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Luc VinetView profile

Professor

• Theoretical Physics
• Mathematical Physics
• Quantum Information
• +7 more

Luc Vinet is a Full Professor of Physics at the Université de Montréal and Executive Director of IVADO. He holds dual PhDs from Université Pierre et Marie Curie (Paris) and Université de Montréal. His research focuses on quantum systems, symmetries, algebraic structures, and orthogonal polynomials. He has led major research institutions like the Centre de recherches mathématiques (CRM) and co-founded MITACS. Vinet has received prestigious awards, including the Order of Canada and the Armand-Frappier Prize. Educations: PhD in Theoretical Physics, Université Pierre et Marie Curie (1979) PhD in Theoretical Physics, Université de Montréal (1980) Research Interests: His work spans exact solutions in physics, quantum information theory, integrable systems, and algebraic combinatorics. He explores symmetries in quantum walks, exclusion processes, and stochastic models. His methodologies involve algebraic structures and orthogonal polynomials, with applications in quantum computing and data science. Awards: Officier de l’Ordre National du Québec (2017) Member of the Royal Society of Canada (2018) Order of Canada (2021) Prix ACFAS Urgel-Archambault (2022) Advising & Grants: Vinet has supervised numerous doctoral and postdoctoral researchers. His funding includes leadership roles in IVADO’s projects like PandemicStop-AI and contributions to CRM’s research initiatives. He chairs committees fostering collaboration between academia and industry. Labs/Teams: Director of CRM and IVADO, leading interdisciplinary teams in mathematical physics, data science, and AI. Key projects include quantum information transport and algebraic representations in integrable models.

Sampath K. KannanView profile

Professor

• Algorithms
• Computational Biology
• Program Checking
• +5 more

Professor Sampath K. Kannan is the Henry Salvatori Professor in the Department of Computer and Information Science at the School of Engineering and Applied Science, University of Pennsylvania. His research focuses on algorithms, computational biology, program checking, and network security. ACM Fellow (2013) ACM SIGACT Distinguished Service Award (2012) Outstanding Faculty Advising Award (2005) Ford Foundation 'Best Advisor' Award (2004) His research spans algorithms (massive data processing, shortest-path optimization), computational biology (evolutionary tree reconstruction, phylogenetic analysis), and program checking (runtime verification, trust management). Recent work involves data stream algorithms and healthcare scheduling optimization . He has taught advanced courses in algorithms and complexity theory since 1994, including CIS 677 (Complexity Theory) and GCB 537 (Computational Biology). His publications bridge theoretical computer science and applied systems research , with a focus on scalability, correctness, and security.

Nikolaos ZygourasView profile

Professor

• Probability Theory
• Statistical Physics
• Random Polymers
• +5 more

Nikolaos Zygouras is a Professor of Mathematics at the University of Warwick's Department of Mathematics, based in the Zeeman Building. His research focuses on Probability Theory and its applications to statistical physics models such as random polymers, KPZ universality, stochastic PDEs, and disordered systems. He explores connections to PDEs, representation theory, combinatorics, and random matrices. Key research areas include critical phenomena, scaling limits, and exactly solvable models. He teaches advanced courses in probability and integrable probability, including Symmetric Functions and Integrable Probability, and has taught a range of graduate and undergraduate courses on stochastic processes, mathematical methods, and statistical analysis. His editorial roles include serving as an Associate Editor for the Journal of the London Mathematical Society, Annals of Applied Probability, and Electronic Journal of Probability. Prof. Zygouras has organized numerous conferences and workshops, such as the P@W Summer School on Probability and Statistics and events on Random Polymers and Algebraic Combinatorics, supported by institutions like EPSRC and the Clay Mathematics Institute. His work integrates theoretical insights with collaborative research across disciplines.

