جست‌وجوی استاد و پژوهشگر

Eugene Zhangمشاهده پروفایل

استاد

• Computer Graphics
• Scientific Visualization
• Information Visualization
• +۷ مورد دیگر

Eugene Zhang is a Professor in the School of Electrical Engineering and Computer Science at Oregon State University. He holds a Ph.D. in Computer Science from the Georgia Institute of Technology and has been recognized with the NSF CAREER Award. His research is centered on computer graphics, scientific visualization, and geometric modeling, with applications in medical imaging, simulation, and artistic rendering. Ph.D., Computer Science, Georgia Institute of Technology, 2004 M.S., Computer Science, Ohio State University, 1995 M.S., Mathematics, Ohio State University, 1994 His research interests include computer graphics, scientific visualization, information visualization, and geometry processing. He pioneers work in vector and tensor field design, enabling more expressive and controllable visual content. His projects often involve interdisciplinary collaborations, helping scientists interpret complex data such as fluid dynamics, stress fields, and geophysical simulations. He also explores non-photorealistic rendering inspired by traditional Chinese art. The recent publications highlight a consistent trend in topological analysis of tensor and vector fields, scalable hypergraph visualization, and interactive design of complex geometric structures. His work frequently appears in IEEE VIS and SIGGRAPH venues, reflecting strong contributions to both foundational algorithms and applied visualization techniques. NSF CAREER Award (2006) Apprentice for Science and Engineering 10-Year Service Award (2018) Eugene Zhang has advised numerous Ph.D. and M.S. students, many of whom have gone on to careers in industry (e.g., Adobe, Amazon, Facebook, NVIDIA, Zoox) and academia. He is currently leading an NSF-funded project (IIS-2437424) on time-varying symmetric tensor fields. He collaborates closely with Dr. Yue Zhang on reading groups focused on graphics and simulation topics. He leads a research group focused on computer graphics and visualization, hosting regular reading sessions and mentoring graduate students in cutting-edge visualization techniques.

Maryanthe Malliarisمشاهده پروفایل

استاد

• Model Theory
• Mathematical Logic
• Classification of Theories
• +۸ مورد دیگر

Maryanthe Malliaris is a Professor in the Department of Mathematics at the University of Chicago. Her research focuses on model theory , particularly the classification of theories and interactions between model theory, set theory, and combinatorics. University of Chicago, Department of Mathematics Research Interests: Malliaris works on model theory, ultrafilters, and their applications to problems in set theory, graph theory, and learning theory. She has advanced understanding of Keisler's order, regularity lemmas, and cofinality spectrum problems. Scientific Contributions: Her recent work connects model theory to machine learning, including PAC learning and online learning. She has contributed to the classification of simple theories, topological model theory, and Ramsey-type combinatorics. Awards and Grants: NSF CAREER Award DMS-1553653 Gödel Research Prize Fellowship Sloan Research Fellowship NSF-BSF 2051825 NSF Grants DMS-1300634, DMS-1001666 Teaching: Courses include Math 277 and Math 309. Contact: Department of Mathematics, University of Chicago, 5734 S. University Avenue, Chicago, IL 60637. Email: malliaris@math.uchicago.edu . Office: 406.

Barbara Maenhautمشاهده پروفایل

دانشیار

• Combinatorics
• Graph Theory
• Combinatorial Design Theory
• +۳ مورد دیگر

Associate Professor Barbara Maenhaut is Head of Mathematics and Deputy Head of the School of Mathematics and Physics at the University of Queensland. She holds a PhD in Mathematics from the University of Queensland (1999) and a Bachelor of Mathematics (Honours) from the University of Waterloo. An award-winning educator, she teaches discrete mathematics and chairs the School Teaching and Learning Committee. Her research specializes in combinatorial design theory and graph theory, focusing on: Graph decompositions and factorizations Hypergraph theory and 1-factorizations Latin squares and combinatorial structures Cycle systems and Hamiltonian decompositions She leads projects on uniform hypergraph factorizations and infinite circulant graph decompositions. Her publications demonstrate sustained focus on combinatorial optimization, with recent work expanding into hypergraph theory and multidimensional decompositions. She maintains consistent collaboration networks, frequently co-authoring with Darryn Bryant and others on extremal graph problems. She has supervised multiple graduate students as principal advisor on topics including uniform cycle decompositions and perfect 1-factorizations. Current funded research includes the ARC Discovery Project 'Fractional decomposition of graphs and the Nash-Williams conjecture' (2024-2028). Past grants include 'Factorisations of graphs' (ARC Discovery, 2013-2015) and 'Virtual Transport Networks' (ARC Linkage, 2012-2014). She contributes to the Combinatorics research group and maintains computational resources for the Oberwolfach problem, with student projects on combinatorial transformations in manifold topology.

