Partition Regularity Professors and Supervisors

Atif Abueidaمشاهده پروفایل

استاد

University of DaytonUnited States

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.

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Maria Jose Serna Iglesiasمشاهده پروفایل

استاد

Polytechnic University of CataloniaSpain

Maria Jose Serna Iglesias is a Full Professor at the Faculty of Informatics of Barcelona (FIB), Universitat Politècnica de Catalunya (UPC)-BarcelonaTech, where she is affiliated with the Department of Computer Sciences. She is a leading member of the ALBCOM research group, focusing on Algorithmics, Bioinformatics, Complexity, and Formal Methods, and serves as the coordinator of the PhD program in Computing. Her academic leadership and research excellence are central to UPC's contributions in theoretical computer science. Licenciado en Ciencias Matemáticas Licenciado en Informática Doctorat en Informàtica (Doctorate in Computing) Her research interests lie at the intersection of theoretical computer science and game theory, with a strong focus on algorithmic game theory , social network analysis , and computational complexity . She investigates influence propagation models, cooperative games, weighted voting systems, and graph algorithms. Her work often involves the design and analysis of algorithms for discrete structures, with applications in network science and decision-making systems. The analysis of her recent publications reveals a consistent trend in studying strategic interactions in networks, particularly through influence models and cooperative game theory . Her work spans both theoretical foundations—such as complexity and dimensionality of games—and practical applications in social networks, education, and synthetic data validation. The keywords across her articles highlight a deep engagement with discrete mathematics, optimization, and algorithmic decision theory. She has received scientific recognition, including competitive awards and active participation in scientific committees of major international conferences such as the International Conference on Algorithmic Decision Theory and the Discrete Mathematics Days. Premiada (Awarded) Scientific committee member for multiple international conferences Maria Jose Serna has led and participated in numerous competitive R+D+i projects, often in collaboration with other prominent researchers in the ALBCOM group. She has advised multiple students and researchers, contributing to the training of the next generation of computer scientists. Her collaborations span institutions in Spain and internationally, particularly in the domains of algorithms and game theory. She is a core member of the ALBCOM research group (Algorísmia, Bioinformàtica, Complexitat i Mètodes Formals) at UPC, which is a leading force in theoretical computer science in Spain. The group fosters interdisciplinary research in algorithms, complexity, and formal methods, with strong ties to mathematical and computational sciences.

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Florian Frankمشاهده پروفایل

مدرس ارشد

University of Erlangen–NurembergGermany

PD Dr. Florian Frank is Privatdozent (senior lecturer with full teaching licence) for Applied Mathematics at Friedrich-Alexander-Universität Erlangen-Nürnberg (FAU) and heads the Bavarian research project „Parallel mesh loading and partitioning for large-scale simulation“ . His expertise spans high-performance computing, phase-field and discontinuous Galerkin methods, digital-rock physics, and reactive transport in porous media. Education & career 2022 – Venia legendi (private lecturer), Mathematics, FAU 2019 – Dr. habil., Mathematics, FAU 2013 – Dr. rer. nat., Applied Mathematics, FAU 2008 – Graduate Mathematician, University of Frankfurt 2021-2022 (acting) W2 Professor Scientific Computing, FAU 2018-2021 (acting) W2 Professor Mathematical Modelling, FAU 2017-2018 Senior Postdoc, CAAM, Rice University, USA 2014-2017 Postdoc, CAAM, Rice University, USA Research interests Frank focuses on the development and analysis of numerical schemes for partial differential equations that govern multiphase, multicomponent and reactive processes in porous or biological media. Key themes include discontinuous Galerkin and finite-volume methods , physics-preserving discretizations , high-performance computing , and digital-rock-based pore-scale simulations . He couples phase-field approaches with (Navier–)Stokes, Cahn–Hilliard, Nernst–Planck and density-gradient equations to quantify flow, transport, colloid dynamics and interfacial phenomena. Recent publications reveal a clear trend toward data-driven modelling : convolutional neural networks are trained with direct numerical simulation data to predict permeability and diffusion coefficients from 3-D micro-CT images, while advanced preconditioners and regularization techniques accelerate multiphase thermodynamic computations. Awards & recognition 2020 – Emmy-Noether-Prize der Naturwissenschaftlichen Fakultät, FAU 2017 – Promotion to Senior Postdoctoral Research Associate , George R. Brown School of Engineering, Rice University Projects, tools & supervision Frank currently leads a Bavarian state-funded project on parallel mesh handling for large-scale simulations. Together with collaborators he maintains the open-source MATLAB/GNU Octave toolbox FESTUNG for discontinuous Galerkin methods. Since 2018 he has (co-)supervised ten BSc and MSc theses on topics ranging from Stokes preconditioning to enriched Galerkin shallow-water solvers, regularly serves as reviewer for more than a dozen international journals, and is guest editor of special issues in Computational Geosciences and Oil & Gas Science and Technology .