• Group Theory
• Finite Groups
• Permutation Groups
• +3 more

Dr. Luca Sabatini is a Research Fellow at the University of Warwick. His work focuses on advanced topics in group theory, including finite groups, permutation groups, nilpotent subgroups, and abelian sections. He explores structural properties of groups through probabilistic methods and their applications to graph theory and expander constructions. His research bridges abstract algebra with combinatorial and probabilistic techniques. Key research interests include Sylow subgroup interactions, stabilizer subgroup analysis, quasirandom group properties, and the growth of abelian sections within finite transitive groups. His work often involves studying subgroup structures, element orders, and maximal subgroup behaviors in both theoretical and computational contexts. His publications from 2021–2025 highlight a focus on group diameter, random Schreier graphs, and extremal subgroup constructions. These studies contribute to understanding the probabilistic and structural limits of finite groups. No awards or grants are explicitly mentioned in the provided data. Labs/Teams: No specific lab or collaborative team affiliations are noted in the text.

Daniel KráľView profile

Professor

• Combinatorics
• Graph Theory
• Extremal Combinatorics
• +6 more

Professor Daniel Kráľ is the Alexander von Humboldt Professor for Discrete Mathematics at Leipzig University since April 2025, affiliated with the Max Planck Institute for Mathematics in the Sciences. Previously, he held the Donald Ervin Knuth Professorship at Masaryk University (2018–2025) and was a professor at the University of Warwick (2012–2020). He also serves as an honorary professor at Warwick and has held visiting roles at UC Berkeley (2025) and Georgia Tech (2005–2006). His research focuses on combinatorics, graph theory, and their intersections with computer science, particularly in extremal combinatorics, graph limits, and algorithmic graph theory. Education: Ph.D. in Computer Science (2004), Master's (2001) from Charles University, Prague. Early career included postdoc roles at TU Berlin and Charles University's Institute for Theoretical Computer Science. Research Interests: Theory of combinatorial limits, extremal combinatorics, structural and algorithmic graph theory, analytic methods in discrete mathematics. Key contributions include resolving Steinberg's Conjecture and Lovász-Plummer Conjecture, development of graph limit theory, and applications to algorithm design. Grants & Awards: Recipient of ERC Starting (CCOSA) and Consolidator (LADIST) grants, SIAM Fellowship (2024), AMS Fellow (2020), Philip Leverhulme Prize (2014), European Prize in Combinatorics (2011). Over 150 journal articles, with impactful work in Advances in Mathematics , Journal of the EMS , and others. Service: Editor-in-Chief of SIAM Journal on Discrete Mathematics , co-editor of Journal of Combinatorial Theory , co-founder of Advances in Combinatorics . Organized major conferences and workshops, including the 2025 Oberwolfach workshop on Graph Theory. Labs/Teams: Leading research groups in discrete mathematics at Leipzig and previously in Brno/Warwick. Collaborates with postdocs and students on projects spanning graph limits, integer programming, and combinatorial optimization.

Dario SpanòView profile

Professor

• Combinatorial Stochastic Processes
• Mathematical Population Genetics
• Bayesian Nonparametric Statistics
• +3 more

Professor Dario Spanò is a faculty member in the Department of Statistics at the University of Warwick, serving as Deputy Head (Teaching and Learning). He holds a PhD in Mathematical Statistics (University of Pavia, 2003) and a Laurea in Economics (University of Pavia, 1998). His research focuses on combinatorial stochastic processes, Bayesian nonparametric statistics, and mathematical population genetics, with applications to genetics and evolutionary dynamics. His work includes contributions to Wright-Fisher diffusions, coalescent theory, and exact simulation methods. He has supervised numerous PhD students and co-organizes international conferences in statistics and probability. Research Interests: Stochastic processes and their applications in population genetics Bayesian nonparametric methods and their theoretical foundations Exact simulation of diffusion processes Coalescent theory and genealogical processes Recent Work Trends: Recent publications emphasize theoretical developments in diffusion models (e.g., Wright-Fisher processes), duality methods, and computational Bayesian inference for genetic data. Key themes include excursion theory, selection dynamics in random environments, and algorithmic advancements for simulating complex stochastic systems. Awards & Recognition: No scientific awards explicitly listed, though his research has been widely cited in the field. Teaching & Supervision: Current courses include ST343 and ST419 (data science topics). He has supervised over a dozen PhD students, focusing on interdisciplinary projects in statistics and mathematical genetics. His students have pursued roles in academia and industry. Labs/Teams: Active in the CRiSM research centre at Warwick, contributing to collaborative projects in statistical genetics and computational methods.

