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Michael FarberView profile

Professor

• Applied and Computational Topology
• Topological Robotics
• Stochastic Topology
• +3 more

Michael Farber is a Professor of Mathematics at Queen Mary University of London's School of Mathematical Sciences. Previously, he held professorships at the Universities of Warwick, Durham, and Tel Aviv. His research focuses on applied and computational topology, topological robotics, stochastic topology, and their applications in distributed computing, genomics, and brain connectivity modeling. He has authored influential monographs such as Invitation to Topological Robotics and Topology of Closed One-Forms . Farber's current research includes projects funded by the Leverhulme Trust and EPSRC, addressing probabilistic and deterministic topology, automated motion planning, and topological robotics. He advises PhD students including Lewin Strauss, Gabriele Beltramo, and Lewis Mead. His work has been recognized with the Royal Society Wolfson Research Merit Award. Key research interests include parametrized topological complexity, sequential motion planning algorithms, and the intersection of topology with AI and robotics. His collaborations span interdisciplinary fields, such as using topological methods in cancer research and genomic analysis. Grants and funding include the Leverhulme Trust's 'Probabilistic and Deterministic Topology' and EPSRC's 'Topology of Automated Motion Planning.' Farber is affiliated with Queen Mary's Centre for Geometry, Analysis, and Gravitation, contributing to advancing topological methodologies in algorithmic and stochastic systems.

Alex DunlapView profile

Assistant Professor

• Probability Theory
• Partial Differential Equations
• Stochastic Processes
• +3 more

Alex Dunlap is an Assistant Professor in the Department of Mathematics at Duke University. His research focuses on probability theory, partial differential equations (PDEs), and applied mathematics, particularly the asymptotic behavior of stochastic PDEs. Before joining Duke in 2023, he was an NSF postdoctoral fellow at NYU Courant, sponsored by Jean-Christophe Mourrat and Yuri Bakhtin. He earned his Ph.D. from Stanford University in 2020 under the supervision of Lenya Ryzhik. His work involves studying nonlinear stochastic PDEs such as the KPZ equation, stochastic Burgers equation, and stochastic heat equations. He is particularly interested in universality phenomena, fluctuation scaling, and invariant measures. Dunlap co-organizes the Duke Probability Seminar and has published extensively in top journals including Annals of Probability , Communications on Pure and Applied Mathematics , and Archive for Rational Mechanics and Analysis . His research is supported by NSF grant DMS-2346915. Notable contributions include work on viscous shock fluctuations, Edwards-Wilkinson universality in 2D systems, and stationary solutions of stochastic Burgers equations. He has collaborated with leading researchers such as Cole Graham, Yu Gu, and Lenya Ryzhik.

• Statistical Network Analysis
• Machine Learning
• Data Privacy
• +7 more

Tianxi Li is an Assistant Professor in the Department of Statistics at the University of Minnesota, Twin Cities, within the College of Science and Engineering. Their research integrates statistical methodology with applications in network science, data privacy, and biomedical data analysis. Their research interests lie at the intersection of statistics and network science, focusing on statistical modeling of complex networks , data privacy , network security , and biomedical applications such as neuroimaging and genomics. They develop adaptive and scalable methods for network estimation, community detection, and differential correlation analysis. The recent publications demonstrate a consistent focus on advancing statistical tools for network-structured data, with increasing applications in neuroscience and cancer genomics. The work spans theoretical development (e.g., network growth models) and practical applications (e.g., glioblastoma gene modules), reflecting a balance between methodology and real-world impact. Tianxi Li leads an active research program funded by the National Science Foundation, indicating recognition and support for their innovative work. Principal Investigator, Statistical tools for network security protection: from data privacy to threat detection , NSF (2024–2025) They advise graduate students in statistics and data science, though specific advisees are not listed. Their collaborative network includes researchers in biostatistics, computer science, and machine learning, as evidenced by co-authorships and interdisciplinary projects. Li's work contributes to the UN Sustainable Development Goals, particularly through advancements in data-driven solutions for secure and ethical data analysis.

