جست‌وجوی استاد و پژوهشگر

Piotr Zwiernikمشاهده پروفایل

دانشیار

• Statistical Computing
• Theoretical Statistics
• High-dimensional Statistics
• +۷ مورد دیگر

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 Farberمشاهده پروفایل

استاد

• Applied and Computational Topology
• Topological Robotics
• Stochastic Topology
• +۳ مورد دیگر

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.

Nathan Kaplanمشاهده پروفایل

استاد

• Number Theory
• Algebraic Geometry
• Combinatorics
• +۶ مورد دیگر

Nathan Kaplan is a Professor in the Department of Mathematics at the University of California, Irvine, where he conducts research in number theory, algebraic geometry, and combinatorics. His work spans rational points on varieties over finite fields, arithmetic statistics, coding theory, and the study of numerical semigroups. He is actively involved in the mathematical community, organizing seminars and conferences including the UC Irvine Number Theory Seminar and the Southern California Number Theory Day. Dr. Kaplan received his PhD from Harvard University in 2013 under the direction of Noam Elkies. Following his doctorate, he was a postdoctoral researcher at Yale University from 2013-2015 before joining the faculty at UC Irvine. His research interests focus on the intersection of number theory and algebraic geometry, with particular attention to problems involving rational points on varieties over finite fields, arithmetic statistics, and coding theory. He has made significant contributions to the study of numerical semigroups, cokernels of random p-adic and integer matrices, and quadratic forms and lattices. His work often bridges theoretical mathematics with applications in coding theory and cryptography. Analysis of his recent publications shows a strong trend toward combinatorial aspects of number theory, particularly in the study of numerical semigroups and their properties. He frequently collaborates with researchers across institutions, with recent work spanning algebraic geometry, combinatorics, and coding theory. His publications demonstrate expertise in both theoretical developments and computational aspects of number theory. Dr. Kaplan is deeply committed to undergraduate research and mentoring. He has experience as a mentor for undergraduate research projects through programs including SUMRY (a research program for Yale undergraduates), the University of Minnesota-Duluth REU program, and the Trinity University REU program. He actively encourages undergraduates to apply for summer research opportunities and has organized numerous outreach activities. He is an organizer of the UC Irvine Number Theory Seminar and the Southern California Number Theory Day conference series. In 2018, he co-organized the Conference on Open Questions in Cryptography and Number Theory in honor of Alice Silverberg's 60th Birthday. Dr. Kaplan has given numerous talks at mathematical venues including the Museum of Mathematics' Math Encounters series, where he presented "Error-Correcting Codes: The Mathematics of Communication" in July 2022. He has also spoken at the Yale Undergraduate Math Society, the UCI Math Circle, and various other outreach events.

Arnab Senمشاهده پروفایل

دانشیار

• Probability Theory
• Discrete Harmonic Analysis
• Statistical Physics
• +۳ مورد دیگر

Arnab Sen is an Associate Professor at the School of Mathematics, University of Minnesota. His research focuses on probability theory and discrete harmonic analysis, with emphasis on models from statistical physics such as spin glasses, random graphs, random matrices, and random polynomials. PhD in Statistics, UC Berkeley (2010), advised by Steven N. Evans and Elchanan Mossel Postdoctoral Fellow, Statistical Laboratory, University of Cambridge His research spans discrete probability , statistical physics , and random matrix theory , addressing topics like disorder chaos in spin glasses, eigenvalue distributions, and quantum percolation. He has taught graduate and undergraduate courses including Random Matrix Theory , Introduction to Stochastic Processes , and Multivariable Calculus . His recent publications analyze spin glass models, random matrices, and combinatorial systems.