Edward ScheinermanView profile

Professor

• Discrete Mathematics
• Graph Theory
• Random Methods
• +3 more

Edward Scheinerman is a Professor in the Department of Applied Mathematics and Statistics at Johns Hopkins University's Whiting School of Engineering. He currently serves as the Vice Dean for Special Projects, previously holding roles such as Vice Dean for Graduate Education and Chair of the Department of Applied Mathematics and Statistics. He is a Fellow of the American Mathematical Society and the Institute of Combinatorics and its Applications. His research focuses on discrete mathematics, including graph theory, random methods, and partially ordered sets. He is renowned for inventing random dot product graphs, which model social and biological networks. His work bridges theoretical mathematics with practical applications in networks and algorithms. Scheinerman authored multiple influential books, including Mathematics Lover’s Companion and textbooks like Invitation to Dynamical Systems and Mathematical Notation . He has received teaching awards such as the Distinguished Faculty Award and the Robert B. Pond, Sr. Excellence in Teaching Award. His academic journey includes a B.A. in Mathematics from Brown University (1980), and M.S. and Ph.D. from Princeton University (1981, 1984). He joined Johns Hopkins in 1984.

Laurent YounesView profile

Professor

• Computational Anatomy
• Medical Imaging
• Statistical Modeling
• +5 more

Laurent Younes is a Professor in the Department of Applied Mathematics and Statistics at Johns Hopkins University (JHU), affiliated with the Whiting School of Engineering. He leads research in computational anatomy, medical imaging, and statistical modeling, and is a member of the JHU Center for Imaging Science and the Data Science and AI Institute. His work focuses on shape analysis, deformation modeling, and applications in neurodegenerative diseases like Alzheimer’s and Huntington’s. Education: B.S. from École Normale Supérieure (1984), M.S. and Ph.D. from University of Paris XI (1985–1988). Research Interests : Developing mathematical frameworks for analyzing shapes and images, including diffeomorphisms, Markov Random Fields, and computational tools for biomedical applications. Key areas include brain atrophy analysis, longitudinal biomarker studies, and integrating molecular data with anatomical imaging. Awards : 2023 SIAM Fellow 2019 Johns Hopkins Discovery Award 2015 Fellow of the Institute for Mathematical Statistics Advising & Collaborations : Collaborates with JHU School of Medicine on transdiagnostic approaches for psychosis. Active in organizing workshops and conferences, including IMA and SIAM events. Editor for Annals of Applied Statistics . Technical Contributions : Authored Shapes and Diffeomorphisms (Springer, 2010), and pioneered 3D scene understanding tools for cardiology. Current work emphasizes cross-modal data integration and early disease biomarker detection.

Marni MishnaView profile

Professor

• Analytic Combinatorics
• Enumerative Combinatorics
• Algorithmic Solutions
• +5 more

Marni Mishna is a Full Professor in the Department of Mathematics at Simon Fraser University’s Faculty of Science. Her academic roles include Associate Dean, Equity, Diversity, and Inclusion (2023–2028) and PIMS Deputy Director (2019–2021). She has held visiting positions at institutions such as CNRS (France), LaBRI (University of Bordeaux), and the Fields Institute. Education: PhD in Mathematics (Université du Québec à Montréal, 2003), MSc in Pure Math (SFU, 2000), BMath (University of Waterloo, 1997). Her research spans analytic and enumerative combinatorics , focusing on generating functions , lattice paths , D-finite symmetric functions , and algorithmic solutions . She explores the intersection of combinatorics with art and design for mathematical communication. Recent articles analyze differential transcendence , Kronecker coefficients , and lattice path universality classes , emphasizing computational methods and asymptotic analysis. Her work often bridges graph theory , symmetric functions , and algebraic structures . She supervises students and postdocs in projects related to combinatorial theory , graph enumeration , and symmetric function applications . Her teaching includes advanced courses like MATH 443/743 (Combinatorics) and MATH 322 (Complex Analysis).