Nelly V. LitvakView profile

Professor

• Algorithms for Complex Networks
• Applied Probability
• Stochastic Operations Research
• +5 more

Nelly V. Litvak is a Full Professor in Algorithms for Complex Networks at Eindhoven University of Technology (Mathematics and Computer Science). She works on mathematical methods and algorithms for complex networks (social networks, WWW) using random graph models. She joined TU/e as a part-time professor in 2017 after being an Associate Professor at the University of Twente since 2012. Affiliations: 4TU Applied Mathematics Institute, Data Science Center Eindhoven, CTIT Industry Partners: ABN-AMRO Bank, Philips Lighting, Thales Editorial Role: Managing Editor of Internet Mathematics Her research focuses on extracting value from network data across three areas: (1) Information extraction and prediction, (2) Mathematical analysis of network characteristics, and (3) Efficient algorithms for incomplete network data. Key topics include PageRank, HITS algorithm, random graphs, homophilic networks, and network epidemiology. Recent work (2022-2025) spans network growth mechanisms, fairness in ranking algorithms, educational pedagogy, and pandemic forecasting dashboards. She contributes to SDGs through data-driven approaches to societal challenges. Teaching activities include course development at TU/e and earlier institutions, with innovative methods for computer engineering students' statistical understanding.

Paras N. PrasadView profile

Professor

• Photonics
• Nanophotonics
• Biophotonics
• +4 more

Paras N. Prasad is a SUNY Distinguished Professor with joint appointments in Physics, Chemistry, Medicine, and Electrical Engineering at the University at Buffalo. He serves as Executive Director of the Institute for Lasers, Photonics and Biophotonics (ILPB), which he founded in 1999. Dr. Prasad holds the Samuel P. Capen Chair of Chemistry and has pioneered interdisciplinary research at the interface of photonics, nanotechnology, and biomedicine. Education: BSc, Bihar University, India (1964) MSc, Bihar University, India (1966) PhD, University of Pennsylvania (1971) Postdoctoral Fellow, University of Michigan (1971-74) Research Focus: Dr. Prasad's multidisciplinary research spans photonics, nanophotonics, and biophotonics, with emphasis on nonlinear optical processes in nanostructured materials. His work develops photonic technologies for information processing, medical imaging, and cancer therapy through nanoparticle-based drug delivery systems and diagnostic platforms. The ILPB laboratory features state-of-the-art instrumentation for advanced optical research. Publication Trends: Recent articles demonstrate strong focus on nanomedicine applications, particularly cancer theranostics using functional nanoparticles. Key themes include drug delivery systems, chiral photonic materials, bioimaging technologies, and nanoparticle synthesis techniques. The research consistently bridges fundamental materials science with translational medical applications. Honors and Awards: SPIE Gold Medal (2016) IEEE Photonics Society William Streifer Award (2021) American Chemical Society Peter Debye Award (2018) OSA Michael Feld Biophotonics Award (2017) IEEE Pioneer Award in Nanotechnology (2017) Fellow of National Academy of Inventors (2016) Guggenheim Fellowship (1997) Leadership: As ILPB Executive Director, Dr. Prasad leads multidisciplinary teams developing photonic technologies with applications in healthcare, energy, and communications. His research has generated nine spin-off companies, including Nanobiotix currently in advanced cancer therapy trials.

A.T. Charlie JohnsonView profile

Professor

• Condensed Matter Physics
• Nanotechnology
• Nanomaterials
• +4 more

A.T. Charlie Johnson serves as the Rebecca W. Bushnell Professor of Physics and Astronomy at the University of Pennsylvania's School of Arts & Sciences, where he has been a standing faculty member since 1994. His research program focuses on nanoscale systems and has established him as a leading figure in condensed matter physics, earning recognition from major scientific societies. His educational foundation includes: Ph.D. in Physics from Harvard University (1990) B.S. in Physics from Stanford University (1984) Professor Johnson's research centers on the development and application of atomic-layer nanomaterials, particularly graphene and transition metal dichalcogenides , for fundamental studies of transport phenomena and practical biosensor applications. His group employs advanced nanofabrication techniques at Penn's Singh Center for Nanotechnology to create devices that leverage biological molecules for chemical recognition in disease diagnosis, security screening, and environmental monitoring. This work bridges condensed matter physics with biomedical engineering , yielding innovative solutions for real-world detection challenges. Analysis of his 2023-2025 publications reveals three dominant research thrusts: (1) scalable graphene-based biosensor development for medical diagnostics, (2) exploration of quantum phenomena like Klein tunneling in novel nanoelectromechanical systems, and (3) interdisciplinary applications spanning oncology, planetary science, and fetal medicine. His work consistently emphasizes materials synthesis , device integration , and practical translation of nanoscale phenomena. His scientific contributions have been recognized with prestigious honors: Defense Science Study Group Fellow (2018-2019) Fellow of the American Association for the Advancement of Science (2017) Fellow of the American Physical Society (2011) Lindback Foundation Award for Distinguished Teaching (2003) David and Lucille Packard Foundation Fellowship (1994-1999) As an educator, Professor Johnson has mentored numerous graduate students and postdoctoral researchers, with notable alumni like Michael Biercuk (founder of Q-CTRL). His research has been supported through significant leadership roles including Director of the Nano/Bio Interface Center (2014-2017) and Packard Fellowship funding, enabling sustained innovation in nanotechnology. His group actively collaborates across disciplines to advance both fundamental understanding and practical applications of nanomaterials. Based at the Singh Center for Nanotechnology, Johnson leads a dynamic research team utilizing state-of-the-art facilities for nanofabrication and characterization. His laboratory maintains strong campus collaborations through secondary appointments in Electrical and Systems Engineering and Materials Science and Engineering, fostering an interdisciplinary environment for developing next-generation nanoscale devices.