Matilde Marcolliمشاهده پروفایل

استاد

• Noncommutative Geometry
• Mathematical Physics
• Number Theory
• +۵ مورد دیگر

Matilde Marcolli is the Robert F. Christy Professor of Mathematics and Computing and Mathematical Sciences at the California Institute of Technology (Caltech). She holds joint appointments in the Division of Physics, Mathematics, and Astronomy (PMA) and the Division of Engineering and Applied Sciences (EAS). Her research spans noncommutative geometry, mathematical physics, number theory, and mathematical linguistics. She has been recognized with prestigious awards such as the Sofja Kovalevskaya Award (2001) and the Heinz Maier Leibnitz Prize (2001). Marcolli has advised numerous PhD students and contributed to over 300 publications. Her work bridges abstract mathematics with applications in cosmology, quantum field theory, and computational linguistics. Education : PhD in Mathematics, University of Chicago, 1997 M.Sc., University of Chicago, 1994 Laurea in Mathematics, University of Pavia, 1993 Research Interests : Marcolli’s research explores the intersection of geometry, number theory, and physics. Key areas include noncommutative geometry models of particle physics and cosmology, motives in quantum field theory, and algebraic models of generative linguistics. She applies advanced techniques such as Feynman integrals, spectral action principles, and Hopf algebras to interdisciplinary problems. Grants & Awards : NSF grants DMS-2104330, DMS-1707882, and others NSERC Discovery Grant RGPIN-2018-04937 FQXi grant FQXi-RFP-1804 Collaborations & Labs : Marcolli collaborates with institutions like the Perimeter Institute and Florida State University. She leads research groups on topics such as quantum statistical mechanics, holography, and neural information networks. Her work on syntax-semantics interfaces and quantum gravity models has been pivotal in interdisciplinary studies.

Ron Peledمشاهده پروفایل

استاد

• Probability Theory
• Statistical Physics
• Mathematical Physics
• +۷ مورد دیگر

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.

Christopher Ferrieمشاهده پروفایل

دانشیار

• Quantum Estimation and Control
• Quantum Machine Learning
• Quantum Information Science
• +۵ مورد دیگر

Christopher Ferrie is an Associate Professor at the University of Technology Sydney (UTS), where he is affiliated with the Faculty of Engineering and Information Technology and the Centre for Quantum Software and Information (QSI). His academic career spans quantum information science, machine learning, and scientific education, with a strong emphasis on both theoretical research and public engagement through science communication. Full-time faculty member at UTS Active researcher in quantum information science Director of the Centre for Quantum Software and Information Author of numerous scientific publications and popular science books Dr. Ferrie earned his PhD in Applied Mathematics from the Institute for Quantum Computing and University of Waterloo in Canada in 2012. His doctoral work focused on quantum information and laid the foundation for his subsequent research career in quantum computing and related fields. Dr. Ferrie's research interests span several interconnected domains within quantum information science. His primary focus is on quantum estimation and control, with particular emphasis on applying machine learning techniques to solve statistical problems in quantum information science. He investigates how quantum systems can be characterized, controlled, and optimized for practical applications. His work bridges theoretical quantum physics with practical implementations, exploring how quantum phenomena can be harnessed for computational advantage. Recent research directions include quantum machine learning, quantum neural networks, and quantum optimization algorithms, with applications ranging from quantum state tomography to solving combinatorial optimization problems. Analysis of Dr. Ferrie's recent publications reveals a strong focus on practical quantum computing challenges. His work consistently addresses the intersection of quantum information theory and machine learning, with particular emphasis on making quantum algorithms more efficient, interpretable, and robust against noise. A significant portion of his recent research explores variational quantum algorithms and their optimization, reflecting the current priorities in near-term quantum computing. His publications also demonstrate growing interest in quantum machine learning applications and the development of techniques for quantum error mitigation and characterization. Dr. Ferrie has secured multiple research grants supporting his work in quantum computing and related fields. His funded projects span quantum control, quantum probability, quantum machine learning, and statistical decision theory, reflecting the breadth of his research program. While specific major awards aren't detailed in the available information, his sustained funding and publication record indicate significant recognition within the quantum information science community. Dr. Ferrie is actively involved in research supervision and teaching, with current funding supporting multiple PhD students and postdoctoral researchers. His teaching responsibilities include courses on quantum computing, where he introduces students to the fundamentals of quantum information processing. His research group at the Centre for Quantum Software and Information focuses on developing novel quantum algorithms and exploring the practical implementation challenges of quantum computing. The Centre for Quantum Software and Information at UTS serves as the primary research environment for Dr. Ferrie's work. This center brings together researchers working on various aspects of quantum computing, from hardware development to algorithm design and applications. Dr. Ferrie's team within the center focuses specifically on quantum software development, quantum algorithm design, and the application of machine learning techniques to quantum information problems. The collaborative environment enables interdisciplinary research that bridges theoretical quantum physics with practical computing applications.