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Viresh Patelمشاهده پروفایل

مدرس

Queen Mary University of LondonUnited Kingdom

Dr. Viresh Patel is a Lecturer in Optimisation at the School of Mathematical Sciences, Queen Mary University of London. His research focuses on extremal and probabilistic combinatorics, graph polynomials, phase transitions, and approximation algorithms. University: Queen Mary University of London School: School of Mathematical Sciences Email: viresh.patel@qmul.ac.uk Research Interests: Patel’s work bridges combinatorics, graph theory, and statistical physics. Key areas include Hamiltonian cycles in dense graphs, algorithmic approaches to graph polynomials, and complexity analysis of physical models. Publication Trends: Recent articles address structural graph theory (cycle/path decomposition), computational complexity (Potts/Ising models), and approximation algorithms using probabilistic and Taylor series methods. Collaborations: Regular co-authorship with researchers like Guus Regts, Allan Lo, and Matthew Jenssen indicates strong interdisciplinary engagement.

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Niv Buchbinderمشاهده پروفایل

استاد

Max Planck Institute for MathematicsGermany

Prof. Niv Buchbinder is a faculty member in the Department of Statistics and Operations Research at the School of Mathematical Sciences, Tel Aviv University. His research centers on algorithmic solutions for combinatorial optimization in offline and online contexts, with significant contributions to primal-dual methodologies and algorithmic game theory. His academic background includes a Ph.D. in Computer Science from the Technion (2008) under Prof. Seffi Naor and an M.Sc. in Computer Science from the Technion (2003) under Prof. Erez Petrank. Key research areas encompass Combinatorial Optimization, Online Algorithms, Algorithmic Game Theory, Primal-Dual Methods, and Submodular Optimization, focusing on competitive analysis for problems like set cover, ad-auctions, and caching. Recent publications (2012-2015) reveal a concentrated effort in submodular optimization and online decision-making, with applications in advertising, resource allocation, and machine learning. These works consistently employ primal-dual frameworks to achieve strong competitive ratios in adversarial settings. Scientific recognition includes: Best Paper Award at ESA 2007 for “Online Primal-Dual Algorithms for Maximizing Ad-Auctions Revenue” Best Paper Award at FOCS 2011 for “A Polylogarithmic Competitive Algorithm for the k-Server Problem” No information is available regarding student advising or research grants. Similarly, details about laboratory facilities, research teams, or future projects are not provided in the source materials.

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Eugene Valkovمشاهده پروفایل

استادیار

Whitman CollegeUnited States

Eugene Valkov serves as a Stadtman Investigator and Head of the Messenger RNA Regulation and Decay Section at the National Cancer Institute's Center for Cancer Research. He also holds an Adjunct Investigator position at the Center for Structural Biology. His laboratory focuses on understanding the molecular mechanisms governing mRNA stability and decay, with particular emphasis on the CCR4-NOT deadenylation complex. Valkov received his D.Phil. in molecular biophysics from the University of Oxford, followed by postdoctoral research at the MRC Laboratory of Molecular Biology in Cambridge studying mRNA export and decay. He joined the Max Planck Institute in Tübingen as a staff scientist in 2014, becoming a group leader in 2017, before moving to the NCI in 2019. His research integrates biochemistry, structural biology (particularly cryo-EM), and computational approaches to build detailed mechanistic models of mRNA regulatory complexes. The lab has successfully reconstituted the complete human CCR4-NOT deadenylation complex and decapping machinery, enabling unprecedented precision in studying mRNA decay pathways. Recent work has focused on multivalent interactions in mRNA regulation, pathogen manipulation of host RNA decay, and the kinetics of mixed poly(A) tails. Valkov's publications demonstrate a consistent focus on the structural and biochemical mechanisms of mRNA decay, with recent high-impact papers in Nature Communications, Nature Structural & Molecular Biology, and other leading journals. His work bridges fundamental RNA biology with implications for cancer and therapeutic development. Stadtman Investigator appointment at NCI German Research Foundation (DFG) