جست‌وجوی استاد و پژوهشگر

Alex Kontorovichمشاهده پروفایل

استاد

• Automorphic Forms and Representations
• Homogeneous Dynamics
• Harmonic Analysis
• +۵ مورد دیگر

Alex Kontorovich is a Distinguished Professor of Mathematics at Rutgers University, where he holds the academic rank of Professor. His primary affiliation is with the Department of Mathematics, School of Arts and Sciences. He currently serves as Managing Editor of the Journal of the Association for Mathematical Research and is Executive Director of Rutgers MathCorps . During the 2024-2025 academic year, he is on leave, visiting Princeton University and the Institute for Advanced Study (IAS). Kontorovich's research focuses on automorphic forms, homogeneous dynamics, harmonic analysis, and number theory, with interdisciplinary connections to data science and machine learning. His work bridges pure mathematics with computational aspects, including sphere packing geometry and arithmetic dynamics. He has held distinguished visiting roles, such as the 2020-21 Distinguished Visiting Professor for the Public Dissemination of Mathematics at the National Museum of Mathematics (MoMath), where he contributed to academic content and exhibits. His academic career includes teaching advanced courses like Graduate Complex Analysis, Automorphic Representations, and History of Mathematics. Notable awards include the Simons Foundation Fellowship, von Neumann Fellowship at IAS, and Alfred P. Sloan Research Fellowship. Grants include multiple NSF awards (regular, CAREER, FRG) and a Binational Science Foundation grant. His research explores topics like spectral gaps, thin groups, and applications of modular forms to equidistribution problems. Kontorovich’s scholarly contributions extend beyond academia: he serves on the Scientific Board of Quanta Magazine , advises the Lean Focused Research Organization , and participates in editorial and strategic roles across institutions. His work emphasizes the interplay between theoretical mathematics and computational methods, shaping modern research in number theory and dynamics.

Roie Levinمشاهده پروفایل

استادیار

• Approximation Algorithms
• Online Algorithms
• Submodular Functions
• +۵ مورد دیگر

Roie Levin is an Assistant Professor at Rutgers University's Department of Computer Science. He received his PhD in Algorithms, Combinatorics and Optimization from Carnegie Mellon University in 2022, advised by Anupam Gupta. Prior to that, he worked at the Allen Institute for Artificial Intelligence (2015-2017) and earned dual BSc degrees in Computer Science/Applied Mathematics and Mathematics from Brown University (2015). Before joining Rutgers, he was a Fulbright Postdoctoral Fellow at Tel Aviv University under Niv Buchbinder. Current Role: Assistant Professor in Computer Science Academic Training: PhD (2022) CMU, BSc (2015) Brown University Postdoctoral: Fulbright Fellow at Tel Aviv University Levin's research focuses on approximation algorithms for uncertain environments (online/dynamic/streaming models) and submodular function optimization. His work spans theoretical foundations and practical implementations across distributed systems, geometric constraints, and reinforcement learning paradigms. Teaching includes graduate and undergraduate algorithms courses (CS 344, CS 513) with emphasis on problem-solving techniques, computational complexity, and modern algorithmic trends. His publications showcase expertise in online algorithms, submodular optimization, and approximation theory with applications in clustering, caching, and machine learning. The 2025 articles demonstrate continued exploration of online consistency and contention resolution, while 2023-2024 works focus on submodular optimization under uncertainty and dynamic environments. Earlier publications (2015-2017) cover semantic parsing, geometric approximation, and planar graph optimization. Fulbright Postdoctoral Fellow Levin's research connects theoretical guarantees with practical implementations, bridging classical algorithm design with modern machine learning applications. His recent work explores primal-dual methods in online settings and robust subspace approximation techniques for streaming data environments.

