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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
• +7 more

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
• +5 more

Timo Seppäläinen is a Professor and the John and Abigail Van Vleck Chair of Mathematics at the University of Wisconsin-Madison since 2005. He is affiliated with the Mathematics Department under the College of Letters & Science. Education: Ph.D. in Mathematics (1991) from the University of Minnesota M.Sc. in Industrial Engineering and Management (1986) from Aalto University High School Diploma (1980) from Helsinki I Normal School Research Interests: His work primarily focuses on probability theory , encompassing topics such as motion in random media, first- and last-passage percolation, interacting particle systems, and large deviation theory. He has contributed to graduate-level stochastic analysis and authored lecture notes on the corner growth model , exclusion processes, and renewal theory. His research has been supported by the National Science Foundation and the Wisconsin Alumni Research Foundation. Scientific Awards and Honors: 2000 Prix Institut Henri Poincaré-Gauthier Villars 2010 Fellow of the Institute of Mathematical Statistics 2011 Van Vleck Research Prize 2014 IMS Medallion Lecturer and Invited Speaker at International Congress of Mathematicians 2015 Vilas Faculty Mid-Career Investigator Award 2015-2016 and 2023-2024 Simons Fellow in Mathematics 2016 Van Vleck Chair of Mathematics 2017 Fellow of the American Mathematical Society Teaching and Editorial Work: He teaches undergraduate and graduate courses in probability theory and stochastic processes, including Math 632 , Math 733-734 , and Math 735 . He has served on editorial boards and organized probability seminars.

• Probability Theory
• Stochastic Processes
• Random Medium Motion
• +4 more

Partha Sarathi Dey serves as Associate Professor in both Mathematics and Statistics at the University of Illinois at Urbana-Champaign, joining the faculty in 2014 after postdoctoral appointments at NYU's Courant Institute and the University of Warwick. His academic credentials include: Ph.D. in Statistics from UC Berkeley (2010) Undergraduate and Masters degrees from Indian Statistical Institute, Kolkata, specializing in Mathematical Statistics and Probability Dr. Dey's research bridges Probability Theory and Statistical Physics, with expertise in First/Last Passage Percolation, Random Growth Models, Stein's Method, Concentration Inequalities, Spin Glasses, Random Graphs, and Random Matrices. His work develops rigorous probabilistic frameworks for physical systems. Recent publications (2023-2025) demonstrate consistent focus on disordered systems and phase transitions, examining random walks on discrete tori, nonlinear Schrödinger equations in higher dimensions, critical-strip path structures, monomer-dimer models, and spin glasses under external fields. These studies reveal deep connections between probabilistic fluctuations and thermodynamic behavior. His distinguished recognitions include: Simons Fellowship Harrison Early-Career Fellowship Though specific advising records and grants are unreported, his extensive co-authorship network reflects active collaboration across international research communities in mathematical physics.

• Probability Theory
• Statistical Physics
• First Passage Percolation
• +7 more

Christopher Janjigian is an Assistant Professor in the Department of Mathematics at Purdue University, specializing in probability theory with a focus on random walks in random environments, KPZ universality class phenomena, and stochastic partial differential equations. He holds a Ph.D. from the University of Wisconsin-Madison (2016) and has held postdoctoral positions at Université Paris Diderot (2016–2017) and the University of Utah (2017–2020). His research explores the infinite volume structure of random walks in random potentials and connections to models like the Kardar-Parisi-Zhang equation. He organizes the Purdue University Probability Seminar and has taught advanced courses including Stochastic Processes, Probability Theory, and Stochastic Calculus. His work bridges theoretical probability with applications in mathematical physics, emphasizing geometric and dynamic properties of stochastic systems. Recent research focuses on geodesic structures in percolation models, Busemann functions, and ergodic properties of directed polymer models. His articles investigate topics like exit point bounds in last-passage percolation and synchronization in the KPZ equation, contributing to the understanding of universal behaviors in stochastic systems.

• Mathematical Physics
• Probability Theory
• Random Walks in Random Environments
• +3 more

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
• +1 more

Ron Peled is a Full Professor in the School of Mathematical Sciences at Tel Aviv University. Starting in summer 2024, he will serve as a Brin Professor in the Department of Mathematics at the University of Maryland, on leave from Tel Aviv University. During the 2022-2024 academic years, he visited Princeton University and the Institute for Advanced Study. His research spans multiple areas of probability theory and statistical physics, with significant contributions to understanding random surfaces, first-passage percolation, spin systems, and disordered models. Peled's research interests primarily focus on Probability Theory and Statistical Physics. His work examines the behavior of random systems, particularly in the presence of disorder or constraints. He has made significant contributions to understanding minimal surfaces in random environments, the structure of geodesics in first-passage percolation, phase transitions in spin systems, and the properties of random surfaces. His research often combines deep probabilistic insights with connections to statistical mechanics and mathematical physics, revealing universal behaviors in complex random systems. Analysis of Peled's recent publications reveals a strong focus on understanding the effects of disorder in statistical physics models. His work spans multiple domains including first-passage percolation, random surfaces, spin systems, and random matrix theory. A recurring theme is the investigation of how microscopic randomness affects macroscopic properties, with particular attention to phase transitions, correlation decay, and geometric structures emerging in random environments. His research often employs sophisticated probabilistic techniques combined with insights from statistical mechanics. Peled has received significant recognition through prestigious grants including multiple Israel Science Foundation grants (1048/11, 861/15, 1971/19, 2340/23), a Marie Skłodowska-Curie Actions International Reintegration Grant (SPTRF), an ERC Starting Grant (LocalOrder), and an ERC Consolidator Grant (Transitions). These awards reflect the importance and impact of his research in the mathematical community. Peled has supervised numerous students and postdocs throughout his career. His Ph.D. students include Daniel Hadas (joint with Wojciech Samotij) and Yinon Spinka (graduated August 2018). His Master's students include Michal Bassan (joint with Shoni Gilboa), Daniel Hadas, Yoav Bar Nir, Dor Elboim (who went on to do a Ph.D. at Princeton), Vital Kharash, Omri Cohen-Alloro, Alexey Gladkich, and Yinon Spinka. He has also mentored postdoctoral fellows including Lakshmi Priya, Paul Dario, Matan Harel, Raimundo Briceño, Alexander Glazman, Alexander Magazinov, Xiaolin Zeng, Nishant Chandgotia, Jeremiah Buckley, Wojciech Samotij, and Tom Ellis. Peled is actively involved in the academic community, serving as one of the organizers of the online Joint Israeli Probability Seminar and previously organizing the Horowitz Seminar on Probability, Ergodic Theory and Dynamical Systems. He has also organized several workshops and conferences including "Challenges in probability and statistical mechanics" at the Technion in 2022, the "Workshop on Strongly Correlated Random Interacting Processes" at Oberwolfach in 2018, and "Elegance in probability: A conference honoring Russell Lyons' 60'th birthday" at Tel Aviv University in 2017.

• Probability Theory
• Statistical Physics
• Random Surfaces
• +4 more

Professor Tony Roberts is the Head of School in the School of Mathematical Sciences at Queensland University of Technology (QUT). He holds a PhD from the Australian National University and is a Fellow of the Australian Mathematics Society. His research focuses on the interplay between material microstructure and macroscopic properties, with emphasis on topology optimization, random structure modeling (e.g., Gaussian fields, percolation models), and material property analysis such as conductivity, diffusion, and fluid flow. He develops computational methods for analyzing experimental techniques like 3D statistical reconstruction and small-angle scattering. His recent work includes optimizing piezoelectric materials for robotics, studying diffusion dynamics in fractal networks, and modeling material failure mechanisms. Key contributions span multi-functional piezoelectric components, anisotropic elastic properties of additively manufactured alloys, and fracture mechanics in perforated materials. Awards include his fellowship in the Australian Mathematics Society. Supervision interests include structural optimization, diffusion in random media, and porous material failure modeling. Education: PhD (Australian National University) Affiliations: Faculty of Science, School of Mathematical Sciences Research Themes: Material science, computational modeling, fracture mechanics, stochastic systems

• Material Science
• Mathematical Modeling
• Fractal Networks
• +5 more

Evan Sorensen is a Joseph F. Ritt Assistant Professor in the Department of Mathematics at Columbia University, specializing in probabilistic models of complex stochastic systems. Education PhD in Mathematics, University of Wisconsin—Madison (2023), advised by Timo Seppäläinen MA in Mathematics, University of Wisconsin—Madison (2020) BS in Mathematics, Brigham Young University (2017) Research Focus Dr. Sorensen investigates universal phenomena in stochastic processes through the lens of the KPZ universality class. His work rigorously analyzes prelimiting models including first-and last-passage percolation, directed random polymers, and interacting particle systems to uncover deep connections to random matrix theory and nonlinear stochastic partial differential equations. Current research explores scaling limits, fluctuation exponents, and invariant measures in these high-dimensional random environments. Affiliations & Activities Joseph F. Ritt Assistant Professor position (prestigious postdoctoral fellowship at Columbia) Active researcher in probability theory with focus on integrable probability No scientific awards, student advising records, or laboratory affiliations are publicly documented in available sources.

