جست‌وجوی استاد و پژوهشگر

Matthias Schulteمشاهده پروفایل

استاد

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

Marcus Michelenمشاهده پروفایل

استادیار

• Probability
• Combinatorics
• Statistical Physics
• +۷ مورد دیگر

Marcus Michelen is an Assistant Professor in the Department of Mathematics, Statistics, and Computer Science at the University of Illinois, Chicago (UIC), within the College of Liberal Arts and Sciences. He earned his Ph.D. in Mathematics from the University of Pennsylvania in 2019 under Robin Pemantle and completed his undergraduate studies at The Cooper Union with a B.S.E. in General Engineering and a minor in Mathematics. He began at UIC as a postdoc in 2019 and transitioned to Assistant Professor in 2021 through the Bridge-to-Faculty program. His research lies at the intersection of probability, combinatorics, statistical physics, and theoretical computer science , with recent work focusing on random matrices, random polynomials, sphere packing, and Gibbs point processes. He frequently uses complex- and Fourier-analytic techniques in discrete settings. His research is supported by an NSF CAREER grant (DMS-2336788) and other NSF awards. His most recent publications explore topics such as lower bounds in Ramsey theory, root separation in random polynomials, gossip propagation via percolation, and limits in sphere packing. These works appear in top journals like the Journal of the American Mathematical Society , Probability Theory and Related Fields , and Combinatorics, Probability and Computing , reflecting a strong trend toward probabilistic methods in combinatorial and geometric problems. NSF CAREER Grant DMS-2336788 Penn Prize for Excellence in Teaching by Graduate Students (2016) Herbert S. Wilf Memorial Award University of Pennsylvania Math Department Good Teaching Awards (2015, 2016) Marcus advises one master’s student and three Ph.D. students and has mentored undergraduate research in mathematics and origami. He has taught courses ranging from Calculus III to graduate-level combinatorics and probability. He was featured in the 2024 Lathisms Calendar and has contributed to public understanding of mathematics through Quanta Magazine and La Recherche .

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

Tom Albertsمشاهده پروفایل

دانشیار

• Probability Theory
• Stochastic Analysis
• 2D Conformally Invariant Systems
• +۴ مورد دیگر

Tom Alberts is an Associate Professor in the Department of Mathematics at the University of Utah, where he has been a faculty member since 2013, achieving the rank of Associate Professor in 2020. His research focuses on probability theory, particularly two-dimensional conformally invariant systems and Schramm-Loewner Evolution (SLE). His educational background includes: PhD in Mathematics (2008) from the Courant Institute of Mathematical Sciences at New York University BS in Mathematics (2002) from the University of Alberta Professor Alberts' research spans multiple areas within probability theory and statistical mechanics. His primary focus is on two-dimensional conformally invariant systems, which can be understood as the natural extension of one-dimensional random paths to two-dimensional random surfaces. This work has potential applications across various fields including physics, finance, and artificial intelligence. He also maintains active research in statistical mechanics, random walks in random environments, directed polymer models, last passage percolation, and random matrix theory. His work often bridges theoretical mathematics with physical applications, demonstrating how abstract probability concepts can illuminate real-world phenomena. His recent publications (2022-2025) demonstrate a sophisticated evolution of his research program, moving from foundational work on SLE boundary behavior to more complex explorations of conformal field theory in multiply connected domains. The trajectory shows increasing mathematical sophistication while maintaining connections to physical systems. His work on directed polymers and last passage percolation reveals deep connections to the Kardar-Parisi-Zhang universality class, which describes interface growth in diverse physical systems. Professor Alberts has presented his research at prestigious venues including the Fields Institute, Mathematical Sciences Research Institute, Institut Mittag-Leffler, and Korean Institute of Advanced Study. His recent talks have focused on conformal field theory in multiply connected domains and the interplay between random geometry and conformal field theory, indicating where his research program is heading. His ORCID identifier is 0000-0002-1696-853X, reflecting his established scholarly presence.