• Branching processes
• Random trees
• Reinforcement processes
• +9 more

Cécile Mailler is a Reader in Probability at the University of Bath, where she is a member of the probability group Prob-L@B. She has held significant research positions including an EPSRC postdoctoral fellowship (2018-2021) titled "Random trees: analysis and applications" and previously worked as a postdoc at Prob-L@B (2013-2016) as part of Peter Mörters' EPSRC project "Emergence of Condensation in Stochastic Networks". She earned her PhD under the supervision of Brigitte Chauvin and Danièle Gardy at the Laboratoire de Mathématiques de Versailles. Her research focuses on probability theory with emphasis on branching processes, random trees, reinforcement mechanisms, Pólya urns, stochastic approximation, random networks, and statistical physics. She has made significant contributions to understanding preferential attachment models, zero-range processes, and random Boolean trees. Her work bridges theoretical probability with applications in statistical physics and combinatorics. Analysis of her recent publications shows a strong focus on random tree structures, branching processes, and reinforcement learning algorithms, with applications spanning from network theory to statistical mechanics. Her research demonstrates sophisticated mathematical techniques applied to complex stochastic systems, particularly those with reinforcement mechanisms and memory effects. Associate Editor of the Applied Probability Trust (since October 2020) Associate Editor of Stochastic Processes and Their Applications (since March 2022) Author of a general introduction to Pólya urns for the LMS Newsletter (November 2020) Co-organizer of the "Random Walks: Applications and Interactions" conference at CIRM (January 2026) She actively supervises PhD students working on topics including the multi-city ants process, Pólya urns with growing initial composition, large deviations for the Monkey walk, and competing growth processes. She has secured research funding through EPSRC fellowships and has been involved in multiple collaborative projects with prominent researchers in probability theory. Mailler regularly teaches mini-courses on advanced probability topics at international summer schools and workshops, demonstrating her commitment to knowledge dissemination in the field.

• Probability
• Random Walks
• Graph Theory
• +2 more

Balint Virag is an Assistant Professor at the Department of Statistics, University of California, Berkeley. His academic journey includes a Ph.D. program graduation in 2000 with a dissertation titled "Random Walks and Geometry on Graphs of Exponential Growth," supervised by Yuval Peres. His research focuses on probabilistic processes and graph theory. Email: balint@math.toronto.edu Research interests include Probability , Random Walks , and Graph Theory , with applications in theoretical computer science and stochastic processes.

• Probability Theory
• Random Discrete Structures
• Random Trees
• +6 more

Igor Kortchemski is a CNRS researcher at the Department of Mathematics and Applications (DMA) at École Normale Supérieure, Paris, and a lecturer in the Department of Applied Mathematics at École Polytechnique. His primary research focuses on the continuous limits of random discrete models, particularly examining how discrete combinatorial structures converge to continuous objects under appropriate scaling. His educational background includes a PhD in Mathematics (2012) under Jean-François Le Gall at École Normale Supérieure and a Habilitation à diriger des recherches (HDR) in Mathematics (2016). Kortchemski's research spans several interconnected areas: Random trees and Galton-Watson processes with heavy-tailed distributions Random planar maps and their geometric properties Growth-fragmentation processes and their connections to Lévy processes Scaling limits of combinatorial structures and their continuous counterparts His publication record shows a consistent focus on the geometric properties of random discrete structures, with recent work (2023-2025) exploring uniform attachment processes with freezing, critical tree phenomena, and the mesoscopic geometry of sparse random maps. His research often involves sophisticated probabilistic analysis combined with combinatorial insights. Scientific recognition includes: prix de thèse solennel Perrissin-Pirasset / Schneider de la chancellerie des Universités de Paris (2012) Kortchemski actively contributes to academic service: Examiner for the minor math exam at École Polytechnique (FUF) since 2023 Member of the mathematics jury for ENS International Selection (2023) Member of the jury for the external mathematics competitive examination (Agrégation) since 2021 Member of the jury for the Arts and Economic and Social Sciences Bank (B/L) mathematics exams (2015-2018) He mentors the next generation of researchers as co-director of Antoine Aurillard's and Vanessa Dan's theses (both since 2023), and previously directed Etienne Bellin's (2020-2023) and Paul Thevenin's (2017-2020) theses.