David Alan Goldbergمشاهده پروفایل

دانشیار

• Applied Probability
• Operations Research
• Stochastic Processes
• +۷ مورد دیگر

David Alan Goldberg is an Associate Professor in the School of Operations Research and Information Engineering (ORIE) at Cornell University, part of Cornell Engineering. He joined Cornell in 2017 and previously held the A. Russel Chandler III Associate Professorship at Georgia Tech’s Industrial and Systems Engineering department. Goldberg earned his Ph.D. in Operations Research from MIT (2011) and a B.S. in Computer Science from Columbia University (2006). Education: B.S. in Computer Science, Columbia University (2006) Ph.D. in Operations Research, MIT (2011) Research Interests: Goldberg’s work focuses on applied probability and stochastic processes, including optimal stopping, inventory and queueing models, combinatorial optimization, and robust optimization. He develops algorithms and insights for complex systems, addressing challenges like the curse of dimensionality. His research spans applications in data science, operations research, and stochastic modeling. Notable contributions include distributionally robust inventory control and high-dimensional decision-making frameworks. Awards and Honors: 2025 Community-Engaged Practice and Innovation Award (David M. Einhorn Center) 2023 Sunny Yau ’72 Teaching Award (Cornell) 2019 INFORMS Applied Probability Society Best Publication Award 2015 NSF CAREER Award Multiple INFORMS Nicholson Student Paper Competitions (First Place, 2019 & 2015) Teaching and Service: Goldberg leads Cornell ORIE’s undergraduate research program, connecting students to real-world applications of OR and data science. He teaches courses in probability modeling, stochastic models, and academic skills for PhD students. He chairs the INFORMS Applied Probability Society and serves on editorial boards for Operations Research and Stochastic Systems . At Cornell, he advises the Undergraduate ORIE Society and directs undergraduate studies in ORIE. Labs & Collaborations: Goldberg’s research integrates theoretical rigor with practical applications, often involving collaborations across disciplines. His work bridges operations research, statistics, and computer science to address modern challenges in inventory systems, queueing networks, and decision-making under uncertainty.

Samuel Johnstonمشاهده پروفایل

مدرس

• Probability
• Stochastic Processes
• Free Probability
• +۷ مورد دیگر

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.

• Representation Theory
• Algebraic Combinatorics
• Mathematical Physics
• +۳ مورد دیگر

Arun Ram is a Professor and Chair of Pure Mathematics at the School of Mathematics and Statistics . His work bridges representation theory, algebraic combinatorics, and mathematical physics, with a focus on Hecke algebras, Macdonald polynomials, and symmetry in algebraic structures. Education: PhD, University of California - San Diego Bachelors Degree, Massachusetts Institute of Technology His research explores the interplay of representation theory with combinatorial models and geometric configurations, including applications to network analysis and number systems. Key contributions include advancements in understanding Macdonald polynomial expansions, Clebsch-Gordan coefficients, and Monk rules. His projects, such as Tantalizer Algebras and Macdonald Polynomials: Combinatorics and Representations , highlight collaborations and grants in algebraic research. While no explicit scientific awards are listed, his 66+ scholarly works and 2007-2016 research contracts demonstrate sustained academic impact.

Lauren K. Williamsمشاهده پروفایل

استاد

• Algebraic Combinatorics
• Cluster Algebras
• Mathematical Physics
• +۴ مورد دیگر

Lauren K. Williams is the Dwight Parker Robinson Professor of Mathematics at Harvard University and the Sally Starling Seaver Professor at the Radcliffe Institute. Her research focuses on algebraic combinatorics, cluster algebras, and mathematical physics, with notable contributions to the study of the positive Grassmannian, amplituhedron geometry, and integrable systems. She holds affiliations with Harvard’s Department of Mathematics and the Radcliffe Institute for Advanced Study. Her work bridges combinatorics, algebraic geometry, and physics, particularly in understanding geometric structures like the amplituhedron, which encode scattering amplitudes in quantum field theory. Key areas include cluster algebras, Schubert varieties, and applications of combinatorial methods to stochastic processes such as the asymmetric exclusion process (ASEP). Recent activities include organizing conferences on combinatorics, mathematical physics, and the legacy of mathematicians like Richard P. Stanley. Her research often explores connections between discrete structures and continuous systems, with a focus on positivity and geometric positivity principles. Awards and grants are not explicitly listed in the provided texts, but her contributions have been recognized through invitations to major international conferences and leadership in the field. She actively mentors students and postdocs in combinatorics and algebraic geometry.

June Huhمشاهده پروفایل

استاد

• Algebraic Geometry
• Combinatorics
• Matroid Theory
• +۵ مورد دیگر

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.