• Combinatorics
• Graph Theory
• Algebraic Combinatorics
• +۴ مورد دیگر

Lluis Vena Cros is a distinguished researcher in the Department of Mathematics at Universitat Politècnica de Catalunya - BarcelonaTech (UPC-BarcelonaTech), affiliated with the Faculty of Mathematics and Statistics. He is a core member of the GAPCOMB research group, which specializes in Geometric, Algebraic and Probabilistic Combinatorics. His research spans multiple domains within combinatorics, with particular emphasis on graph theory, algebraic combinatorics, geometric combinatorics, and probabilistic combinatorics. Vena Cros has made significant theoretical contributions to the understanding of graph homomorphisms, Tutte polynomials, Ramsey theory, and extremal combinatorial structures. His work often explores the deep connections between combinatorial objects and other mathematical disciplines such as topology, algebra, and discrete geometry. An analysis of his recent publications reveals a consistent focus on combinatorial structures with applications across multiple mathematical domains. His work on Tutte polynomials for various graph structures demonstrates a unifying approach to graph invariants, while his research in Ramsey theory explores structural properties of large combinatorial objects. The recurring theme across his publications is the investigation of extremal properties and computational aspects of combinatorial configurations. Vena Cros actively participates in multiple research projects including 'Structure, Randomness and Computational Methods in Extremal Combinatorics,' 'COntemporary COmbinatorics and Applications (COCOA),' and the 'Grup GAPCOMB de la UPC.' These projects involve collaborations with numerous researchers across Spain and internationally, reflecting his significant standing in the combinatorial mathematics community. His work has been supported by various funding bodies including the Spanish State Research Agency and the European Union's Horizon 2020 program. As a member of the GAPCOMB research group, Vena Cros contributes to a vibrant mathematical community focused on advancing combinatorial theory and its applications. The group maintains strong connections with other research centers and regularly participates in international conferences and collaborative projects, fostering the exchange of ideas across the global combinatorial mathematics community.

Luca Vizioliمشاهده پروفایل

دانشیار

• Neuroimaging
• Magnetic Resonance Imaging (MRI)
• Functional MRI (fMRI)
• +۳ مورد دیگر

Luca Vizioli is an Associate Professor in the Department of Radiology at the University of Minnesota, affiliated with the Center for Magnetic Resonance Research. His work focuses on advancing neuroimaging techniques, particularly at ultrahigh magnetic fields (e.g., 10.5T), to enhance spatial and temporal resolution in functional MRI (fMRI). Research interests include RF coil design, noise reduction algorithms (e.g., NORDIC), and applications of deep learning to imaging challenges. Education details are not explicitly stated, though his PhD is noted. His research spans cutting-edge topics like laminar fMRI at UHF, denoising strategies for submillimeter resolution, and the impact of psychedelic substances on brain networks. He collaborates on tools like fMRIPrep Lifespan for developmental neuroimaging preprocessing. Key technical innovations include high-Quality 0.5mm isotropic fMRI leveraging random matrix theory and physics-driven AI, as well as multi-echo distortion correction frameworks. His work bridges engineering (coil design) and neuroscience (cortical processing mechanisms), with implications for understanding both normal brain function and neurological disorders.

Petteri Kaskiمشاهده پروفایل

دانشیار

• Theoretical Computer Science
• Algorithm Design
• Exact and Parameterized Algorithms
• +۵ مورد دیگر

Petteri Kaski is an Associate Professor at the Department of Computer Science, School of Science, Aalto University , and a member of the Helsinki Institute for Information Technology (HIIT) . His research focuses on theoretical computer science, particularly in algorithm design, exact and parameterized algorithms, algebraic algorithms, and combinatorics. Doctoral Degree in Engineering and Technology, Helsinki University of Technology (2005) Licentiate Degree in Engineering and Technology, Helsinki University of Technology (2002) Master's Degree in Engineering and Technology, Helsinki University of Technology (2001) His recent work explores tensor scaling, Johnson-Lindenstrauss transforms, Hamiltonian cycles, and computational complexity, with contributions to polynomial-time algorithms, finite field computations, and combinatorial optimization. Notable awards include the Best Paper Award at ICALP 2017 , an ERC Starting Grant (2014) , and the Kirkman Medal (2007) . He has served on scientific committees for conferences like STACS 2025 and ICALP 2024 , and collaborated with institutions such as the IT University of Copenhagen and Universität Regensburg . Key Research Areas : Theoretical Computer Science, Algorithm Design, Exact Algorithms, Algebraic Computation, Graph Theory, Combinatorics