Danul K. Gunatillekaمشاهده پروفایل

مدرس

• Model Theory
• Generic Structures
• Amalgamation
• +۳ مورد دیگر

Danul K. Gunatilleka is a Lecturer in the Department of Mathematics at the University of Maryland, College Park. His research focuses on Model Theory, particularly on generic structures constructed via Fraïssé limits with strong substructure constraints. PhD : University of Maryland, 2019 Master of Science : Louisiana State University, 2012 Bachelors Degree : University of Colombo, Sri Lanka, 2010 Gunatilleka's work explores the intersection of Model Theory and generic structures, emphasizing amalgamation and joint embedding properties. His research applies Hrushovski's methods to control theories of Fraïssé limits, notably in Baldwin-Shi hypergraphs. His publications highlight trends in Model Theory, focusing on the classification of countable models and atomic models within Baldwin-Shi hypergraphs. These studies contribute to understanding strong substructures and their impact on model-theoretic properties. Gunatilleka teaches courses including Calculus I (Math 140), Calculus II (Math 141H), and Linear Algebra (Math 240). He previously served as a sole contact instructor for Math 113 and Math 120.

• Combinatorics
• Graph Theory
• Asymptotic Enumeration
• +۷ مورد دیگر

Brendan McKay is a Professor in the School of Computing at the Australian National University (ANU), where he conducts research at the intersection of mathematics and computing. His work is deeply rooted in combinatorics, graph theory, and probabilistic methods in discrete structures. His research interests span a wide range of topics in discrete mathematics, including random graphs , asymptotic enumeration , Hamiltonian cycles , planar and bipartite graphs , matrix theory , and computational combinatorics . His work often combines theoretical depth with algorithmic applications, particularly in the analysis of complex networks and discrete systems. The recent publications (2015–2025) reflect a sustained focus on asymptotic enumeration techniques, structural properties of graphs, and algorithmic challenges in graph isomorphism and distance queries. Key themes include the use of probabilistic tools (e.g., cumulant expansions, martingales), enumeration under degree constraints, and symmetry-preserving operations on maps. His collaborations frequently involve researchers such as Mikhail Isaev, Catherine Greenhill, and Qing Wang. Scientific Awards and Recognitions: Fellow, Australian Mathematical Society (2000 → …) Fellow, Australian Academy of Science (1997 → …) McKay has led and co-led several major research projects, including Deep Learning for Graph Isomorphism and Hypergraph models for complex discrete systems , demonstrating leadership in both theoretical and applied directions. He has not explicitly listed advisees, but his role as Principal Investigator (PI) on multiple projects indicates active mentorship and grant leadership. His work often involves interdisciplinary applications in bioinformatics and network science. He is associated with research groups and projects focused on graph algorithms , random discrete structures , and computational combinatorics , often in collaboration with the mathematical sciences community at ANU and beyond.

Andrew Thomasonمشاهده پروفایل

استاد

• Combinatorics
• Graph Theory
• Algorithms
• +۳ مورد دیگر

Professor Andrew Thomason is a faculty member at the University of Cambridge within the Department of Pure Mathematics and Mathematical Statistics . Holding the title of Professor of Combinatorial Mathematics , his research focuses on Combinatorics , Graph Theory , and Algorithms . His work spans extremal graph theory, hypergraph containers, and hereditary properties. Notable contributions include studies on graph minors, edge colorings, and spectral analysis of networks. Recent research trends highlight his exploration of multipartite hypergraph colorings, saturated matrices, and structural analysis of networks using weighted spectral distribution.