• Algebraic Combinatorics
• Representation Theory
• Statistical Mechanics
• +۳ مورد دیگر

Greta Panova is a Gabilan Distinguished Professor of Science and Engineering and a Professor of Mathematics at the University of Southern California (USC). Her research focuses on Algebraic Combinatorics, with connections to representation theory, statistical mechanics, probability, and computational complexity theory. She also engages in molecular biology modeling. Panova holds editorial roles at journals including the Electronic Journal of Combinatorics, Arnold Mathematical Journal, and Communications of the American Mathematical Society. She is a writer/editor for the Putnam Mathematical Competition (2023-2025) and is currently supported by NSF grants in the CCF division. Her research interests span Algebraic Combinatorics, Representation Theory, Statistical Mechanics, Probability, and Computational Complexity Theory. Specific areas include Kronecker and Littlewood-Richardson coefficients, asymptotic behavior of combinatorial structures, and the interplay between algebraic structures and computational complexity. She also explores applications in molecular biology, particularly protein dynamics in DNA lesions. NSF grants in CCF division (current) Editorial roles at Electronic Journal of Combinatorics, Arnold Mathematical Journal, and others Contributor to the Putnam Mathematical Competition Panova's research is supported by NSF grants, focusing on computational complexity and algebraic combinatorics. She has advised students in areas related to her research, though specific names aren’t listed here. Grants have funded explorations into geometric complexity theory, asymptotic combinatorics, and molecular biology modeling. Her work involves collaborations across disciplines, including statistical mechanics and integrability, as highlighted in her white paper contributions.

Tobias Müllerمشاهده پروفایل

استاد

• Combinatorics
• Probability Theory
• Random Graphs
• +۵ مورد دیگر

Prof. Tobias Müller is a Professor at the Bernoulli Institute for Mathematics, Computer Science and Artificial Intelligence at the University of Groningen. His academic journey includes previous positions at Utrecht University, CWI (Centrum Wiskunde & Informatica), Tel Aviv University, and Eindhoven University of Technology, with a doctorate from the University of Oxford under Colin McDiarmid. His research focuses on combinatorics, probability theory, random graphs, percolation, discrete and stochastic geometry, and combinatorial game theory. He has contributed extensively to understanding complex networks, hyperbolic models, and geometric random structures. Research Interests: Random Graphs and Percolation Theory Discrete and Stochastic Geometry Hyperbolic Network Models Probabilistic Combinatorics Geometric Probability Graph Algorithms and Connectivity Notable Contributions: Analysis of Voronoi and Poisson-Voronoi percolation in hyperbolic planes. Studies on Mallows random permutations and their cycle structures. Research on component games and logical limit laws in graph theory. Investigations into the geometry and properties of random geometric graphs. Grants & Collaborations: Active in organizing workshops and conferences on random graphs and geometric networks, including the BIRS Workshop on Random Geometric Graphs and the STAR Workshops series. Labs/Teams: Member of the Bernoulli Institute’s research groups, focusing on stochastic studies, combinatorics, and algorithmic methods.

Salem Lahlouمشاهده پروفایل

استادیار

• Machine Learning
• Artificial Intelligence
• GFlowNets
• +۷ مورد دیگر

Salem Lahlou is an Assistant Professor in the Machine Learning Department at the Mohamed bin Zayed University of Artificial Intelligence (MBZUAI), having joined in September 2024. He previously served as a Senior Researcher at the Technology Innovation Institute (TII) in 2024. His academic background includes a PhD from Mila and Université de Montréal (UdeM) under Yoshua Bengio (2023), with prior studies in applied mathematics at École Polytechnique and statistical learning at École Normale Supérieure Paris-Saclay. His research focuses on developing more capable and reliable AI systems through three interconnected pillars: Novel Method Development : Core contributions to Generative Flow Networks (GFlowNets), uncertainty estimation techniques (DEUP), and curriculum learning frameworks Large Language Model Advancement : Enhancing reasoning capabilities and alignment through preference optimization and trace-based learning Community Tooling : Creation of torchgfn library for GFlowNets and benchmarks including BabyAI, FinChain, and LLM-BabyBench Core research areas span Machine Learning, GFlowNets, Uncertainty Estimation, LLM Reasoning, Reinforcement Learning, and AI for Science. Recent publications (2023-2025) demonstrate strong emphasis on GFlowNet theory/improvements (8+ papers), LLM reasoning evaluation (FinChain, LLM-BabyBench), uncertainty quantification, and societal AI impacts. Key application domains include mathematical reasoning, financial systems, privacy preservation, and cognitive science. He currently advises graduate students including Junyi (privacy risks in SNNs) and Abhijith (LLM reasoning). His group collaborates with MBZUAI faculty (Nils Lukas, Alham Fikri, Mingming Gong, Martin Takac) and industry partners on projects involving Conversational AI, Personalization, and Affective AI.