves as an associate editor for leading journals including Biometrika, Scandinavian Journal of Statistics, and Algebraic Statistics. His research program includes the development of the GOLAZO R package for asymmetric regularization of log-likelihood in Gaussian graphical models. As an academic leader, he has served as Associate Chair for Research in his department and actively participates in numerous international conferences and workshops, reflecting his significant standing in the statistical community. His recent publications show a strong trend toward algebraic and geometric approaches to statistical problems, with increasing focus on tensor methods, positivity constraints in statistical models, and the theoretical foundations of graphical models. The work demonstrates remarkable continuity in exploring the mathematical structures underlying statistical models while adapting to emerging challenges in high-dimensional data analysis. Zwiernik is committed to mathematical accessibility and education, guided by Federico Ardila's four axioms which emphasize equitable distribution of mathematical potential, joyful mathematical experiences, mathematics as a malleable tool, and treating every student with dignity and respect. He actively seeks PhD students with strong mathematical backgrounds for research at UPF or the Institute of Mathematics of UPC.

Michael FarberView profile

Professor

• Applied and Computational Topology
• Topological Robotics
• Stochastic Topology
• +3 more

Michael Farber is a Professor of Mathematics at Queen Mary University of London's School of Mathematical Sciences. Previously, he held professorships at the Universities of Warwick, Durham, and Tel Aviv. His research focuses on applied and computational topology, topological robotics, stochastic topology, and their applications in distributed computing, genomics, and brain connectivity modeling. He has authored influential monographs such as Invitation to Topological Robotics and Topology of Closed One-Forms . Farber's current research includes projects funded by the Leverhulme Trust and EPSRC, addressing probabilistic and deterministic topology, automated motion planning, and topological robotics. He advises PhD students including Lewin Strauss, Gabriele Beltramo, and Lewis Mead. His work has been recognized with the Royal Society Wolfson Research Merit Award. Key research interests include parametrized topological complexity, sequential motion planning algorithms, and the intersection of topology with AI and robotics. His collaborations span interdisciplinary fields, such as using topological methods in cancer research and genomic analysis. Grants and funding include the Leverhulme Trust's 'Probabilistic and Deterministic Topology' and EPSRC's 'Topology of Automated Motion Planning.' Farber is affiliated with Queen Mary's Centre for Geometry, Analysis, and Gravitation, contributing to advancing topological methodologies in algorithmic and stochastic systems.

David FisherView profile

Professor

• Geometric Rigidity Theory
• Dynamical Systems
• Geometric Group Theory
• +3 more

David Fisher is the Milton B. Porter Professor of Mathematics at Rice University, specializing in geometric rigidity theory, dynamical systems, and geometric group theory. His research explores lattice actions, superrigidity, quasi-isometric embeddings, and the Zimmer program, with collaborations spanning institutions like the University of Chicago and Stanford University. B.S., Columbia University Ph.D., University of Chicago (1999) His work focuses on the interplay between group actions, Lie groups, and topology. Key contributions include advancements in Zimmer's conjecture, rigidity of warped cones, and quasi-isometric rigidity of solvable groups. His publications highlight collaborations with leading mathematicians such as Alex Eskin, Gregory Margulis, and Shmuel Weinberger. Fisher’s research intersects coarse geometry, harmonic maps, and measure rigidity, often addressing fundamental questions in non-uniform lattices and affine actions. He maintains active engagement in the mathematical community through publications and academic leadership.