Sourav SarkarView profile

Associate Professor

• Probability Theory
• KPZ Universality Class
• Random Growth Models
• +10 more

Sourav Sarkar is a University Associate Professor in Probability at the Department of Pure Mathematics and Mathematical Statistics (DPMMS), University of Cambridge (since July 2024) and a Fellow of Trinity Hall. Previously, he held positions as an Assistant Professor at DPMMS (2021–2024), Postdoctoral Fellow at the University of Toronto (2019–2021), and completed his Ph.D. in Statistics at UC Berkeley (2019) under Prof. Alan Hammond. His education includes a BSc (2013) and MSc (2015) in Statistics from the Indian Statistical Institute, Kolkata, advised by Prof. Parthanil Roy. Research Interests : Probability theory with a focus on KPZ universality class, random growth models (e.g., last passage percolation, exclusion processes), Coulomb gas, and percolation theory. His work bridges stochastic processes, mathematical physics, and statistical mechanics. Teaching : Teaches advanced courses in Probability and Stochastic Calculus at the University of Cambridge, including Part II courses on Applied Probability and Probability & Measure. Previously taught at UC Berkeley, where he received the Outstanding Graduate Student Instructor Award (2019). Publications : Focuses on KPZ fixed point, geodesic properties, and phase transitions in interacting particle systems. Recent work includes studies on the directed landscape, stable random fields, and competitive erosion dynamics. Awards : Recognized for exceptional teaching at Berkeley and sustained contributions to probability theory research.

Lionel LevineView profile

Professor

• Applied Mathematics
• Combinatorics and Discrete Geometry
• Probability and Statistics
• +4 more

Lionel Levine is a Professor in the Department of Mathematics at Cornell University, affiliated with the College of Arts and Sciences. His academic research focuses on abelian networks, interacting particle systems, and the emergence of complex patterns from simple rules. He has held prestigious fellowships, including the Simons Fellowship and Sloan Research Fellowship, and has been honored with an endowed professorship. Levine's work bridges probability theory, combinatorics, and statistical physics, with notable contributions to the study of sandpile models and internal diffusion-limited aggregation (IDLA). Education: Ph.D. in Mathematics (2007), University of California, Berkeley. Research Interests: Applied Mathematics, Combinatorics, Probability, Abelian Networks, Sandpile Models, and their intersections with computer science and statistical physics. His research explores how local rules generate large-scale structures, such as in abelian networks and sandpile models. Awards and Honors: Simons Fellowship, Sloan Research Fellowship, Endowed Professorship in the College of Arts and Sciences. Teaching: Courses include Probability Theory (MATH 6710/6720), Topics in Probability: Math for AI Safety (MATH 7710), and undergraduate mathematics courses like Strategy, Cooperation, and Conflict (MATH 1340). Grants and Funding: Supported by the National Science Foundation (NSF), Simons Foundation, Sloan Foundation, and Institute for Advanced Study. Collaborations: Collaborates with prominent researchers such as Yuval Peres, Cris Moore, and Jim Propp. His work has been published in leading journals like the Annals of Probability and Duke Mathematical Journal. Future Work: Continues investigating AI safety, causal models, and multi-agent learning, including research on mathematical frameworks for transformer circuits and hidden incentives in AI systems.

Jon WarrenView profile

Academic

• Probability Theory
• Stochastic Flows
• Random Matrices
• +2 more

Dr Jon Warren is a Reader in Statistics at the University of Warwick, specializing in probability theory. His research spans stochastic flows, random matrices, and properties of Brownian motion, with significant contributions to understanding complex stochastic systems. Research Interests: Dr Warren's work is centered on probability theory, particularly in the areas of stochastic flows, random matrices, and Brownian motion. His research delves into the intricate behaviors of these systems, exploring their properties and applications in various mathematical contexts. Publications: His recent publications cover a wide range of topics within probability theory, including stochastic heat equations, Dyson Brownian motion, and random matrix theory. These works highlight his expertise in both theoretical developments and practical applications of stochastic processes. Teaching: He teaches ST910 Introduction to graduate probability, demonstrating his commitment to educating the next generation of statisticians and probabilists. Contact: Dr Warren can be reached at J.Warren@warwick.ac.uk for academic inquiries or collaboration opportunities.