Sean Prendivilleمشاهده پروفایل

مدرس

• Additive Combinatorics
• Analytic Number Theory
• Combinatorial Number Theory
• +۱ مورد دیگر

Lecturer in Mathematics at Lancaster University, Sean Prendiville specializes in additive combinatorics and analytic number theory. His work bridges discrete Fourier analysis with problems in Ramsey theory and partition regularity. Institution: Lancaster University Academic Rank: Lecturer Research Interests: Additive combinatorics Analytic number theory Fourier analysis in number theory Nonlinear Diophantine equations Partition regularity Arithmetic Ramsey theory Publication Trends: Recent works (2024-2023) focus on quantitative bounds for nonlinear Roth-type configurations, multidimensional extensions, and Fourier uniformity in Sidon sets. Earlier contributions span polynomial Szemerédi theorems, matrix progressions, and mean value estimates in Weyl sums. Academic Activities: Co-organizer of the Webinar in Additive Combinatorics during the pandemic, editorial board member of the Proceedings of the Royal Society of Edinburgh Section A: Mathematics , and contributor to lecture notes on Fourier analysis in combinatorics and Ramsey theory.

Duncan Adamsonمشاهده پروفایل

مدرس

• Combinatorics on Words
• Algorithms for Combinatorial Objects
• Crystal Structure Prediction
• +۷ مورد دیگر

Duncan Adamson is a Lecturer in the School of Computer Science at the University of St Andrews, United Kingdom. He has held prior research positions at the Leverhulme Research Centre for Functional Materials Design, the University of Göttingen, Reykjavik University, and the University of Liverpool. His academic foundation includes a PhD from the University of Liverpool supervised by Prof. Igor Potapov and undergraduate studies at the University of Glasgow. His research lies at the intersection of theoretical computer science and discrete mathematics, with a focus on combinatorics on words , algorithm design , crystal structure prediction , and temporal graphs . He explores symmetry in multidimensional words, develops algorithms for combinatorial enumeration, and investigates computational hardness in materials science. His work often bridges abstract theory with real-world applications in chemistry and robotics. The recent publications show a strong trend in combinatorial algorithms , particularly in enumeration, ranking, and optimization over structured words, graphs, and sequences. A significant portion of his work involves the k-centre problem for implicitly defined combinatorial classes and temporal graph colouring , with increasing emphasis on algorithmic complexity and practical implementation. His 2024 paper on harmonious colourings in temporal matchings was awarded best paper at SAND 2024, highlighting the impact of his research. Scientific Awards: Best Paper Award, SAND 2024 – 'Harmonious Colourings of Temporal Matchings' Duncan Adamson has been supported by the Leverhulme Research Centre for Functional Materials Design during his PhD and postdoctoral work. While no formal advisees are listed, his supervision by leading researchers and collaborative projects suggest active mentorship and team integration. He teaches core computer science modules at St Andrews, including CS2001 and CS3302, and maintains a busy research schedule with weekly meetings and supervisory responsibilities. His research is conducted within the Algorithms and Complexity group at St Andrews, continuing collaborations with international teams in Iceland, Germany, and beyond. Projects often involve interdisciplinary efforts, especially in applying computational techniques to materials science and chemistry.

• Machine Learning
• Artificial Intelligence
• Data Science
• +۷ مورد دیگر

Pan Xu is a tenure-track Assistant Professor at Duke University with joint appointments in the Department of Biostatistics & Bioinformatics , Department of Computer Science , and Department of Electrical & Computer Engineering . He earned his Ph.D. in Computer Science from the University of California, Los Angeles (2021) and completed postdoctoral training at the California Institute of Technology (2021-2022) . Research Focus: Machine Learning, Reinforcement Learning, Optimization, and High-Dimensional Statistics with applications in Bioinformatics and Healthcare. Key Contributions: Developing algorithms for robust sequential decision-making under uncertainty, improving Thompson Sampling efficiency, and advancing multi-agent reinforcement learning. Scientific Recognition: NSF award for exploration in decision-making (2023) Whitehead Scholar award (2023) PIMCO Postdoctoral Fellowship (2022) Best Paper Award at ACM FAccT (2023) Teaching: Offers graduate courses in machine learning and decision-making frameworks. Service: Serves as Area Chair for ICML, NeurIPS, and AAAI; Action Editor for TMLR.