• Probability Theory
• Stochastic Processes
• KPZ Universality Class
• +4 more

Leon Bungert is a Professor of Mathematics of Machine Learning at the University of Würzburg, working in applied analysis and numerics with a particular focus on data science and machine learning. His research investigates PDEs and variational models on graphs, adversarial robustness of machine learning, variational regularization, and nonlinear optimization. Dr. Bungert serves as a guest editor for the European Journal of Applied Mathematics, an associate editor for Advances in Continuous and Discrete Models: Theory and Applications, and is a member of the program committee at SSVM 2025. He is also an ELLIS member and actively organizes conferences and workshops, including "MIA'25" at IHP in Paris (January 13-15, 2025), "Synergies of Machine Learning and Numerics" in Osaka (March 11-13, 2025), and "Mathematical Analysis of Adversarial Machine Learning" in Oaxaca (August 17-22, 2025). Research Interests Dr. Bungert's primary research areas include: PDEs on graphs Adversarial robustness in machine learning Inverse problems Optimization Variational problems in L-infinity Nonlinear eigenvalue problems Image reconstruction with structural priors His work bridges theoretical mathematics with practical applications in machine learning, particularly focusing on the mathematical foundations of deep learning and developing robust algorithms that can withstand adversarial attacks. He has made significant contributions to understanding the connections between partial differential equations and machine learning algorithms. Research Trends Analysis of Dr. Bungert's recent publications reveals a strong focus on the intersection of machine learning and mathematical analysis. A key theme is the application of variational methods and partial differential equations to machine learning problems, particularly in understanding and improving the robustness of neural networks against adversarial examples. His work on Lipschitz learning on graphs has established important theoretical foundations for graph-based semi-supervised learning. Additionally, his research on the infinity Laplacian and p-Laplacian equations provides deep insights into the mathematical structure of machine learning algorithms. The development of Bregman learning frameworks for sparse neural networks represents a significant contribution to efficient deep learning model training. Professional Activities Dr. Bungert is actively involved in the academic community through editorial roles and conference organization. His current professional activities include: Guest editor for the European Journal of Applied Mathematics Associate editor for Advances in Continuous and Discrete Models: Theory and Applications Member of the program committee at SSVM 2025 ELLIS member Co-organizer of multiple international conferences and workshops Technical Contributions Dr. Bungert has developed several open-source software packages that implement his theoretical contributions, including: Code for convergence rates of Lipschitz learning on graphs A Bregman training framework for sparse neural networks CLIP: Cheap Lipschitz Training of Neural Networks Nonlinear Power Method for Proximal Operators and Neural Networks Robust Image Reconstruction with Misaligned Structural Information These implementations are primarily in Python and MATLAB, demonstrating his commitment to making theoretical advances accessible for practical applications.

• Applied Analysis
• Numerical Analysis
• Data Science
• +6 more

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
• +4 more

Christopher Hoffman is a Professor in the Department of Mathematics at the University of Washington. He holds a PhD from Stanford University (1996) and specializes in probability theory, stochastic processes, and related areas such as percolation, ergodic theory, and combinatorics. His research explores topics including activated random walks, first-passage percolation, and self-organized criticality. He teaches courses like Math 124 Calculus I and maintains active involvement in academic outreach through Google Scholar and LinkedIn. Education: PhD in Mathematics, Stanford University, 1996 Research focuses on probabilistic models with applications to statistical physics and graph theory. Key interests include analyzing phase transitions, geodesic paths in percolation models, and properties of interacting particle systems. Recent work addresses critical density thresholds in activated random walks and scaling limits of stochastic processes. Hoffman frequently collaborates on interdisciplinary projects involving combinatorics and ergodic theory. Teaching responsibilities include foundational calculus courses and maintaining detailed lecture notes for students. His work has been supported by grants from the National Science Foundation and other institutions.

• Probability Theory
• Random Walks
• Percolation Theory
• +3 more

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.

• Stochastic processes
• Complex analysis
• Graph theory

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
• +1 more

Marcel Ortgiese is a Reader in the Department of Mathematical Sciences at the University of Bath, affiliated with the EPSRC Centre for Doctoral Training in Statistical Applied Mathematics (SAMBa) and the Probability Laboratory at Bath. His research focuses on probability theory, including spatial population models, stochastic processes in random environments, and evolving random graphs. He investigates large-scale behaviors of interfaces in evolutionary biology contexts, branching processes in inhomogeneous environments, and dynamic processes on random graph structures. His work integrates theoretical analysis with applications to real-world systems, emphasizing interdisciplinary connections between probability and complex networks. Current projects include studying the interplay between geometry and randomness in fitness landscapes for expanding populations (EPSRC-funded) and advancing methods for cumulants and superconcentration phenomena. Ortgiese has contributed to foundational research on contact processes, voter models, preferential attachment networks, and symbiotic branching systems. His recent studies explore adaptive network dynamics, subcritical random graph properties, and asymptotic behaviors in weighted recursive trees. He holds a PhD from the University of Bath (2009) under Prof. Peter Morters and has supervised numerous graduate students through SAMBa's training programs. His academic contributions are reflected in over 20 peer-reviewed articles in top journals such as Stochastic Processes and their Applications and Annals of Applied Probability . Research themes consistently emphasize stochastic analysis of complex systems, with applications ranging from epidemiological modeling to evolutionary dynamics.

• Probability Theory
• Stochastic Processes
• Spatial Population Models
• +3 more