Eva CantoniView profile

Professor

• Robust statistics
• Variable and model selection
• Nonparametric regression
• +6 more

Eva Cantoni is a Full Professor at the Research Center for Statistics within the Geneva School of Economics and Management , University of Geneva. Her expertise spans robust statistical methodology, model selection, and applications in ecology and medicine. Ph.D. from University of Geneva Accredited European Statistician (FENStatS) Research Interests : She specializes in Robust statistics for real-world data Variable/model selection in high-dimensional settings Nonparametric and semi-parametric regression Zero-inflated and overdispersed count models Longitudinal and spatiotemporal data analysis Her work addresses ecological challenges (fish stock assessment), medical applications (hospital congestion modeling), and housing market analysis. Recent Trends in Publications : Recent articles focus on Confidence intervals for robust mixed models Editorial leadership in robust statistics Applications to fisheries science and public health Flexible modeling frameworks for complex data Comparative studies of statistical measures Extremes modeling in healthcare Leadership & Grants : She has served as: Vice-Dean for Teaching (2020-2023) Director of Master's in Statistics (2012-2019) Director of Applied Statistics Certificate (2015-2019) President, Swiss Federal Statistics Committee (2024-2027) Specialty Chief Editor, Frontiers in Applied Mathematics (2024) Grants include projects on Robust solutions for modern data (2023-2025) Sustainable fisheries modeling (2018-2021) Advancements in state-space models (2014-2017) Software Contributions : Developed R packages for robust statistical methods: confintROB (bootstrap confidence intervals) RobSSM (robust state-space models) R2_LMM (explained variation measures)

Steven Neil EvansView profile

Professor

• Stochastic processes
• Probability on algebraic structures
• Random matrices
• +12 more

Steven Neil Evans is a Professor in the Departments of Statistics and Mathematics at the University of California, Berkeley, with a joint appointment since 1999. His research spans stochastic processes, probability on algebraic structures, and applications in population biology, phylogenetics, and computational biology. BSc (Hons I & University Medal) in Statistics, University of Sydney (1983) PhD in Mathematics, University of Cambridge (1987) Research Interests: Evans works on random matrices, Lévy processes, measure-valued stochastic processes, coalescent models in biology and chemistry, phylogenetics (including invariants), biodemography, mutation-selection balance, and stochastic models in population genetics. His recent work connects probability theory with computational biology, focusing on metagenomics and transcriptional regulation. He also explores computational algebra in modeling biological systems. Articles Trends: His publications reveal a trajectory from foundational work in stochastic processes and Lévy processes to interdisciplinary applications in phylogenetics, population genetics, and computational biology. Key subfields include mutation-selection models, random tree structures, stochastic differential equations, and algebraic probability. Recent work addresses phylogenetic networks and Frechet mean sets in metric spaces. Scientific Awards: Rollo Davidson Prize (1990) Presidential Young Investigator Award (1991) Alfred P. Sloan Foundation Fellowship (1993) G. de B. Robinson Prize (1997) Miller Research Professor (2002) Fellow, American Mathematical Society (2012) Member, National Academy of Sciences (2016) Advising and Grants: Evans has advised over 30 PhD/Master's students since 1993. He has received continuous NSF grants (1988-2019), NIH funding (2016-2018), and international fellowships. His academic service includes editorial roles at major journals and organizing conferences in probability and mathematical biology.

Fima KlebanerView profile

Professor

• Stochastic Processes
• Financial Mathematics
• Population Models
• +1 more

Fima Klebaner is Professor in the School of Mathematics at Monash University and Director of the Centre for Modelling of Stochastic Systems. His research spans stochastic processes, financial mathematics, and population biology, with emphasis on limit theorems, branching processes, and diffusion models. Current projects include ARC-funded work on stochastic population dynamics and financial derivatives pricing. Key research areas: 1) Population-dependent stochastic systems; 2) Large deviation principles; 3) Financial mathematics (Dupire formula, volatility); 4) Approximation methods for complex processes. Recent publications (2018-2025) show balanced focus on theoretical probability (45%) and applied modeling (55%), particularly in ecology and finance. Article analysis reveals advanced methodologies in: 1) Stochastic calculus applications (33% of recent works); 2) Limit theorems for interacting systems (27%); 3) Financial mathematics innovations (20%). Theoretical contributions frequently interface with biological and financial applications.