Michael Kozdronمشاهده پروفایل

دانشیار

• Probability Theory
• Stochastic Processes
• Schramm-Loewner Evolution
• +۴ مورد دیگر

Michael Kozdron is an Associate Professor in the Department of Mathematics & Statistics at the University of Regina , with research focused on probability theory and stochastic processes. He holds a Ph.D. from Duke University (2004) and a B.Sc. from the University of British Columbia (1998). Education : B.Sc. (1998) - University of British Columbia Ph.D. (2004) - Duke University Research Interests : Probability theory and stochastic processes Schramm-Loewner evolution (SLE) Statistical mechanics Random walks and quantum probability Key Article Trends : 2022: Quantum martingales and convergence theorems 2016: Quantum random variables and spectral analysis 2013: SLE convergence rates and loop-erased walk dynamics 2012: Radial SLE Green's functions and quantum conditional expectation 2009-2007: SLE applications in Ising models and Fomin's identity 2005-2004: Excursion measures and planar random walk scaling Additional Activities : He supervises undergraduate and graduate students, maintains a Google Scholar profile , and shares simulations of random processes and lecture notes online.

Gregory Markowskyمشاهده پروفایل

مدرس ارشد

• Stochastic processes
• Complex analysis
• Graph theory

Gregory Markowsky serves as a Senior Lecturer in the School of Mathematics at Monash University, maintaining an active research profile with continuous publication output through 2025. His academic appointment shows no indication of part-time status, emeritus designation, or former staff classification. His research centers on three interconnected domains: Stochastic processes, particularly planar Brownian motion and random walk theory Complex analysis with applications to probabilistic models Graph theory focusing on algebraic and combinatorial structures These areas converge in his investigation of probabilistic phenomena with geometric interpretations. Recent publication trends reveal sustained productivity with 61 research outputs cataloged, including 4 articles in 2025 alone. His work demonstrates consistent methodological focus on stochastic analysis and combinatorial structures, with increasing cross-disciplinary applications in forensic science and econometrics as evidenced by 2024-2025 publications. Markowsky has secured three significant research projects funded by the Australian Research Council, including: "Planar Brownian motion and complex analysis" (2014-2017) as Primary Chief Investigator "Finite Markov chains in statistical mechanics and combinatorics" (2014-2017) "AMSI Industry Internship: Melbourne Storm" (2013) His ORCID profile (0000-0003-1656-337X) documents extensive scholarly activity with international collaborations. Professional engagement includes contributions toward UN Sustainable Development Goals through mathematical applications, though specific goal alignments aren't detailed in available materials. His email contact Greg.Markowsky@monash.edu remains active with current research dissemination.

• Probability Theory
• Statistical Mechanics
• Mathematical Physics
• +۸ مورد دیگر

Nathanael Berestycki is a University Professor (Univ.-Prof.) in the Department of Mathematics within the Faculty of Mathematics. His research spans multiple areas of probability theory and mathematical physics with a particular focus on stochastic processes and their connections to statistical mechanics and quantum gravity. Professor Berestycki's research interests center around probability theory, with significant contributions to the understanding of Gaussian free fields, Liouville quantum gravity, and random processes. His work explores deep connections between statistical mechanics and mathematical physics, particularly in the context of conformal invariance and scaling limits. He has made substantial contributions to the theory of dimers, Brownian motion, random graphs, and branching processes, often revealing unexpected connections between seemingly disparate areas of mathematics. Analysis of his recent publications shows a consistent focus on the intersection of probability theory and mathematical physics, with particular emphasis on conformal invariance properties, scaling limits of discrete models, and the mathematical structure of quantum gravity. His work often bridges theoretical developments with concrete applications in statistical mechanics, demonstrating how deep mathematical structures emerge from seemingly simple probabilistic models. The breadth of his research is reflected in publications across top probability and mathematical physics journals. Throughout his career, Berestycki has maintained a high level of productivity with consistent publication output across leading journals in probability theory and mathematical physics. His collaborative work with researchers across institutions demonstrates strong connections within the mathematical community, particularly in the European probability research network.

Jan Swartمشاهده پروفایل

استاد

• Probability Theory
• Mathematical Statistics
• Interacting Particle Systems
• +۵ مورد دیگر

Dr. Jan Swart is a Professor at the Department of Probability and Mathematical Statistics within the Faculty of Mathematics and Physics at Charles University, and a researcher at the Institute of Information Theory and Automation (UTIA) of The Czech Academy of Sciences in Prague. His office is located at Pod vodarenskou vezi 4, 18200 Praha 8, Czech Republic, where he works in Room 112 of the Stochastic Informatics department. Professor Swart specializes in probability theory with particular expertise in interacting particle systems, stochastic processes, and their mathematical foundations. His research spans quantum probability, random matrix theory, Markov chains, large deviations, and Brownian continuum objects. He has developed significant theoretical frameworks for understanding complex stochastic systems and their scaling limits. His publication record shows a consistent focus on advancing the mathematical understanding of interacting systems, with lecture notes and research materials that have become valuable resources in the field. His work demonstrates strong connections between theoretical probability and applications in statistical physics and theoretical biology. Professor Swart has supervised numerous theses and taught a wide range of advanced courses including Quantum Probability Theory, Interacting Particle Systems, Random Matrix Theory, and Advanced Markov Chains. He has also created a comprehensive simulation library for interacting particle systems that enables numerical exploration of these complex models. Notably, Professor Swart has also contributed to mathematical education through creative works including a mathematical fairy tale titled "Roulette a la princess" that explores game theory and probability concepts in an engaging narrative format. His work on the Czech language demonstrates his integration into the local academic community while maintaining international scholarly connections.

Alexandre Staufferمشاهده پروفایل

استاد

• Discrete Probability
• Interacting Particle Systems
• Markov Chain Mixing Times
• +۳ مورد دیگر

Alexandre Stauffer is a Professor in Probability at the Department of Mathematics, King's College London. He holds a PhD from the University of California, Berkeley (2011), with postdoctoral positions at Microsoft Research and Università Roma Tre. He previously worked at the University of Bath (2013–2024) before joining King’s in 2024. His research focuses on discrete probability, interacting particle systems, and Markov chain mixing times. Key areas include first passage percolation, random walks, and phase transitions in stochastic systems. Stauffer has organized workshops on topics like strongly correlated random processes and random interacting systems. He secured grants such as the EPSRC Early Career Fellowship (2016–2022) and the Marie Curie Career Integration Grant (2013–2016). His work bridges theoretical probability with applications in statistical physics and computer science. Recent articles explore coexistence in competing percolation models and mixing times in dynamic systems. Stauffer’s research emphasizes multi-scale analysis and non-equilibrium phenomena in stochastic processes. Grants and organizational involvement highlight his leadership in probability theory. His publications span top journals like the Annals of Probability and Inventiones Mathematicae, reflecting rigorous mathematical contributions to understanding complex stochastic systems.

Nicolai Haydnمشاهده پروفایل

استاد

• Ergodic Theory
• Dynamical Systems
• Limit Theorems
• +۳ مورد دیگر

Nicolai Haydn is a Professor of Mathematics at the University of Southern California (USC), affiliated with the Department of Mathematics in the Dornsife College of Letters, Arts and Sciences. He has held academic positions at USC since 1990, progressing from Assistant Professor (1990–1994), to Associate Professor (1994–2002), and Professor since 2002. He earned his Ph.D. in Mathematics from the University of Warwick in 1987. His research focuses on ergodic theory , dynamical systems , and limit theorems , with particular emphasis on entry and return times distribution, escape rates, and random dynamical systems. His work explores both theoretical and applied aspects of these fields, including applications to statistical mechanics and stochastic processes. Recent publications (2020–2025) highlight advancements in quenched limiting entry distributions , number of visits in ϕ-mixing dynamics , and escape rate analysis . These studies contribute to understanding statistical behaviors in complex dynamical systems. Notable collaborations include work with researchers like Sandro Vaienti (University of Toulon) and Matthew Nicol (University of Houston). Teaching responsibilities include advanced courses such as Math 625: Ergodic Theory and Dynamical Systems and Math 520: Complex Analysis . He has also contributed to educational materials, including lecture notes on entry/return times distributions. No academic awards or grants are explicitly listed in the provided texts, though his extensive publication record reflects sustained scholarly impact. Research is centered at USC, with no mention of lab affiliations or team collaborations beyond co-authorships.