Ron PeledView profile

Professor

• Probability Theory
• Statistical Physics
• Mathematical Physics
• +7 more

Ron Peled is a Full Professor in the School of Mathematical Sciences at Tel Aviv University , currently on leave to serve as the Brin Professor in the Department of Mathematics at the University of Maryland starting summer 2024. During 2022–2024 he was a Member at Princeton University and the Institute for Advanced Study . Education & Career: While explicit degrees are not listed, his trajectory shows appointments at NYU (2009–2010), UC Berkeley and Tel Aviv University as a teaching assistant, followed by faculty positions culminating in full professorship. Research Interests: His work lies at the intersection of probability theory, statistical physics, and combinatorics . Key themes include: Disordered systems and random environments (random-field Ising, spin glasses) First-passage percolation and random metrics Random surfaces and height functions Loop models and critical phenomena Random matrices and band matrices Geometric probability and allocation problems Publications & Impact: With over 70 papers in top journals such as Annals of Mathematics , Annals of Probability , Inventiones Mathematicae , and Communications in Mathematical Physics , his recent work explores minimal surfaces in random environments, localization in random band matrices, and quantitative disorder effects in low-dimensional spin systems. Grants & Awards: Research has been continuously funded by: Israel Science Foundation (grants 1048/11, 861/15, 1971/19, 2340/23) ERC Starting Grant LocalOrder ERC Consolidator Grant Transitions Marie Skłodowska-Curie International Reintegration Grant SPTRF Teaching & Mentoring: Prof. Peled has taught a broad spectrum of courses at Tel Aviv University (Brownian motion, probability, percolation, random matrices, stochastic calculus) and NYU (combinatorics, discrete mathematics). He has supervised 13 post-doctoral fellows and 8 graduate students (PhD & MSc) to date. Service & Outreach: He co-organizes the Joint Israeli Probability Seminar and has organized numerous international workshops and conferences including at Oberwolfach, Technion, and Tel Aviv University.

Samuel JohnstonView profile

Lecturer

• Probability
• Stochastic Processes
• Free Probability
• +7 more

Samuel Johnston is a Lecturer in Probability Theory at the Department of Mathematics, King's College London, affiliated with the Faculty of Natural, Mathematical & Engineering Sciences. He joined King's in 2022 after postdoctoral roles at the University of Bath, University of Graz, and University College Dublin. MMath, University of Oxford (2014) PhD in Probability, University of Bath (2017) Johnston's research spans probability theory, with a focus on stochastic processes involving branching, coalescence, and fragmentation. He actively explores free probability, random matrices, integrable combinatorics, and combinatorial approaches to the Jacobian conjecture. His work intersects with statistical physics and asymptotic geometric analysis. Recent publications highlight coalescent structures in heavy-tailed branching processes, integrable probability models, free probability via entropic transport, and convexity in high dimensions. Keywords include universality classes, Berry-Esseen bounds, and fragmentation-scaling limits. Samuel has not been mentioned to have received specific scientific awards or honors. He advises PhD students Rohan Shiatis (2023-) and Neil Mukerji (2024-). Collaborations span institutions in the UK, USA, Mexico, Austria, and Poland, with invited talks at global conferences including Xiangtan University, Imperial College London, and UCLA.

June HuhView profile

Professor

• Algebraic Geometry
• Combinatorics
• Matroid Theory
• +5 more

June Huh is a Mathematics Professor at Princeton University's Department of Mathematics. His research focuses on the interplay between algebraic geometry, combinatorics, and matroid theory, with notable contributions to Hodge theory, tropical geometry, and log-concavity phenomena. He is actively involved in collaborative projects such as the FRG initiative on matroids, graphs, and algebraic geometry. Key research interests include matroid polytopes, Chow rings, Lagrangian geometry, and combinatorial applications of Hodge-Riemann relations. His work bridges discrete and continuous mathematics, with implications for enumerative geometry and geometric combinatorics. Recent publications explore topics like volume polynomials, Bergman fans, and singular Hodge theory in combinatorial geometries. He has received funding for interdisciplinary research through grants like the FRG Collaborative Research program. His contributions highlight innovative methods in geometric and algebraic combinatorics.

Avi WigdersonView profile

Professor

• Computational Complexity Theory
• Randomness and Computation
• Algorithms and Optimization
• +10 more

