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Yufei ZhaoView profile

Associate Professor

• Combinatorics
• Extremal combinatorics
• Probabilistic combinatorics
• +4 more

Yufei Zhao is an Associate Professor of Mathematics at the Massachusetts Institute of Technology (MIT). His research focuses on extremal, probabilistic, and additive combinatorics, with applications to graph theory, discrete geometry, and computer science. Dr. Zhao received his S.B. in Mathematics and Computer Science and Engineering from MIT in 2010, followed by an M.A.St. in Mathematics with Distinction from Cambridge University in 2011. He completed his Ph.D. in Mathematics at MIT in 2015 under the supervision of Jacob Fox. His research interests span a broad range of combinatorial mathematics, with particular emphasis on the interplay between structure and randomness. Dr. Zhao has made significant contributions to extremal graph theory, additive combinatorics, and the theory of pseudorandom graphs. His work often connects different areas of mathematics through innovative applications of combinatorial methods. Dr. Zhao's publications demonstrate a consistent focus on fundamental problems in combinatorics, with recent work exploring equiangular lines, spherical codes, extremal set theory, and the connections between graph theory and additive combinatorics. His research has been recognized with prestigious awards including the Fulkerson Prize (2024), NSF CAREER award (2021), Sloan Research Fellowship (2019), and Dénes König Prize (2018). Fulkerson Prize (2024) NSF CAREER award (2021) Sloan Research Fellowship (2019) Dénes König Prize (2018) Dr. Zhao actively mentors students, currently advising Travis Dillon, Dingding Dong, and Nitya Mani. His former PhD students include Benjamin Gunby, Jonathan Tidor, Aaron Berger, Ashwin Sah, and Mehtaab Sawhney. He has also authored the influential textbook "Graph Theory and Additive Combinatorics: Exploring Structure and Randomness" (Cambridge University Press, 2023), which has received high praise from leading mathematicians including Terry Tao and Ben Green.

Atif AbueidaView profile

Professor

• Graph Theory
• Design Theory
• Combinatorics
• +5 more

Atif Abueida is a Professor and Chair of the Department of Mathematics in the College of Arts and Sciences at the University of Dayton. He has been a full-time faculty member since 2000 and also contributes to the Premedical Programs by teaching relevant mathematics courses. His research focuses on discrete mathematics, particularly in Graph Theory , Design Theory , and Complex Analysis . He has made significant contributions to the study of graph decompositions, multidecompositions, cycle extendability, Hamiltonian cycles in chordal graphs, and equitable colorings in combinatorial designs. The recent publications, spanning from 2010 to 2025, highlight a sustained research trajectory in combinatorics and graph theory, with increasing focus on Johnson graphs, starlike trees, and structural properties of chordal and multipartite graphs. His work often involves deep combinatorial analysis and structural investigations, with applications in design theory and graph labeling. Dr. Abueida has delivered numerous invited and contributed talks at national conferences such as the Joint Mathematics Meetings and the Cumberland Conference, demonstrating active engagement with the mathematical community. Though specific student names are not listed, his role as a professor and advisor is evident from his long tenure and research output. Ph.D. in Mathematics, Auburn University, 2000 M.S. in Mathematics, East Tennessee State University (ETSU), 1996 B.Sc. in Mathematics, UAE University, 1987 He previously worked as a high school mathematics teacher for eight years before pursuing his graduate studies. His scholarly service includes reviewing and collaborative research, as seen in co-authored publications with colleagues across institutions.

Wilfrid GangboView profile

Professor

• Calculus of Variations
• Nonlinear Analysis
• Partial Differential Equations
• +7 more

Wilfrid Gangbo is a Professor of Mathematics at UCLA, specializing in nonlinear analysis, partial differential equations, and calculus of variations. He received his Ph.D. from EPFL (Switzerland) in 1992 and maintains active research in mathematical physics and optimization theory. Research interests focus on: Calculus of variations and nonlinear PDEs Optimal transport theory and Wasserstein spaces Mean field games and kinetic theory Functional analysis with applications to fluid mechanics Recent publications demonstrate strong focus on: Hamilton-Jacobi equations in metric spaces Structure of Wasserstein spaces Connections between optimal transport and game theory Regularity theory for polyconvex energies Professional activities include founding EcoAfrica, an organization supporting mathematical sciences in African countries through workshops and collaborative projects since 1990. Current teaching includes advanced mathematics courses such as Math 131BH (Winter 2025).

Karen GundersonView profile

Associate Professor

• Graph theory
• Combinatorics
• Random graphs
• +10 more

Karen Gunderson is an Associate Professor in the Department of Mathematics at the University of Manitoba's Faculty of Science. Her research spans graph theory, combinatorics, random graphs, percolation, hypergraphs, and extremal combinatorics. Research Focus : Graph theory, combinatorics, random graphs, percolation, hypergraphs, extremal combinatorics Academic Role : Associate Professor, Acting Associate Head Graduate Contact : Karen.Gunderson@umanitoba.ca , karen.gunderson@umanitoba.ca Her work includes bootstrap percolation , random geometric graphs , and extremal hypergraph problems , with applications in network modeling and probabilistic combinatorics. Recent publications focus on adversarial burning densities, Erdos-Ko-Rado robustness, and Turán numbers in switching contexts. Academic Leadership : Co-organizer of the University of Manitoba Combinatorics Seminar and key organizer for the 2023 CanaDAM conference and Movement & Symmetry in Graphs retreat.

Nicolas Garcia TrillosView profile

Associate Professor

• Applied Analysis
• Computational Probability
• Statistics
• +5 more

Nicolas Garcia Trillos is an Associate Professor in the Department of Statistics at the University of Wisconsin-Madison, with research spanning applied analysis, computational probability, statistics, and machine learning. His work focuses on the intersection of calculus of variations, optimal transport, and partial differential equations in learning systems. His educational background includes: Bachelor's degree in Mathematics from Universidad de Los Andes, Bogotá, Colombia (2010) Ph.D. in Mathematics from Carnegie Mellon University (2015) Prager Assistant Professor (postdoctoral position) at Brown University (2015-2018) Garcia Trillos' research centers on the mathematical foundations of machine learning , developing tools in optimal transport and calculus of variations to analyze graph-based learning and continuum models. Key contributions address adversarial robustness , federated learning , and spectral clustering , providing theoretical guarantees for large-scale problems through PDE frameworks. Recent publications (2022-2025) demonstrate a cohesive focus on geometric approaches to adversarial robustness , using optimal transport to derive classification bounds and study solution existence. He has advanced federated learning via consensus-based bi-level optimization (CB2O) and developed Fermat distances for metric approximation, bridging pure mathematics with statistical learning theory. No scientific awards were mentioned in the provided text. There is no information available regarding student advising, research grants, or laboratory affiliations in the source material.

Janoš VidaliView profile

Assistant Professor

• Cryptography
• Graph Theory
• Discrete Geometry
• +3 more

Assistant Professor at the University of Ljubljana's Faculty of Computer and Information Science (FRI) specializing in theoretical computer science and discrete mathematics. His research bridges cryptography, graph theory, and metric geometry with practical applications in algorithm design and security protocols. Research Focus: Primary interests include cryptography (hash function analysis), graph theory (combinatorial structures, graph databases), and discrete geometry (metric cones, polyhedral combinatorics). His work demonstrates strong interdisciplinary connections between theoretical mathematics and computational problem-solving. Publication Trends: Recent publications reveal a consistent focus on combinatorial structures with applications in both cryptography and geometry. His research combines rigorous mathematical proofs with computational implementations, particularly evident in metric space analysis and graph-theoretical frameworks. Projects: Leads significant open-source initiatives including gap-graphs (computational graph theory) and GraphZOO (graph database), alongside the Kriptogram crypto portal for cryptographic education in Slovene. Teaching: Maintains office hours by appointment with focus on advanced mathematical and cryptographic concepts. Integrates research directly into teaching through computational projects and open-source tools development.

• Computer Science
• Multidimensional Data Visualization
• Visualization Quality Assessment
• +4 more

Dr. Rasa Karbauskaitė is a Researcher at the Cognitive Computing Group within Vilnius University's Institute of Data Science and Digital Technologies . She holds a Doctor of Computer Science degree (2010) and specializes in multidimensional data visualization, dimensionality reduction, and intrinsic dimension estimation. Her work combines geometric and statistical methods to analyze high-dimensional datasets. PhD: Computer Science (2010), focusing on local structure preservation in multidimensional data visualization Advanced Training: B2.1 English language course (2016) Research interests include: Fractal dimension analysis for speech emotion classification Manifold learning and topological preservation Optimization of maximum likelihood estimators for dimensionality reduction Geodesic distance applications in data structure analysis Nonlinear data projection algorithms Scientific contributions show a focus on Developing visualization quality assessment frameworks Advancing dimensionality reduction techniques Fractal-based feature selection for emotion recognition Comparative analysis of intrinsic dimension estimation methods Parameter optimization in manifold learning algorithms Awards : Lithuanian Academy of Sciences Young Scientists' Research Prize (2011) Professional Roles : Managing Editor of the Informatica journal Participant in international conferences like Data Analysis Methods for Program Systems (2011-2015) Contributor to IEEE proceedings and specialized workshops

• Machine Learning
• Non-parametric Methods
• Computational Methods
• +6 more

Yifan Chen is an Assistant Professor in the Department of Computer Science and affiliate faculty in the Department of Mathematics at Hong Kong Baptist University's Faculty of Science. He joined HKBU in Fall 2023 after completing his PhD in Statistics from the University of Illinois Urbana-Champaign in 2023 under the guidance of Prof. Yun Yang. His educational background includes: B.S. in Statistics from Fudan University (2018), advised by Prof. Juan Shen and Prof. Chenghong Zhang Ph.D. in Statistics from University of Illinois Urbana-Champaign (2023), advised by Prof. Yun Yang Dr. Chen's research focuses on developing efficient algorithms for machine learning, with particular emphasis on non-parametric models and neural networks featuring intensive matrix operations. His work bridges statistical theory with practical computational challenges in modern machine learning systems, especially those involving Transformers (language models) and Graph Neural Networks (GNNs). He approaches machine learning from both theoretical and applied perspectives, seeking to understand statistical structures while addressing real-world computational constraints. His publication record shows consistent output in top-tier venues including ICML, NeurIPS, KDD, and EMNLP, with recent work spanning graph coarsening, optimal transport, efficient language model fine-tuning, and causal inference. His research demonstrates strong mathematical foundations combined with practical applications in AI systems. Among his notable achievements: NSFC Young Scientists Fund (2025) GDSTC General Program funding (2024) RGC Early Career Scheme proposal grant (2024) ICML 2023 Grant Award ($1,500) Dr. Chen actively mentors students through his research group, supervising PhD students and visiting research assistants. He has successfully guided students who have gone on to PhD programs at institutions including Institute of Science Tokyo, HKU, Fudan, and NUS. His teaching includes COMP 7070 Advanced Topics in Artificial Intelligence and Machine Learning, which covers core machine learning concepts for AI application research, and COMP 2027 Applied Linear Algebra for Computing. His research group focuses on efficient machine learning algorithms, with current projects spanning graph neural networks, optimal transport, language model efficiency, and causal inference. He collaborates with researchers from institutions including UIUC, Fudan University, and industry labs like Amazon Alexa AI.