جستجوی استاد راهنمای دانشگاهی

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.

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

Riddhipratim Basu is an Associate Professor at the International Centre for Theoretical Sciences (ICTS-TIFR) in Bengaluru, India, since September 2017. Previously, he was a Szegö Assistant Professor of Mathematics at Stanford University (2015–2017) and a Ph.D. graduate in Statistics from UC Berkeley (2015), supervised by Allan Sly. Research focuses on Probability Theory, with emphasis on First/Last Passage Percolation, Interacting Particle Systems, Large Deviations, and Random Matrix Theory. Key collaborators include Allan Sly, Shirshendu Ganguly, Mahan Mj, and Manan Bhatia. Publications span journals like Communications on Pure and Applied Mathematics , Annals of Probability , and Comm. Math. Phys. His work explores geodesic structures in percolation models, scaling exponents in KPZ universality, and geometric properties of stochastic processes. Recent studies include Liouville Quantum Gravity and Airy process fluctuations.

• Probability Theory
• First Passage Percolation
• Last Passage Percolation
• +۵ مورد دیگر

Sebastian Andres is Professor of Stochastics at TU Braunschweig's Institute of Mathematical Stochastics. His research focuses on random processes in disordered media, including homogenization of random walks, percolation theory, and heat kernel analysis. He holds a habilitation from University of Bonn and previously held positions at University of Cambridge and University of Manchester. Research Themes: Quantitative homogenization, invariance principles for degenerate random walks, and applications to mathematical physics. Teaching: Current courses include Probability Theory and Discrete Financial Mathematics; authored lecture notes on Martingale Theory for Finance.

• Stochastic Analysis
• Random Walks
• Percolation Theory
• +۱ مورد دیگر

Prof. Dr. Matthias Meiners is a faculty member at Justus-Liebig-University Giessen, holding the Chair of Stochastics within the Mathematical Institute. His research focuses on Asymptotic statistics Branching processes Limit theorems Random walks in random environments Regenerative processes Renewal theory Statistical mechanics . Recent publications analyze complex stochastic phenomena across branching processes, random walks, and renewal equations, with 2025 preprints on CMJ explosions and Markov renewal expansions. 2024 works examine active Brownian particle dynamics and supercritical branching fluctuations. Earlier studies address kinetic equations, martingale convergence, and percolation model speeds. Contact: Email: Matthias.Meiners@math.uni-giessen.de Phone: 0641 99-32100 Office: Room 216, Arndtstr. 2, 35392 Gießen

• Asymptotic statistics
• Branching processes
• Limit theorems
• +۴ مورد دیگر

Artem Sapozhnikov is a Professor at the Mathematisches Institut of Universität Leipzig, Germany. His research focuses on probability theory, particularly percolation, random walks, and random graphs. He maintains an active research program with numerous publications and supervises PhD students and postdocs. Professor Sapozhnikov teaches a range of mathematics courses for physicists, including Mathematics for Physicists 1-4, Probability Theory I & II, and specialized courses on percolation. His teaching spans both undergraduate and graduate levels, demonstrating his commitment to mathematical education. His research interests center around probability theory , with specific focus on: Percolation theory and phase transitions Random walks and their properties on various structures Random interlacements and Brownian motion Boolean models and geometric probability Models with long-range correlations His work bridges theoretical probability with applications in statistical physics. Analysis of his recent publications shows a strong focus on the properties of random interlacements, Brownian motion, and percolation models. His research examines connectivity properties, phase transitions, and geometric aspects of these stochastic processes. Recent work with Yingxin Mu has explored visibility windows and indistinguishability of components in various models. Earlier collaborations with Caio Alves, Deepan Basu, and Yinshan Chang investigated decoupling inequalities, crossing probabilities, and loop percolation. Professor Sapozhnikov has supervised several PhD students and postdocs, including: Deepan Basu (PhD 2013-2017) Caio Alves (postdoc 2016, 2017-2020) Yinshan Chang (postdoc 2013-2016) Lorenzo Taggi (PhD 2012-2015) Yingxin Mu (postdoc 2021-2023) He currently has open positions for PhD students through the IMPRS program.