Avi Wigderson is the Herbert H. Maass Professor in the School of Mathematics at the Institute for Advanced Study, Princeton. He is a leading authority in theoretical computer science, particularly computational complexity theory. Wigderson organizes the Computer Science and Discrete Mathematics (CSDM) program at the Institute, fostering interdisciplinary research at the intersection of mathematics and computer science. Wigderson earned his Ph.D. (1983), M.A. (1982), and M.S.E. (1981) from Princeton University. Prior to his current position, he held appointments at The Hebrew University of Jerusalem (1986-2003), Princeton University (1990-1992), Mathematical Sciences Research Institute, Berkeley (1985-1986), IBM Research (1984-1985), and University of California, Berkeley (1983-1984). Wigderson's research spans computational complexity theory, randomness and computation, algorithms and optimization, circuit complexity, proof complexity, quantum computation and communication, and cryptography. His work explores fundamental questions like whether mathematical creativity can be automated (P vs NP problem), the security of electronic commerce, the role of randomness in computation, and the potential of quantum mechanics to enhance computation. He has made significant contributions to understanding the power and limitations of efficient computation. Analysis of Wigderson's recent publications reveals a strong focus on optimization, complexity theory, and their mathematical foundations. His work connects diverse areas including non-commutative algebra, geometric complexity, graph theory, and quantum computing. A recurring theme is exploring whether fundamental computational problems like P vs NP can be addressed through optimization techniques such as gradient descent. His research shows increasing interdisciplinary connections between theoretical computer science, mathematics, and physics. ACM A.M. Turing Award (2023) Abel Prize (2021) Donald E. Knuth Prize (2019) Gödel Prize (2009) American Mathematical Society's Levi L. Conant Prize (2008) Rolf Nevanlinna Prize (1994) Yoram Ben-Porat Presidential Prize for Outstanding Researcher (1994) Bergman Fellowship (1989) Member, American Academy of Arts and Sciences Member, National Academy of Sciences While specific details about Wigderson's students are not provided in the source material, his extensive lecture series, workshops, and program organization suggest significant mentorship activities. His book "Mathematics and Computation" published by Princeton University Press serves as an educational resource for students and researchers. Wigderson has organized major programs at the Institute for Advanced Study including "Lower Bounds in Computational Complexity" (2018) and "Pseudorandomness" (2017), creating research opportunities for numerous scholars. Wigderson leads the Computer Science and Discrete Mathematics (CSDM) program at the Institute for Advanced Study, which brings together researchers from mathematics and computer science to explore fundamental questions in computation. His work with collaborators across multiple institutions has established connections between theoretical computer science and diverse fields including quantum information theory, algebraic geometry, and optimization. Recent projects focus on non-commutative optimization and its applications to computational complexity problems.

• Artificial Intelligence
• Optimization
• Machine Learning
• +7 more

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.

Gerd StummeView profile

Professor

• Data Science
• Artificial Intelligence
• Formal Concept Analysis
• +6 more

Gerd Stumme is a Full Professor of Computer Science at University of Kassel , leading the Chair on Knowledge and Data Engineering . He serves as Executive Director of the Research Center for Information Systems Design (ITeG) , director of the International Centre for Higher Education Research (INCHER) , and founding member of the Hessian Institute for Artificial Intelligence (hessian.AI) . His research spans the intersection of Data Science, AI, and Mathematics , focusing on semantic/structural analysis of social networks, concept hierarchies, and mathematical structures (graphs, ordered sets) for knowledge acquisition. He pioneered work on Semantic Web, Web Mining, Social Bookmarking , and Recommender Systems , and has recently revisited mathematical foundations for knowledge representation. Recent publications analyze ordinal motifs in lattices , controversy mapping , and social network structures , with applications to business models, journalism, and AI. His work often integrates graph theory and formal concept analysis . He is a core developer of BibSonomy , a social bookmarking and publication-sharing system, and has contributed to FolkRank and TriAS algorithms for collaborative knowledge management.

Daniel HugView profile

Professor

• Probability
• Geometry
• (Convex) geometric analysis
• +3 more

Prof. Dr. Daniel Hug is a Professor at the Karlsruhe Institute of Technology (KIT) , affiliated with the Department of Mathematics and the Institute of Stochastics . His research focuses on Probability , Geometry , Convex geometric analysis , Stochastic geometry , and Educational mathematics . He contributes to the DFG Priority Program Random Geometric Systems and previously participated in the DFG research unit Geometry and Physics of Spatial Random Systems . Prof. Hug has co-authored two influential monographs: Poisson Hyperplane Tessellations (Springer Monographs in Mathematics) and Lectures on Convex Geometry (Springer Graduate Texts in Mathematics, 2020). His work spans theoretical advancements in convex and stochastic geometry , including integral geometry, random mosaics, and tensor valuations, alongside applied studies in digital microstructures and Minkowski tensors . His publications from 2025–2024 emphasize hyperbolic space , Boolean models , and Minkowski tensor estimators , reflecting ongoing collaborations with researchers like M. Klatt, C. Thäle, and R. Schneider. Prof. Hug leads courses such as Stochastic Geometry and seminars on Random graphs and tessellations , and is actively involved in working groups like AG Stochastische Geometrie and AG Stochastik .