Gherardo VarandoView profile

Assistant Professor

• Uncertainty Quantification
• Probabilistic Graphical Models
• Graphical Models for Dynamical Systems
• +4 more

Gherardo Varando is an Assistant Professor at the University of Valencia's Department of Statistics and Operational Research, and a researcher in the Image and Signal Processing Group. His work focuses on causal inference, probabilistic graphical models, and their applications in Earth sciences and health data analysis. He holds a PhD and has been a faculty member since 2024. Research interests include uncertainty quantification, staged event trees, and spatio-temporal causal discovery. Varando has published extensively on topics like causal representation learning, staged tree models, and kernel methods for pairwise causal discovery. His contributions span machine learning theory, statistical methodology, and interdisciplinary applications in climate science and biomedicine. Key achievements include the development of the stagedtrees R package for structural learning of graphical models, and open-source tools like leaflet-map-builder for geospatial visualization. His work bridges theoretical advancements with practical implementations, addressing challenges in causal structure learning and interpretable AI.

Thomas PrellbergView profile

Professor

• Lattice Statistical Mechanics
• Enumerative and Asymptotic Combinatorics
• Dynamical Systems
• +2 more

Thomas Prellberg is a Professor of Mathematics at Queen Mary University of London (QMUL) and affiliated with Technical University Clausthal in Germany. His primary role is at the School of Mathematical Sciences, where he contributes to research and teaching in Mathematics. His research focuses on lattice statistical mechanics, enumerative and asymptotic combinatorics, dynamical systems, and Monte Carlo algorithms, with applications in soft condensed matter physics. Research Interests include polymer collapse transitions, vesicle models, and the statistical mechanics of lattice systems. He has supervised three PhD students: Anum Khalid (2014–2018), Nils Haug (2013–2017), and Paul Mortimer (2011–2015). His work often involves interdisciplinary collaborations, combining mathematical rigor with computational methods. He is part of the Centre for Complex Systems at QMUL, further emphasizing his engagement with complex systems research. Prellberg’s publications span topics from exact solutions of lattice models to Monte Carlo algorithm optimization. His research has advanced understanding of phase transitions, critical phenomena, and universality in polymer and vesicle systems. Despite no explicitly listed awards, his contributions reflect significant impact in theoretical and computational physics.

• Coding Theory
• Combinatorics
• Error-Correcting Codes
• +2 more

Anina Gruica is a Research Fellow in the Algebra group at the Technical University of Denmark (DTU), specializing in coding theory with emphases on density questions, combinatorial structures of error-correcting codes, and DNA-based data storage systems. Her work bridges theoretical mathematics and practical applications in next-generation storage technologies. Her research program centers on Coding Theory and Combinatorics, investigating the geometric and probabilistic properties of codes in metric spaces. Key interests include rank-metric codes, MRD codes, and combinatorial optimization for DNA storage efficiency, where she develops novel approaches to random access and coverage depth challenges through algebraic and geometric frameworks. Dr. Gruica's publication portfolio reveals a strong interdisciplinary trajectory, with increasing focus on DNA storage applications since 2022. Her work combines deep theoretical insights in combinatorial geometry with practical storage system design, resulting in high-impact publications across SIAM journals, IEEE conferences, and arXiv preprints that address both classical coding problems and emerging biological storage constraints. She has received competitive recognition including: ALCOCRYPT conference travel award (February 2023) DIAMANT PhD travel grant (2000 EUR, January 2023) DIAMANT visitor grant (1900 EUR, October 2021) SIAM Travel Award (August 2021) As co-organizer of the Postgraduate International Coding theory Seminar (PICS), Dr. Gruica actively mentors junior researchers while maintaining extensive collaborations with A. Ravagnani, J. Sheekey, and E. Yaakobi. Her conference presentations at venues like ISIT and SIAM AG23 demonstrate her leadership in translating theoretical advances to storage applications. She contributes to DTU's Algebra group research ecosystem, focusing on algebraic structures for coding theory and cryptography. Her current projects, evident from 2024-2025 preprints, explore combinatorial geometry for DNA storage efficiency and advanced rank-metric code constructions, positioning her at the forefront of coding theory's application to biological data systems.