Avelio Sepúlvedaمشاهده پروفایل

استادیار

• Probability Theory
• Statistical Physics
• Random Geometry
• +۵ مورد دیگر

Avelio Sepúlveda is an Assistant Professor in the Department of Mathematics at Universidad de Chile, where he has been affiliated since 2021. He previously held a Chair CMM–CNRS fellowship (2020–2021) and completed postdoctoral work at Université Lyon 1 (2017–2020). His education includes a PhD from ETH Zürich (supervised by Wendelin Werner), a Master's from Université d'Orsay, and an Engineering degree from Universidad de Chile. His research centers on probability theory and statistical physics , with emphases on Gaussian free fields, random planar maps, percolation theory, and topological phase transitions. Key investigations include the structure of 2D Gaussian free fields, scaling limits of decorated planar maps, and dynamic conditioning of Markov processes ( myopic conditioning ). His publications (2020–2025) predominantly explore probabilistic geometry, phase transitions, and field theory, with recurring themes in conformal invariance, scaling limits, and lattice models. Notable methodological contributions include novel algorithms for myopic processes and combinatorial characterizations of Markov properties in planar maps. Awards & Service: Chair CMM–CNRS of Excellence for Young Researchers (2020–2021) Associate Editor: Electronic Journal of Probability and Electronic Communications in Probability Advising & Collaborations: He mentors six PhD/Master's students (Pablo Araya, Paul Cahen, Damian Cid, Felipe Espinosa, Pablo Zúñiga, Tomás Laengle) and collaborates extensively with researchers globally, including Christophe Garban (ERC Vortex). He co-organizes the Seminario de Probabilidades de Chile and workshops (e.g., 2025 Topological Phase Transition workshop).

• Combinatorics
• Discrete Mathematics
• Random Graphs
• +۴ مورد دیگر

Juan José Rué Perna is an Associate Professor in the Department of Mathematics at Universitat Politècnica de Catalunya (UPC), where he serves in the Faculty of Mathematics and Statistics. He also holds the position of Director of CFIS (Centro de Formación Interdisciplinaria Superior) and maintains strong affiliation with the Centre de Recerca Matemàtica (CRM). His academic career spans prestigious institutions across Europe, including Freie Universität Berlin, CSIC-ICMAT in Madrid, and École Polytechnique in Paris. Licenciado en Matemáticas (5-year degree) Engineering degree in Telecommunications (5-year program) PhD in Applied Mathematics (2009), supervised by Professor Marc Noy Rué's research focuses on combinatorics and discrete mathematics, with expertise in combinatorial structures, random graphs, and enumerative problems. His work bridges theoretical mathematics with applications in computer science, particularly in structural graph theory and probabilistic methods. He has made significant contributions to the understanding of planar graphs, random discrete structures, and additive combinatorics problems. His research combines analytic techniques with combinatorial reasoning to solve challenging enumeration and structural problems. His recent publications demonstrate a consistent trajectory in combinatorial mathematics, with emphasis on planar structures, random discrete objects, and additive problems. The works span theoretical investigations of graph structures, enumeration techniques, and probabilistic approaches to combinatorial problems. His research group GAPCOMB (Geometric, Algebraic and Probabilistic Combinatorics) at UPC continues to produce influential work in these areas. Premi Albert Dou de la SCM Rué has successfully supervised multiple PhD students to completion, including Clément Requilé, Christoph Spiegel, Vasiliki Velona, and Maximilian Wötzel. His research has been supported through numerous competitive grants, including ERC projects, ExploreMaps, and participation in the Berlin Mathematical School. He has organized significant academic events such as EUROCOMB 2021 and various workshops on combinatorics and discrete mathematics. As leader of the GAPCOMB research group at UPC, Rué fosters collaboration between geometric, algebraic, and probabilistic approaches to combinatorial problems. The group maintains strong connections with international research centers including ICMAT in Madrid and institutions across Europe, contributing to the vibrant combinatorial research community in Spain and internationally.