Tim ConradView profile

Researcher

• Bioinformatics
• Computational Biology
• Machine Learning
• +5 more

Dr. Tim Conrad is a researcher at the Zuse Institute Berlin in the Visual and data-centric computing department under the Mathematics of Complex Systems division. He leads projects at the intersection of computational biology, AI, and medical data analysis. Projects: Geometric Learning for Single-Cell RNA Velocity Modeling, MODAL MedLab, Sparse Compressed Sensing in -Omics Data, BIFOLD (Big Data and Machine Learning) Research Networks: Affiliated with MATH+ and MODAL Research Campus His research focuses on applying machine learning , network optimization , and sparse data analysis to biological and medical challenges including disease modeling, microbiome dynamics, and ECG classification. Recent work explores hybrid PDE-ODE epidemic models and federated learning in healthcare. 2023-2025 publications highlight trends in AI for biological networks , temporal community detection , and medical signal processing . He co-authored studies on SARS-CoV-2 simulations, proteomics feature selection, and multi-label ECG analysis. His 2004 doctoral thesis at Monash University laid foundations for later work in metabolic pathway analysis. Awarded as a Zuse Fellow , he contributes to open science initiatives like FAIR data sharing . Collaborations span institutions including Freie Universität Berlin and Charité in medical informatics and clinical applications.

Marc LackenbyView profile

Professor

• Topology
• Geometry
• Group Theory
• +7 more

Prof. Marc Lackenby is a Professor of Mathematics at the Mathematical Institute, University of Oxford. His research spans topology, geometry, group theory, and their intersections, particularly focusing on low-dimensional topology and geometric algorithms. His editorial roles include serving as an editor for the International Mathematical Research Notices , Groups, Geometry and Dynamics , and the Forum of Mathematics, Pi and Sigma . He was previously an editor for the Journal of Topology (2007–2021) and the Journal of the LMS (2008–2013). Recent publications highlight his work on hyperbolic knots, triangulation complexity of 3-manifolds, and applications of machine learning to topological problems. His research bridges classical geometric topology and modern computational methods. Scientific awards include the LMS Whitehead Prize (2003), EPSRC Advanced Research Fellowship (2004–09), Philip Leverhulme Prize (2006), and the Frontiers of Science Award (2024). He was an invited speaker at the International Congress of Mathematicians (ICM) in 2010.

Ioannis EmirisView profile

Professor

• Computational Geometry
• Algebraic Algorithms
• Robotics
• +4 more

Ioannis Z. Emiris is a Professor in the Department of Informatics & Telecoms at the National & Kapodistrian University of Athens and concurrently serves as President and General Director of the ATHENA Research Center in Greece. He holds a BSc in Computer Science from Princeton University (1989) and a PhD in Computer Science from UC Berkeley (1994). His research spans computational geometry, algebraic algorithms, robotics, structural bioinformatics, and optimization. He is a leading expert in sparse elimination theory, geometric modeling, and algorithmic algebra. Affiliations: ATHENA Research Center, National & Kapodistrian University of Athens, INRIA Sophia Antipolis (France via joint AROMATH team). Education: BSc (Princeton), PhD (UC Berkeley). Research Interests Emiris's work focuses on geometric algorithms, algebraic systems, and their applications. His contributions include advancements in sparse elimination theory, computational geometry for high-dimensional data, and robotics. He has developed algorithms for polynomial system solving, Voronoi diagrams, and geometric predicates for ellipses. Articles Overview His recent work bridges theoretical advances with practical applications, such as deep learning for protein structure prediction (HydraProt) and geometric algorithms for high-dimensional data analysis. He explores intersections between algebraic geometry and computational methods, with applications ranging from robotics to bioinformatics. Scientific Awards Best Paper Award at ISSAC 2003 and 2010 MSCA Network GRAPES (2019-2023) Advising & Grants Emiris has supervised numerous students and researchers, contributing to interdisciplinary projects. He has secured grants for initiatives like the GRAPES network and has led teams in algorithm design and geometric software development. His work on MARS (Maple/Matlab/C Resultant-Based Solver) exemplifies his focus on practical algorithm implementation. Labs & Teams He directs the Lab of Geometric & Algebraic Algorithms and collaborates with the AROMATH team at INRIA. His research group develops open-source tools for computational geometry and algebraic computations.