• Fractals
• Geometric Measure Theory
• Metric Rectifiability
• +3 more

Tuomas Orponen is a Professor at the Department of Mathematics and Statistics, University of Helsinki, where he serves as a supervisor for the Doctoral Programme in Mathematics and Statistics. His research focuses on fractal geometry, geometric measure theory, and quantitative rectifiability, with particular emphasis on the interplay between geometric configurations and measure-theoretic properties in Euclidean and non-Euclidean spaces. He has led major research projects such as 'Incidences on Fractals' and 'Quantitative Rectifiability of Sets and Measures,' supported by the University of Helsinki. Orponen has published over 40 peer-reviewed articles and actively contributes to academic activities, including organizing conferences like 'Geometric Measure Theory and its Connections' and delivering invited talks at events like the Barcelona Analysis Conference 2019. His work bridges pure mathematics and applications in harmonic analysis and combinatorics, addressing questions about fractal structures, projection theorems, and metric dimensions. Orponen has collaborated internationally, visiting institutions such as the Institute of Mathematics of the Polish Academy of Sciences (IMPAN) and hosting researchers at the University of Helsinki. His research has advanced understanding in areas like Hölder-regular surfaces, self-similar measures, and continuous analogues of combinatorial problems.

• Computational Complexity
• Randomness and Computation
• Algorithms and Optimization
• +4 more

Avi Wigderson is the Herbert H. Maass Professor at the School of Mathematics, Institute for Advanced Study (IAS), Princeton. He leads the Computer Science and Discrete Mathematics (CSDM) activities at IAS, focusing on foundational questions in computational complexity, randomness, and their applications. His research bridges theoretical computer science, mathematics, and physics, addressing challenges such as P vs NP, quantum computing, and cryptographic security. Education: Ph.D. in Mathematics from Princeton University (1983), with prior roles including professorships at The Hebrew University of Jerusalem and visiting positions at Princeton University and UC Berkeley. Research interests span computational complexity theory, randomness in computation, cryptography, quantum computation, and pseudorandomness. He has pioneered work on derandomization, probabilistic proof systems, and the interplay between algebraic structures and computational efficiency. Awards include the 2023 ACM Turing Award, 2021 Abel Prize, Rolf Nevanlinna Prize (1994), and Gödel Prize (2009). His contributions to theoretical computer science and mathematics have profoundly impacted areas like algorithm design, cryptography, and computational geometry. Key contributions include work on pseudorandomness, proof complexity, and the role of randomness in computation. He has also explored applications of computational methods to mathematics, such as algebraic geometry and number theory.

Luc DevroyeView profile

Professor

• Computer Science
• Probability Theory
• Algorithms
• +4 more

Luc Devroye is a Professor in the School of Computer Science at McGill University, Montreal. He joined McGill in 1977 after completing his PhD at the University of Texas in 1976. His research spans probabilistic analysis of algorithms, random structures, and typography. Notable contributions include work on random trees, data structures, and simulation methods. Affiliations : McGill University since 1977, School of Computer Science. Education : PhD in Computer Science from the University of Texas (1976), studies at the University of Leuven and Osaka University. Research Interests : Algorithms, probability theory, random graphs, data structures, and typography. Focus on average-case analysis, random tree structures, and simulation techniques. His work emphasizes theoretical foundations with practical applications, including contributions to random number generation and algorithmic efficiency. He advises students in theoretical computer science and maintains an active research agenda with over 500 publications. Teaching : Courses like COMP 252 (Algorithms), COMP 690 (Probabilistic Analysis of Algorithms). Students : Mentored numerous PhD and MSc students, many now in academia and industry.