Allan Slyمشاهده پروفایل

استاد

• Probability
• Statistical Mechanics
• Stochastic Processes
• +۳ مورد دیگر

Allan Sly is the Anthony H.P. Lee '79 P11 P14 Professor of Mathematics at Princeton University, affiliated with the Department of Mathematics at Fine Hall. His research focuses on probability theory, including statistical mechanics, stochastic processes, and random graphs. He is recognized for contributions to phase transitions, percolation, and interacting particle systems. Notable awards include the Wolfgang Doeblin Prize (2016). His work often addresses foundational questions in probability, such as mixing times of Markov chains, critical phenomena in statistical physics, and reconstruction problems on trees. Recent research explores epidemic models, TASEP dynamics, and constraint satisfaction problems. Sly has contributed to the theoretical foundations of algorithms for community detection in networks and the study of random constraint satisfaction thresholds. His research spans interdisciplinary topics, including applications of probability to computer science and biology. Grants include NSF CAREER awards for stochastic processes on networks. Collaborations often involve analyzing complex systems through rigorous probabilistic methods.

Arjun Krishnanمشاهده پروفایل

استادیار

• First-Passage Percolation
• Stochastic Homogenization
• Directed Polymers
• +۵ مورد دیگر

Arjun Krishnan is an Assistant Professor of Mathematics at the University of Rochester, within the School of Arts & Sciences. His research focuses on stochastic processes and mathematical physics, particularly in first-passage percolation, KPZ universality, and directed polymers. He holds a PhD from NYU (2014) and a Master's from the University of Michigan (2008). His work integrates probabilistic methods with geometric and dynamical systems analysis, addressing questions in stochastic homogenization, geodesic dynamics, and concentration inequalities. Key contributions include variational formulas for time constants in percolation models and studies of Tracy-Widom fluctuations in polymer systems. Publications span topics from motor protein dynamics (2006-2011) to modern advancements in first-passage percolation theory (2013–2022), reflecting a trajectory from applied fluid mechanics to abstract probability theory. His research bridges theoretical mathematics with applications in statistical physics and biological systems. Dr. Krishnan’s academic profile includes a 2007 study on unsteady flow dynamics and early work on mechano-chemical models of motor proteins, demonstrating interdisciplinary engagement. Office hours are held by appointment in Hylan Building 817.

Yuri Bakhtinمشاهده پروفایل

استاد

• Random dynamics
• Probabilistic models of mathematical physics
• Stochastic processes
• +۳ مورد دیگر

Yuri Bakhtin is a Professor of Mathematics at the Courant Institute of Mathematical Sciences, New York University. He holds a Ph.D. from Moscow State University (2001). His research focuses on random dynamics and probabilistic models in mathematical physics, including stochastic processes, partial differential equations, and statistical physics. He has authored numerous influential papers on topics such as first passage percolation, directed polymers, and noise-driven dynamical systems. His academic roles include teaching advanced courses on probability, stochastic calculus, and limit theorems. Bakhtin has advised several Ph.D. students and holds NSF grants. His honors include the Institute of Mathematical Statistics Fellowship (2021). He is affiliated with editorial boards of journals like Stochastics and Dynamics .

• Algorithms and Complexity
• Theoretical Computer Science
• Probability Theory
• +۷ مورد دیگر

Dr. John A Lapinskas is a Senior Lecturer in the School of Computer Science at the University of Bristol, where he conducts research in theoretical computer science and discrete mathematics. His work is centered on algorithmic foundations, probabilistic models, and networked systems. His research interests include: Algorithms and Complexity Probability Theory in Computing Random Graphs and Spatial Networks Contention Resolution and Distributed Protocols Sublinear and Randomized Algorithms The recent publications of Dr. Lapinskas reflect a strong focus on theoretical models of computation and communication, particularly in distributed and stochastic environments. His work often bridges computer science and mathematical probability, with applications in network stability, graph algorithms, and protocol design. Key themes include the analysis of backoff protocols, first-passage percolation, and oracle-based estimation in hypergraphs. He collaborates with researchers across Europe and contributes to peer-reviewed journals and conferences in theoretical computer science. Dr. Lapinskas has authored or co-authored multiple publications in recent years, with a consistent output in high-quality venues such as the Journal of Computer and System Sciences and the Electronic Journal of Probability. His research is openly accessible and contributes to foundational knowledge in algorithm design and analysis. While no formal advising or grant details are listed, his active collaboration network and recent publications indicate ongoing research leadership. He is also associated with datasets related to contact-tracing applications, showing applied interest in public health modeling through computational methods.