• Geometric Group Theory
• Discrete Groups
• Metric Geometry
• +6 more

Goulnara Arzhantseva is a full Professor at the Department of Mathematics within the Faculty of Mathematics at the University of Vienna. With an active research career spanning over a decade, she has established herself as a prominent figure in geometric group theory and related fields. Her work demonstrates continuous scholarly output with publications appearing consistently from 2011 through 2024. Professor Arzhantseva's research focuses primarily on geometric group theory, with particular emphasis on discrete groups, metric geometry, approximation properties of groups, small cancellation theory, expanders, and sofic groups. Her work bridges pure mathematics with applications in metric geometry and functional analysis. The breadth of her research is evident in her collaborations with numerous mathematicians across different institutions and countries. Her publication record shows a clear trajectory of increasingly sophisticated work in geometric group theory. The most recent publications (2022-2024) focus on metric approximations of discrete groups, spectral gap properties, origami expanders, and acylindrical hyperbolicity in cubical small cancellation groups. These works represent cutting-edge research at the intersection of group theory, geometry, and combinatorics. Scientific Awards: Prize mentioned in search results (4 total, specific names not provided) Prize mentioned in search results (4 total, specific names not provided) Prize mentioned in search results (4 total, specific names not provided) Prize mentioned in search results (4 total, specific names not provided) Professor Arzhantseva has been actively involved in numerous research projects, with records showing 7 projects between 2011-2024. Her collaborative approach is evident in her extensive publication record, which includes significant contributions to prestigious mathematics journals. While specific details about her advising activities are not provided in the available information, her substantial research output suggests active mentorship of graduate students in geometric group theory.

Sainyam GalhotraView profile

Assistant Professor

• Databases
• Data Management
• Responsible Data Science
• +7 more

Sainyam Galhotra is an Assistant Professor in the Department of Computer Science at Cornell University. His research focuses on developing data science tools for effective and responsible analytics, leveraging techniques from causal inference, data management, theoretical computer science, machine learning, and human-computer interaction to address challenges in trustworthy system design including robustness, explainability, and fairness. Education: Postdoc: University of Chicago PhD: University of Massachusetts Amherst (supervised by Barna Saha) BTech: Indian Institute of Technology Delhi (IIT Delhi) (supervised by Prof. Amitabha Bagchi) Research Interests: Dr. Galhotra's research spans several interconnected areas in data science and artificial intelligence. His work primarily focuses on Responsible Data Science , where he develops methods to ensure that data-driven systems operate fairly and transparently. Within this broad area, his specific interests include: Causal Inference techniques for understanding cause-effect relationships in complex data Algorithmic Fairness approaches to mitigate bias in machine learning systems Explainable AI methods that make black-box models more interpretable Data Management systems for efficient and reliable data processing Entity Resolution techniques for integrating data from multiple sources Trustworthy System Design that addresses robustness, explainability, and fairness His recent publications demonstrate a clear trend toward developing frameworks that combine causal reasoning with practical data management systems, particularly focusing on how to make data-driven decisions more transparent and equitable. The intersection of database systems with fairness considerations appears to be a particularly active area of his research. Scientific Awards: Rising Star in Data Science at the Data Science Institute, UChicago (Oct 2021) Computing Innovation Fellowship Award Recipient (by CRA, CCC and NSF) (Apr 2021) DAAD AInet Fellow (Feb 2021) ACM SIGMOD Entity Resolution Programming Contest – Top 5 finalist (May 2020) Most reproducible paper award in SIGMOD 2018 and 2019 (Jun 2019) First recipient of Krithi Ramamritham Computer Science Scholarship (Jun 2019) Best paper award in SIGSOFT FSE 2017 (May 2017) Dr. Galhotra is actively seeking students to collaborate with on his research projects. His work has been supported by various fellowships and awards, including the prestigious Computing Innovation Fellowship. He has mentored several students through his research projects, with a focus on developing the next generation of data scientists who can build responsible and trustworthy systems. His research group appears to focus on the intersection of database systems and responsible AI, developing tools like HypeR for causal reasoning, Ver for view discovery, and Nexus for correlation discovery in spatio-temporal data. This work suggests a cohesive research agenda centered around making data systems more transparent, fair, and user-friendly.