• Probability theory
• percolation
• random walks
• +۱ مورد دیگر

Erika Roldán Roa is a mathematician with academic affiliations at Ohio State University (Visiting Assistant Professor, 2019-2020) and Technische Universität München (Marie Skłodowska-Curie Fellow, 2020-2022). She holds a Ph.D. in Probability and Statistics from CIMAT (2018) and has conducted postdoctoral research at institutions like EPFL and Ohio State. Her work bridges stochastic topology, combinatorics, and educational technology, focusing on topics like cubical sliding puzzles, Eden growth models, and extremal topological problems in polyominoes. Education: Ph.D. in Probability and Statistics, CIMAT (2018) M.Sc. in Mathematics, CIMAT (2014) B.Sc. in Mathematics, Universidad de Guanajuato (2010) Research Interests: Her research spans stochastic topology, extremal combinatorics, and applications of discrete geometry to puzzles and games. She explores configuration spaces, topological properties of random structures, and innovative educational approaches like the Music Math Project, which integrates music and mathematics. Awards: Includes the Marie Skłodowska-Curie Fellowship (2020-2022), Arana-Ordaz Prize (2019), and Sofia Kovalevskaia Prize (2019). She has also received grants from CONACYT and CIMAT. Grants & Outreach: Developed workshops in marginalized communities using music and math, and contributed to software tools like the Eden Model analysis package. Active in science popularization through articles and outreach programs. Labs/Teams: Collaborated on projects like the Eden Model (topology analysis software) and extremal animal configurations, often involving interdisciplinary teams in mathematics and computer science.

• Stochastic Topology
• First Passage Percolation
• Extremal Topological Combinatorics
• +۳ مورد دیگر

Prof. Dr. Benedikt Jahnel is a Professor at the Institute of Mathematical Stochastics, Carl Friedrich Gauss Faculty, Technical University of Braunschweig, and Head of the Leibniz Junior Research Group on Probabilistic Methods for Dynamic Communication Networks. His research focuses on the modeling and analysis of spatially embedded systems with interacting random components, with applications in physics, epidemiology, and telecommunications. Education: PhD in Organismic and Evolutionary Biology from Ruhr University Bochum (2011), Diploma from Technical University of Berlin. Research interests: Uses tools from statistical mechanics and stochastic geometry to study phase transitions, percolation theory, and spatial random processes. Current projects investigate continuum percolation, interacting particle systems, and probabilistic methods for communication networks. Recent publications demonstrate strong emphasis on spatial stochastic processes and percolation theory, with applications to network connectivity and epidemic modeling. Trends include mathematical analysis of phase transitions in random environments and dynamics of communication networks. Scientific awards include leadership of Leibniz Junior Research Group, EURANDOM Ambassador appointment, and election to the board of the Probability and Statistics Group of the German Mathematical Society. Leads a research team including 3 postdocs and 3 predoctoral researchers. Current projects funded by DFG, ERC, and Math+ Cluster of Excellence. Heads the DYCOMNET research group at Weierstrass Institute Berlin, focusing on dynamic spatial random systems.

• Statistical Mechanics
• Stochastic Geometry
• Spatial Epidemiology
• +۳ مورد دیگر

Leon Bungert is a W2 Professor with Tenure Track to W3 Professorship for Mathematics III (Mathematics of Machine Learning) at the University of Würzburg, appointed since 2023. He leads the Mathematics of Machine Learning group within the Institute of Mathematics. His academic journey includes a Ph.D. (summa cum laude) from the University of Erlangen-Nürnberg in 2020, focusing on nonlinear spectral theory with variational methods, followed by postdoctoral research at TU Berlin, Bonn, and Erlangen-Nürnberg. Research Interests: Bungert's work bridges applied analysis and machine learning, emphasizing PDEs on graphs, adversarial robustness, inverse problems, optimization, and nonlinear eigenvalue problems. His research often employs variational methods and explores the intersection of geometry, probability, and numerical analysis. Notable areas include adversarial machine learning, Lipschitz learning on graphs, and regularization techniques for inverse problems. Professional Activities: He serves as a guest editor for the European Journal of Applied Mathematics , associate editor for Advances in Continuous and Discrete Models , and co-organizes major conferences like MIA'25 and SSVM 2025. His work has been published in top journals such as Annals of Applied Probability , Journal de Mathématiques Pures et Appliquées , and SIAM Journal on Imaging Sciences . Upcoming Engagements: Invited speaker and organizer at events including the IHP Paris conference (January 2025), Osaka workshop on machine learning and numerics (March 2025), and the BIRS workshop on adversarial machine learning (August 2025). His research team collaborates with institutions globally, including MIT, University of Cambridge, and University of Bonn. Lab/Team: The Mathematics of Machine Learning group at Würzburg focuses on theoretical and applied aspects of machine learning, with active projects in adversarial robustness, PDE-based optimization, and geometric data analysis. Bungert’s team includes researchers like Eloi Martinet and Tim Roith, advancing interdisciplinary work in mathematical foundations of AI.

• Machine Learning
• Applied Mathematics
• Partial Differential Equations
• +۵ مورد دیگر

Prof. Dr. Matthias Schulte is a Professor of Stochastics at Hamburg University of Technology (TUHH), leading the Chair of Stochastics since September 2020. Previously, he held roles as Associate Professor at Heriot-Watt University (2020), Senior Assistant at University of Bern (2016–2020), and Research Associate at Karlsruhe Institute of Technology (2013–2016). He earned his PhD in Mathematics from the University of Osnabrück in 2013 and completed his Habilitation in Stochastics at the University of Bern in 2019. His research focuses on stochastic geometry, limit theorems, random graphs, and extreme value theory. Key areas include Boolean models, Malliavin-Stein methods, random geometric graphs, and Poisson process approximations. He has contributed extensively to stochastic analysis, spatial statistics, and Gaussian processes. Recent publications highlight advancements in hyperbolic stochastic geometry, moderate deviations on Poisson chaos, and algorithmic generation of hypergraphs. His work bridges theoretical probability with applications in geometric modeling and complex network analysis. Education: PhD (2013), Habilitation (2019), Diploma in Business Mathematics (2010) Affiliations: TUHH, University of Bern, Karlsruhe Institute of Technology Teaching: Courses in stochastic processes, stochastic geometry, and mathematical statistics Grants and collaborations: Active in GAMM Activity Group on Applied Operator Theory and SIAM Chapter Hamburg

• Stochastic Geometry
• Limit Theorems and Stein's Method
• Random Graphs
• +۵ مورد دیگر

Prof. Dr. Abhinav Sharma is a faculty member at the Leibniz Institute for Polymer Research Dresden, working in the Department of Soft Matter Theory and Polymer Physics. His research program investigates fundamental principles of soft matter systems with significant biological relevance, employing advanced theoretical and computational approaches. Dr. Sharma's research focuses on three interconnected domains: Biopolymer Networks Mechanics - Studying how disordered elastic networks in biological systems (like cytoskeletons and extracellular matrices) exhibit nonlinear mechanical responses including strain stiffening and phase transitions from floppy to rigid states Active Matter - Investigating systems where individual components consume energy to generate motion (molecular motors, bacteria with flagella), with particular interest in emergent phenomena like chemotaxis in cargo-carrying particle systems Odd-Diffusive Systems - Exploring diffusion processes with broken time-reversal symmetry where particles exhibit unusual "rolling past" collision dynamics that enhance spatial exploration His methodological toolkit combines classical statistical mechanics with computational techniques including Brownian Dynamics and Monte Carlo simulations, along with theoretical frameworks from liquid-state theory such as density functional theory and mode-coupling theory. Dr. Sharma's publication record from 2010-2021 reveals a consistent research trajectory focused on non-equilibrium statistical mechanics of soft materials. His work demonstrates increasing sophistication in connecting microscopic dynamics to macroscopic properties, with notable contributions in developing scaling theories for fiber network mechanics and explaining chemotactic behavior in active matter systems. Though specific advisory roles aren't detailed in available materials, his collaborative publication pattern suggests active mentorship of junior researchers. His research has significant implications for understanding biological materials and developing new active matter technologies, bridging fundamental physics with potential biomedical applications.

