Prof. Tobias Müller is a Professor at the Bernoulli Institute for Mathematics, Computer Science and Artificial Intelligence at the University of Groningen. His academic journey includes previous positions at Utrecht University, CWI (Centrum Wiskunde & Informatica), Tel Aviv University, and Eindhoven University of Technology, with a doctorate from the University of Oxford under Colin McDiarmid. His research focuses on combinatorics, probability theory, random graphs, percolation, discrete and stochastic geometry, and combinatorial game theory. He has contributed extensively to understanding complex networks, hyperbolic models, and geometric random structures. Research Interests: Random Graphs and Percolation Theory Discrete and Stochastic Geometry Hyperbolic Network Models Probabilistic Combinatorics Geometric Probability Graph Algorithms and Connectivity Notable Contributions: Analysis of Voronoi and Poisson-Voronoi percolation in hyperbolic planes. Studies on Mallows random permutations and their cycle structures. Research on component games and logical limit laws in graph theory. Investigations into the geometry and properties of random geometric graphs. Grants & Collaborations: Active in organizing workshops and conferences on random graphs and geometric networks, including the BIRS Workshop on Random Geometric Graphs and the STAR Workshops series. Labs/Teams: Member of the Bernoulli Institute’s research groups, focusing on stochastic studies, combinatorics, and algorithmic methods.
Tamon Stephen is a Professor in the Department of Mathematics at Simon Fraser University (SFU), part of the Faculty of Science. His research focuses on operations research, with an emphasis on combinatorial optimization, algorithms, discrete geometry, and computational biology. He holds a Ph.D. in Mathematics from the University of Michigan (2002). His work often bridges theoretical and computational aspects, addressing interdisciplinary applications. Stephen is affiliated with the Centre for Operations Research and Decision Sciences (CORDS) and has contributed to software tools for hypergraph transversals and colorful linear programming. He has taught courses such as Math 208W (Introduction to Operations Research) and has advised projects in metabolic network analysis and scheduling optimization. His office is located at the Surrey campus (SRYC 2886). Key research collaborations include studies on firefighter scheduling, nurse rostering, and metabolic pathway analysis. His methodologies often leverage algorithm design, polytope theory, and discrete mathematics. Stephen actively participates in academic service, organizing seminars and contributing to conferences such as the West Coast Optimization Meeting. His work emphasizes practical applications of theoretical results, with a focus on solving real-world optimization challenges.
Daniel Horsley is an Associate Professor and ARC Future Fellow at the School of Mathematical Sciences, Monash University. His research focuses on combinatorial designs and edge decomposition of graphs, with notable contributions to extremal graph theory, Zarankiewicz problems, and graph decomposition theorems. Current Role: ARC Future Fellow, Associate Professor Affiliation: School of Mathematical Sciences, Monash University Active Projects: 'The Zarankiewicz problem through linear hypergraphs and designs' (2022–2025), 'Edge decomposition of dense graphs' (2017–2022), and more Research interests span combinatorial designs, graph decomposition, and extremal combinatorics. His work emphasizes theoretical advancements in design theory, with applications in discrete mathematics and optimization. Recent articles address semi-inducibility, Zarankiewicz numbers, and embedding partial designs, reflecting his expertise in structural and extremal combinatorics. Key awards include the ARC Future Fellowship. His research outputs include over 57 publications in journals like Journal of Graph Theory , SIAM Journal on Discrete Mathematics , and European Journal of Combinatorics . Grant projects include collaborations with ARC, University of Queensland, and University of Melbourne, focusing on Steiner systems, compressed sensing, and combinatorial structure analysis. Advising PhD students and mentoring researchers in discrete mathematics and combinatorial design theory.
Prof. Michael Krivelevich holds the Baumritter Chair in Combinatorics at the School of Mathematical Sciences, Tel Aviv University. His research focuses on probabilistic methods in combinatorics, random graphs, and positional games. He has authored influential books such as Positional Games and contributed to foundational work in random graph theory. Currently teaching Introduction to Combinatorics and Graph Theory (Spring 2025), he has extensive experience in courses like Graph Theory and Hypergraph Coloring. His work bridges theoretical computer science, coding theory, and combinatorics, with over 150 publications. Recent research explores game-theoretic thresholds, random graph evolution, and equitable coloring algorithms. Education: Ph.D. in Mathematics, Tel Aviv University (not explicitly stated, inferred from career trajectory). Research Interests Krivelevich's work emphasizes random structures , extremal graph theory , and probabilistic combinatorics . He investigates phase transitions in random graphs, positional game strategies, and algorithmic challenges in graph coloring. His contributions include proving sharp thresholds for Hamilton cycle games and analyzing WalkSAT performance on smoothed k-CNF formulas. Collaborations span theoretical computer science and discrete mathematics. Publications Recent articles address Hamiltonicity in Maker-Breaker games, equitable coloring of random graphs, and smoothed analysis of satisfiability processes. His work often combines rigorous proofs with algorithmic insights. Teaching & Mentorship Guides students through advanced combinatorial topics and has taught foundational courses since 2002. No explicit student listings available in provided texts.
Jimmy Huang is a Full Professor and Tier 1 York Research Chair in Big Data Analytics at the School of Information Technology, York University. His research focuses on information retrieval, AI, NLP, and big data analytics in healthcare and web systems. He has published 360+ papers in top venues like SIGIR and ACL, and leads grants totaling $4M+. Huang chairs IEEE's Technical Community on Intelligent Informatics and serves on numerous conference committees. Education: PhD in Information Science (City, University of London), M.Eng and B.Eng in Computer Science Roles: Chair of IEEE TCII, General Chair of SIGIR 2020 and CIKM 2008 Research interests span task-oriented IR, conversational search, healthcare analytics, and graph-based models. His work on hypergraph collaborative filtering (SIGIR 2022) was named a top influential paper. Current projects include NSERC Discovery Grants ($384K) and ADERSIM CREATE ($1.65M). Award highlights include Fellowships from ACM, IEEE, and Canadian Academy of Engineering. Supervised over 90 students, currently mentoring 12 PhD/MSc candidates and 3 postdocs. Active in surgical safety checklist research and medical data analytics. Labs include the IRLab focused on IR and NLP innovations. Major grants include ORF-RE ($3.5M), NSERC CREATE, and multiple CRD partnerships with industry.
Dr. Jingyun Wang is an Assistant Professor in the Department of Computer Science at Durham University. Previously, she held an Assistant Professor position at Kyushu University, Japan. Her primary affiliations include the Centre for Neurodiversity & Development and the Artificial Intelligence and Human Systems Group (AIHS), as well as the Pedagogical Innovation in Computer Science Group (PICS). She is a Fellow of the Higher Education Academy and has led or contributed to research projects funded by JSPS, JST, NICT, Innovate UK, and industry partners. Her research focuses on AI-driven educational technologies, including AI-based feedback systems, computational thinking education, game-based learning, and ontology techniques. She actively contributes to editorial boards (e.g., Computers & Education: Artificial Intelligence ) and serves as a conference chair for AIED, ICCE, and LTLE. Current research includes adaptive learning systems for mathematics education, serious games for cybersecurity training, and visualization tools for e-learning. Her scientific contributions span over 50 peer-reviewed publications, with recent work emphasizing learning analytics, multimodal systems, and digital health interventions. She advises multiple PhD students and mentors in professional recognition pathways. Key projects include developing the BETTER speech training system, the MEMORABLE cybersecurity game framework, and ontology-based language learning platforms.
Pietro Liò is Full Professor in the Department of Computer Science and Technology at the University of Cambridge, where he leads research in Artificial Intelligence and Computational Biology as part of the AI group and the Cambridge Centre for AI in Medicine. He holds additional affiliations as Fellow and Council member of Clare Hall College, member of Ellis (European Lab for Learning & Intelligent Systems), and member of Academia Europaea. Professor Liò earned dual PhDs in Complex Systems and Non Linear Dynamics from the University of Florence and in Theoretical Genetics from the University of Pavia, Italy. His educational background bridges theoretical computer science with biological sciences, forming the foundation for his interdisciplinary research approach. His research focuses on developing Artificial Intelligence and Computational Biology models to understand disease complexity and advance personalized medicine. Current work emphasizes Graph Neural Network modeling for integrating multi-scale, multi-omics, and multi-physics data; combining deep learning with mechanistic approaches; explainability in medical AI; and developing AI-based medical digital twins and personal decision support systems. His work spans from fundamental algorithm development to clinical applications, with particular emphasis on translating computational advances into medical solutions. Analysis of his recent publications reveals strong activity in geometric deep learning , explainable AI for healthcare , and multi-omics integration , with increasing focus on clinically applicable tools that maintain both predictive power and interpretability. Member of Academia Europaea Listed among Top Italian Scientists by VIA-Academy Professor Liò has mentored over 40 PhD students and postdoctoral researchers, including notable names such as Petar Velickovic, David Buterez, and Chaitanya Joshi. His research is supported through collaborations with the Cambridge Centre for AI in Medicine and various international partnerships. He serves on departmental committees including Student Complaints and Postdoc Mentoring, and has completed equality and diversity training essentials. He leads research within the Artificial Intelligence group at Cambridge, focusing on creating computational frameworks that bridge biological complexity with clinical applications through advanced machine learning techniques.
Hao Liu is a researcher affiliated with institutions like Chinese Academy of Sciences , Beihang University , and Stanford University . His work spans Computer Science , Artificial Intelligence , and Robotics . Key affiliations: National Space Science Center (Beijing), School of Astronautics (Beihang), Key Laboratory of Pervasive Computing (Tsinghua) Research interests include Machine Learning , Image Processing , Graph Neural Networks , and Wireless Communication Optimization His recent publications focus on: Advanced control systems for fuzzy models Medical imaging via hyperspectral analysis Transformer-based approaches in NLP and vision Quantum-safe and edge computing protocols
Pan Xu is a tenure-track assistant professor with joint appointments in the Department of Biostatistics & Bioinformatics, Department of Computer Science, and Department of Electrical & Computer Engineering at Duke University's Pratt School of Engineering. Prior to joining Duke, he was a Postdoctoral Scholar Research Associate at the California Institute of Technology, and he earned his Ph.D. in Computer Science from UCLA. His research bridges theoretical foundations with practical applications in machine learning and artificial intelligence. Dr. Xu's research focuses on developing computationally- and data-efficient machine learning algorithms with strong theoretical guarantees, particularly in reinforcement learning, optimization, and high-dimensional statistics. His work addresses two fundamental challenges in sequential decision-making: efficient exploration with minimal interactions and robustness against distributional shifts. His research spans theoretical algorithm design, practical implementation, and real-world applications in bioinformatics and healthcare. His publication record demonstrates consistent high-impact contributions to top-tier conferences including ICML, NeurIPS, ICLR, AAAI, and AISTATS. The research trends show a progression from foundational work in non-convex optimization and multi-armed bandits toward increasingly sophisticated frameworks for robust reinforcement learning, with particular emphasis on distributional robustness, efficient exploration strategies, and practical applications. His work often bridges theoretical guarantees with empirical validation. NSF award on approximate sampling based exploration for sequential decision making Whitehead Scholar award from Duke University School of Medicine PIMCO Postdoctoral Fellowship in Data Science UCLA Outstanding Graduate Student Research Award Rising Stars in Data Science by University of Chicago Best Paper Award for Queer In AI: A Case Study in Community-Led Participatory AI at FAccT 2023 Featured Certification for Wasserstein Distributionally Robust Policy Evaluation and Learning for Contextual Bandits at TMLR Oral Presentation award at AAAI 2024 Dr. Xu actively mentors students and researchers, seeking highly motivated individuals with strong mathematical backgrounds for Ph.D. programs in Biostatistics & Bioinformatics, Computer Science, and Electrical & Computer Engineering at Duke. He has received multiple research grants including an NSF award on approximate sampling based exploration for sequential decision making. His service to the academic community includes roles as area chair for NeurIPS, ICML, ICLR, and AISTATS, as well as action editor for Transactions on Machine Learning Research. His research group develops algorithms that address fundamental challenges in sequential decision-making, with applications spanning healthcare, bioinformatics, and multi-agent systems. Current research directions include distributionally robust reinforcement learning, efficient exploration strategies, and applications of graph neural networks to biological problems.
Professor Asaf Shapira is a faculty member in the Department of Theoretical Mathematics at Tel Aviv University's School of Mathematical Sciences. He has been actively contributing to combinatorics and graph theory research for over a decade, with numerous publications in top journals including Journal of the ACM, Advances in Mathematics, and Geometric and Functional Analysis. Professor Shapira's research focuses on extremal combinatorics, graph theory, and property testing. His work explores fundamental questions in Ramsey theory, hypergraph theory, and probabilistic methods in combinatorics. He has made significant contributions to the study of graph regularity, removal lemmas, and extremal problems in dense and sparse graphs. His recent publications demonstrate a consistent focus on theoretical aspects of combinatorics with connections to theoretical computer science. A notable trend is his work on developing polynomial bounds for various combinatorial theorems and exploring connections between combinatorial structures and computational complexity. His research often bridges pure mathematics with theoretical computer science applications. Professor Shapira teaches advanced courses at Tel Aviv University including Extremal Graph Theory, Basic Combinatorics, and seminars on specialized topics in combinatorics. His teaching spans undergraduate and graduate levels, reflecting his commitment to educating the next generation of mathematicians.
Tibor Szabó is a Professor in the Combinatorics and Graph Theory group at the Department of Mathematics, Freie Universität Berlin. He holds a PhD from The Ohio State University, advised by Ákos Seress. Prior to his current position, he held roles at McGill University, ETH Zürich, the Institute for Advanced Study (Princeton), and the University of Illinois (UIUC) as a J.L. Doob Research Assistant Professor. Research Interests: His work focuses on combinatorics and combinatorial optimization, including extremal problems, random structures and algorithms, pseudorandom graphs, positional games, and the combinatorics of linear programming. He explores tools from algebra, probability theory, and topology applied to combinatorics. Teaching: He teaches courses such as Algorithmic Combinatorics, Extremal Combinatorics, and runs the Combinatorics Seminar. His lecture notes include works on positional games and explicit constructions in extremal combinatorics. Students & Postdocs: Notable PhD advisees include Yamaan Attwa, Silas Rathke, Simona Boyadzhiyska, and Patrick Morris. Postdoctoral fellows include Olaf Parczyk and Anurag Bishnoi. His research has involved collaborations with over 50 co-authors. Funding & Grants: Supported by grants from the Swiss National Science Foundation (SNF) and German Research Foundation (DFG), focusing on topics like positional games and extremal graph theory.
Ben Li is an Associate Professor in the Department of Computer Science at the University of Manitoba, Faculty of Science. His research focuses on combinatorics and theoretical computer science, including combinatorial design theory (e.g., Steiner triple systems, BIBDs, difference sets), graph theory, and algorithm design for combinatorial optimization problems. He also explores approximation algorithms for NP-Hard problems and subclasses of such problems with polynomial solutions. Dr. Li teaches a wide range of courses, including COMP 1010 (Introduction to Computer Science), COMP 2080 (Analysis of Algorithms), and advanced graduate courses like COMP 7720 on approximation algorithms and combinatorial optimization. His academic contributions span both theoretical computer science and interdisciplinary archaeological studies, as reflected in his publications. His recent work includes studies on ostrich eggshell beads' role in prehistoric social networks, archaeological site analysis in southern Africa, and medical education reforms in Canada. While his primary research aligns with computer science theory, his published articles also highlight interdisciplinary interests in anthropology, archaeology, and healthcare policy.
Sebastian Cioaba is a Professor in the Department of Mathematical Sciences at the University of Delaware (UD), part of the College of Arts & Sciences. His research focuses on spectral graph theory, algebraic combinatorics, and their applications. He earned his Ph.D. from Queen’s University (2005) and joined UD in 2009 after postdoctoral work at UC San Diego and the University of Toronto. Cioaba has advised 8 Ph.D., 4 M.Sc., and numerous undergraduate researchers, with current advisees including John Byrne and Isabel Byrne. His work is supported by NSF, NSA, and international grants. Education - B.Sc. Mathematics & Computer Science, University of Bucharest (Undergraduate) - Ph.D. Mathematics, Queen’s University (2005) Research & Awards - 2024 College of Arts & Sciences Award - Co-editor of Discrete Mathematics and Linear Algebra and its Applications - Over 70 publications and two books: A Bridge to Advanced Mathematics (2023) and A First Course in Graph Theory and Combinatorics (2022, 2nd ed.) Teaching & Service - Organized conferences in discrete mathematics - Supervised over 25 undergraduate and high school students in research projects Advising - Current Ph.D. students: John Byrne, Isabel Byrne, Colby Sherwood - Notable past advisees include Vishal Gupta (Ph.D. 2025, Rochester) and Dheer Noal (Ph.D. 2022, Memphis postdoc)
Felipe Thomaz is an Associate Professor of Marketing at Saïd Business School, University of Oxford, and Deputy Director of the Oxford Future of Marketing Initiative. He holds a PhD in Marketing from the University of Pittsburgh and previously taught at the University of South Carolina. His research focuses on marketing strategy, AI ethics, illicit markets, and ESG integration, with notable contributions to frameworks like Ad Net Zero for net-zero advertising emissions. He collaborates with UN agencies, NGOs, and tech companies to address global sustainability goals and wildlife trafficking networks. Education: PhD in Marketing (University of Pittsburgh), MSc in Marketing & Finance (University of Pittsburgh), BSc in Animal Sciences (University of Florida). Research interests include digital marketing channels, brand performance via social networks, AI-driven marketing strategies, and conservation science linked to wildlife trade. His work bridges academia and industry, resulting in spinouts and IP transfers from Saïd Business School. Key projects include: Ad Net Zero: Global standard for reducing advertising emissions UN collaboration on wildlife trafficking through dark web analysis UNESCO partnerships on eliminating stereotypes in advertising His interdisciplinary approach spans marketing, mathematics, and conservation science, with publications in top journals like Journal of Marketing and Conservation Science and Practice .
Jindrich Zapletal is a Professor in the Department of Mathematics at the University of Florida, affiliated with the College of Liberal Arts and Sciences. He holds a Ph.D. from The Pennsylvania State University (1995), advised by Thomas Jech. His research focuses on set theory, mathematical logic, and their applications, with specializations in descriptive set theory, combinatorial set theory, and forcing techniques. He is a core member of the UF Logic and Set Theory Group, which organizes the annual South Eastern Logic Symposium (SEALS), funded by the NSF. He teaches advanced courses like Invariant Descriptive Set Theory and has advised numerous graduate students. His work frequently explores consistency results under ZF set theory, chromatic numbers of hypergraphs, and interactions between forcing and topology. He actively contributes to conferences and has co-authored influential textbooks and monographs in set theory and logic. Education: Ph.D., Mathematics, The Pennsylvania State University, 1995 Research Interests: Choiceless set theory, descriptive set theory, forcing with ideals, and combinatorial structures in set theory Professional Roles: Organizer of SEALS conferences, instructor for graduate-level logic courses Grants: NSF Award 1945890 and multiple University of Florida grants Zapletal’s research emphasizes foundational questions in set theory, particularly under weaker axioms like ZF+DC, and their implications for algebra, topology, and combinatorics. His recent work includes studies on chromatic numbers of geometric hypergraphs and the structure of sigma-ideals in Polish spaces. Collaborations span topics like permutation models, game determinacy, and measure-theoretic forcing.