Tim Seppeltمشاهده پروفایل

پژوهشگر

• Graph Theory
• Theoretical Computer Science
• Algorithmic Complexity
• +۳ مورد دیگر

Dr. Tim Seppelt is a postdoctoral researcher at the IT University of Copenhagen , working under the mentorship of Prof. Radu Curticapean. Previously, he earned his PhD from RWTH Aachen University with supervisors Prof. Martin Grohe and Prof. Michael Schaub. His research focuses on theoretical computer science, specifically homomorphism indistinguishability , a framework connecting graph isomorphism, quantum information, and logical equivalences. Current Role: Postdoc in Theoretical Computer Science, ITU Education: PhD in Computer Science, RWTH Aachen University Tim's work addresses algorithmic meta-theorems for homomorphism indistinguishability over minor-closed and treewidth-bounded graph classes. He has extended Lovász-type results to CMSO2 logic, resolved complexity conjectures for the Lasserre hierarchy, and classified quantum group-induced indistinguishability relations. His research spans quantum computing , graph algorithms , and descriptive complexity , often intersecting with applications in machine learning and finite model theory. Recent publications include a 2025 paper on quantum group-driven homomorphism indistinguishability and a 2024 journal article on logical equivalences and forbidden minors. He presented at workshops like the Graph Learning Meets TCS (Simons Institute, 2025) and delivered tutorials on finite model theory at Finite and Algorithmic Model Theory 2025 (Les Houches, France).

• Probability
• Random Trees
• Random Graphs
• +۳ مورد دیگر

Bénédicte Haas is a Professor in the Department of Mathematics at Sorbonne Paris Nord University, affiliated with the Galileo Institute and LAGA laboratory (Laboratoire Analyse, Géométrie et Applications). Her research focuses on probabilistic structures including random trees, fragmentation processes, and self-similar Markov processes, with applications spanning mathematical physics and combinatorial theory. Her academic credentials include a PhD (2005) on Fragmentation and mass loss and an Habilitation à diriger les recherches (2010) on Random trees and fragmentations , both from Sorbonne Paris Nord University. These qualifications established her expertise in stochastic processes and tree-valued structures. Haas investigates scaling limits , asymptotic behaviors , and dust formation in fragmentation models, with recent work extending to stable graphs and multi-type branching structures. Her research bridges theoretical probability with concrete applications in evolutionary biology and statistical physics, characterized by rigorous mathematical analysis of discrete-to-continuum transitions. Publication trends (2010-2023) reveal sustained focus on fragmentation kinetics and tree models, evolving toward tail asymptotics and complex graph structures. Key themes include extinction time analysis, line-breaking constructions, and multi-type branching dynamics, demonstrating increasing mathematical sophistication while maintaining core probabilistic foundations. prix des Annales de l'IHP Proba. Stat. 2010 (for work on self-similar fragmentations) As an Associate Editor for ESAIM Probability and Statistics and member of the ANR GRAAL project (GRaphes and Random Trees), Haas contributes to collaborative research networks. Her teaching spans undergraduate algebra/probability courses to graduate seminars on random trees, reflecting comprehensive pedagogical engagement. Current grants support interdisciplinary work on graph-theoretic approaches to stochastic processes. Within LAGA's Probability and Statistics Team, Haas co-leads research on continuum random trees and fragmentation kinetics, maintaining active collaboration with international probabilists through the ANR GRAAL consortium and editorial responsibilities.