Daniel Tenbrinckمشاهده پروفایل

استاد

• Data Science
• Machine Learning
• Biomedical Imaging
• +۶ مورد دیگر

Daniel Tenbrinck is an Academic Councilor and Acting Professor (W3) at the Department of Data Science, Faculty of Natural Sciences, Friedrich-Alexander-Universität Erlangen-Nürnberg (FAU Erlangen-Nürnberg). His career spans roles at the University of Münster, ENSICAEN (France), and FAU Erlangen-Nürnberg. He holds a PhD in Computer Science (2013) from WWU Münster. Research Focus: Data Science, Machine Learning, Biomedical Imaging, Variational Methods, Graph Theory, and Numerical Analysis. Recent Publications: Explore hypergraph p-Laplacians, Fourier neural operators for image classification, and Bregman learning frameworks for sparse networks. Awards: Received the 2022 Teaching Award from FAU's Faculty of Natural Sciences and a 2021 performance bonus for outstanding contributions. Secured significant third-party funding, including a €1.98M BMBF grant for "COMFORT" (2024) and a €915k Bavarian Digitalization grant (2023). Teaching: Offers courses in Numerics, Mathematical Image Processing, Inverse Problems, and Data Science seminars. Actively involved in academic program development through grants like the 2023 Innovation Fund for Teaching.

Robert Janczewskiمشاهده پروفایل

دانشیار

• Graph Theory
• Algorithmic Graph Theory
• Chromatic Graph Theory
• +۶ مورد دیگر

Dr hab. inż. Robert Janczewski serves as an Associate Professor at the Department of Algorithms and Systems Modelling within the Faculty of Electronics Telecommunications and Informatics at Gdańsk University of Technology. He simultaneously holds the position of Senior Specialist – Design Programmer at the Team for Development and Maintenance of Data Warehouses CUI, both positions located in Building A of the Faculty of Electronics, room EA 224. His academic journey includes obtaining a "dr inż." degree in 2001 and a higher doctoral degree "dr hab. inż." in Information Science (Technology) in 2015, both from the same faculty. His research focuses on the theoretical foundations of graph theory, with particular emphasis on various coloring problems including edge coloring, incidence coloring, and contrast coloring of graphs. His work spans both classical and signed graph theory, exploring chromatic indices, backbone coloring, and frequency allocation problems. Janczewski's research has significant applications in algorithm design, network optimization, and theoretical computer science, with many of his contributions addressing NP-hard problems and developing polynomial-time solutions for specific graph classes. Analysis of his recent publications (2022-2025) reveals a concentrated research trajectory on signed graph coloring problems, where he has made substantial contributions to understanding edge coloring of signed graphs and their products. His work demonstrates a progression from foundational coloring problems toward more complex signed graph structures, establishing classification systems for signed graphs based on their chromatic properties. The interdisciplinary nature of his research bridges pure mathematics with practical computational applications. Janczewski has maintained a consistent publication record in prestigious journals including Discrete Applied Mathematics, Theoretical Computer Science, and Discussiones Mathematicae Graph Theory. His collaborative work with researchers like Turowski, Wróblewski, and Obszarski indicates strong research partnerships within the graph theory community. His methodology combines theoretical proofs with algorithmic approaches, often providing both complexity analyses and practical solution strategies for graph coloring problems.

• Reinforcement Learning
• Stochastic Optimization

Daniil Nikolaevich Tyapkin is a Junior Research Fellow and Postgraduate Student at the National Research University Higher School of Economics (HSE), affiliated with the Faculty of Computer Science. He works within the Institute of Artificial Intelligence and Digital Sciences and the International Laboratory of Stochastic Algorithms and Multidimensional Data Analysis under the supervision of Alexey Alexandrovich Naumov. Education: 2021: Bachelor's degree in Applied Mathematics and Computer Science from HSE Currently in 2nd year of postgraduate studies with dissertation topic: "Randomized trial for reinforcement learning" Research Interests: His work focuses on reinforcement learning and stochastic optimization, including exploration strategies, generative flow networks, and theoretical analysis of learning algorithms. He develops efficient methods for complex decision-making problems with applications in AI and data science. Publications: His research appears in top venues (ICLR, NeurIPS, ICML, AISTATS) covering reinforcement learning, optimization, and probabilistic modeling. Recent works explore GFlowNets, entropy regularization, and high-probability bounds for learning algorithms. Awards & Honors: Letter of gratitude from HSE Faculty of Computer Science (2022) Publication bonus for List A journal articles (2023-2024) HSE Personnel Reserve - New Researchers category (2022-2023) Teaching & Advising: Teaches courses in Mathematical Statistics and Matrix Computations. No formal student advising mentioned. Laboratory Affiliation: Member of the International Laboratory of Stochastic Algorithms and Multidimensional Data Analysis, focusing on algorithmic development for machine learning challenges.