• Biopolymer Networks Mechanics
• Active Matter
• Odd-Diffusive Systems
• +۳ مورد دیگر

Prof. Dr. Chiranjib Mukherjee is a Professor of Mathematics at the University of Münster, affiliated with the Institute of Mathematical Stochastics. He holds a leadership role as an Investigator in Mathematics Münster and contributes to the Collaborative Research Center (CRC) projects on Groups, Geometry & Actions. His research focuses on stochastic analysis, large deviations, stochastic PDEs, and applications in statistical mechanics. Education and Academic Background: While specific degree details are not explicitly listed, his academic trajectory includes postdoctoral work at institutions like the Courant Institute (NYU) and his current role as a full professor indicates advanced academic qualifications. Research Interests: His work bridges probability theory and mathematical physics, addressing topics like directed polymers, stochastic homogenization, percolation, and geometric group theory. Recent projects include studies on polaron measures, Gaussian multiplicative chaos, and the interplay between stochastic processes and geometric structures. Publications: Over 30 peer-reviewed articles since 2016, appearing in journals such as Annals of Probability , Communications on Pure and Applied Mathematics , and Probability Theory and Related Fields . Key contributions address the effective mass of polarons, large deviation principles for random walks, and SPDEs in disordered media. Teaching & Supervision: Supervised over 10 master's and bachelor's theses on topics like Liouville first passage percolation, electrical networks, and machine learning applications. Teaches advanced courses on stochastic analysis, Markov processes, and probability theory on groups/networks. Labs/Teams: Leads the research group in stochastic analysis at the Institute of Mathematical Stochastics, collaborating with postdocs like Konstantin Recke and PhD students such as Luzie Kupffer. Active in organizing conferences on probability, dynamics, and group theory.

• Stochastic Analysis
• Large Deviations
• Stochastic Partial Differential Equations (SPDEs)
• +۵ مورد دیگر

Lisa Hartung is a Full Professor (W3) at Johannes Gutenberg University Mainz's Department of Mathematics since 2025, leading research in stochastic processes. She previously held Associate (2019-2025) and Assistant Professor (2019) positions at the same institution and served as a Courant Instructor at NYU's Courant Institute (2016-2018). Her educational background includes a PhD from Bonn University (2016) under Prof. Anton Bovier in Probability Theory. Key research interests span branching Brownian motion , Gaussian free fields , random energy models , and stochastic processes on evolving networks , with applications in statistical mechanics and mathematical physics. Her work frequently addresses extremal processes, phase transitions, and complex temperature phenomena. Analysis of her recent publications reveals dominant trends in log-correlated random fields, variable-speed branching processes, and hard-wall boundary conditions. Articles consistently explore asymptotic behavior, maximum processes, and structural properties of stochastic systems, with increasing interdisciplinary connections to computer science through data stream modeling. Scientific recognition includes: Förderpreis der Fachgruppe Stochastik (2018) Hartung secures substantial research funding as PI for multiple high-impact projects: the ANR-DFG grant Random Energy Models (2020), DFG's SPP 2265 Random Geometric Systems (2020), TRR146 Multiscale Simulation Methods (2020), and Carl-Zeiss project TOPML (2022). She also serves on the executive board of DMV Fachgruppe Stochastik (since 2023) and coordinates Jugend trainiert Mathematik for grades 8/9. Her research group participates in the DFG-funded network Stochastic Processes on Evolving Networks and collaborates internationally through workshops on interacting particle systems and stochastic population models.

• Probability Theory
• Stochastic Processes
• Mathematical Physics
• +۳ مورد دیگر