Philippe Sosoeمشاهده پروفایل

دانشیار

• Probability
• Statistics
• Probabilistic models
• +۵ مورد دیگر

Philippe Sosoe is the Frank Spitzer and Narahari Umanath Prabhu Associate Professor of Mathematics at Cornell University's College of Arts and Sciences. He earned his Ph.D. in 2014 from Princeton University. Research Interests: Probability theory, statistical mechanics, and their applications to random matrix theory and spin systems. His work focuses on understanding probabilistic models like percolation and Dyson Brownian motion through analytic methods and partial differential equations. Publication Trends: Recent articles emphasize universality in random matrix theory (2016), sublinear variance in percolation models (2015), and central limit theorem regularity for Wigner matrices (2013). Scientific Awards: NSF development award Endowed professorship from A&S

• Geophysics
• Machine Learning
• Differential Equations
• +۵ مورد دیگر

Facu Sapienza is a Postdoctoral Scholar in Geophysics at Stanford University, focusing on interdisciplinary research that combines paleomagnetism , differential equations , machine learning , and quantum thermodynamics . His work bridges computational methods with Earth and planetary sciences, particularly through physics-informed machine learning models for glaciology and magnetospheric studies. His recent publications highlight expertise in Universal differential equations for geophysical modeling Statistical learning for remagnetization analysis Quantum thermodynamic resource theory Planetary magnetic field interactions (Mars) Distance learning algorithms with Fermat principles trends in applying Gaussian processes, causal inference, and differentiable programming to geoscience challenges. He maintains an active research profile with collaborations in computational geophysics and quantum information theory, though no formal awards or student advising details are publicly listed in the provided materials.

Lucas Gerinمشاهده پروفایل

مدرس

• Random graphs
• Permutations
• Scaling limits
• +۳ مورد دیگر

Lucas Gerin is a Lecturer-researcher at the École Polytechnique's CMAP Probability Department. His roles include co-heading the Probability Department and supervising PhD students such as Théo Lenoir and Maxime Marivain. His research focuses on random graphs, permutations, scaling limits, and probabilistic combinatorial optimization, with notable contributions to permutation patterns and graphon limits. Education: PhD in Applied Mathematics from Université Henri Poincaré (2008), Habilitation to Supervise Research from Université Paris-Sud (2018). Grants: ANR Louccoum (2025-2029). Events: Organized PC Days 2022, designed logo for Permutation Patterns 2019, participated in conferences like European Meeting of Statisticians (2015). Labs/Teams: Active in CMAP research groups focusing on probability and combinatorial structures. Research highlights include work on the Ulam-Hammersley problem, dense graph limits, and longest increasing paths in permutations. His interdisciplinary work bridges probability theory, combinatorics, and statistical physics.

Chiranjib Mukherjeeمشاهده پروفایل

استاد

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

• Probability Theory
• Stochastic Processes
• Mathematical Physics
• +۳ 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.

Régine Marchandمشاهده پروفایل

مدرس

• Discrete probability
• Statistical physics
• Particle systems
• +۴ مورد دیگر

Régine Marchand is a permanent lecturer at the Faculty of Science and Technology of the University of Lorraine , affiliated with the Probability and Statistics team at IECL (Institut Élie Cartan de Lorraine). She works in the fields of discrete probability theory and statistical physics, with a focus on probabilistic models in spatial random structures. Position: UL Lecturer and HDR Lecturer Institution: University of Lorraine Research Location: Campus des Aiguillettes, Boulevard des Aiguillettes, 54506 Vandœuvre-lès-Nancy, Office 209 Research Interests: Her work centers on stochastic processes and spatial probability models, including: Discrete probability distributions Statistical physics of random systems Interacting particle systems Percolation thresholds and first passage phenomena Boolean percolation models Contact: regine.marchand@univ-lorraine.fr , +33 3 72 74 54 69

• Stochastic Processes
• Martingale Representation Properties
• Markov Jump Processes
• +۴ مورد دیگر

Kais Hamza is a Professor and Deputy Head of School at the School of Mathematics, Monash University , with a focus on stochastic processes and their applications. His research bridges theoretical probability (martingales, Markov jump processes) and practical domains like financial market modeling, population dynamics, and biomedical systems. Stochastic Processes : General theory, martingale representation, and random walk analysis Mathematical Finance : Financial derivatives, volatility models, and scenario generators Biomedical Applications : Vocal cord dysfunction diagnostics, COPD exacerbation biomarkers, and respiratory imaging Recent work includes books and articles on stochastic calculus, CT imaging for laryngeal disorders, and metabolic biomarkers in COPD. No specific student advisement details are provided. Key collaborations span institutions like the Australian Research Council and Data61 , with projects on topics like Self-Interacting Random Walks (2023–2026).