József Baloghمشاهده پروفایل

استاد

• Graph Theory
• Extremal Combinatorics
• Additive Combinatorics
• +۳ مورد دیگر

Prof. József Balogh is a Professor in the Department of Mathematics at the University of Illinois. His research focuses on graph theory, extremal combinatorics, and additive combinatorics, with significant contributions to the container method, including foundational work on independent sets in hypergraphs. He has held prestigious awards such as the George Pólya Prize in Combinatorics (2016) and multiple fellowships including the Simons and Marie Curie Fellowships. Education: PhD in Mathematics from the University of Memphis (2001). Research Interests: Balogh's work bridges theoretical and applied combinatorics. His container method has been pivotal in resolving conjectures in Ramsey theory, discrete geometry, and theoretical computer science. Recent articles explore sharp thresholds in random antichains, constructor-blocker games, and maximum clique-free subgraphs. Awards: NSF CAREER Award (2008–2013) Simons Fellow (2013–2014) Marie Curie Fellow (2013–2017) AMS Fellow (2023) Labs/Teams: Active collaborations in combinatorics and discrete mathematics, with over 200 research outputs and 40+ papers leveraging his container method. His work emphasizes probabilistic and counting variants in graph theory.

Youngtak Sohnمشاهده پروفایل

استادیار

• Probability Theory
• Mathematical Statistics
• Theoretical Computer Science
• +۵ مورد دیگر

Youngtak Sohn is an Assistant Professor in the Division of Applied Mathematics at Brown University since July 2024. Previously, he was a postdoctoral researcher at MIT Mathematics, mentored by Elchanan Mossel and Nike Sun through the NSF/Simons Collaboration on Theoretical Foundations of Deep Learning. He earned his PhD in Statistics from Stanford University (2021) under Amir Dembo and a BSc in Mathematics from Seoul National University (2016). His research focuses on probability theory and its intersections with mathematical statistics, theoretical computer science, and statistical physics. Key areas include high-dimensional statistics, random constraint satisfaction problems, spin glass theory, and the analysis of stochastic networks. Recent work explores phase transitions in estimation problems, universality of max-margin classifiers, and solution geometry of random constraint satisfaction problems. He has taught courses at Brown (APMA 2640) and MIT (Theoretical Computer Science seminar), and served as a teaching assistant for numerous Stanford courses including stochastic processes, statistical inference, and regression analysis.

Chuxu Zhangمشاهده پروفایل

دانشیار

• Machine Learning
• Data Science
• Graph Neural Networks
• +۵ مورد دیگر

Chuxu Zhang is an Associate Professor of Computer Science and Engineering at the University of Connecticut (UConn), affiliated with the College of Engineering's School of Computing. He directs the Machine Intelligence and Data Science (MINDS) Lab, focusing on resource-efficient machine learning and graph-based AI applications. His work bridges foundational research and societal impact, including healthcare, social media, and public health. Education: PhD in Computer Science and Engineering from the University of Notre Dame (2017–2020), advised by Prof. Nitesh Chawla. Previously, he was an Assistant Professor at Brandeis University (2020–2024). His research emphasizes graph neural networks, large language models, and data-efficient learning. Awards include the NSF CAREER Award (2024), Frontiers of Science Award (2024), and AAAI New Faculty Highlight (2023). He actively organizes workshops like RelKD and RelWeb on resource-efficient learning. Key grants include NSF funding for community resilience and opioid misuse detection. Key contributions span over 50 papers in top venues (ICML, NeurIPS, KDD). The MINDS Lab has 8 current PhD students and focuses on ethical, robust, and interpretable AI systems. Future work includes advancing graph foundation models and LLM integration in healthcare.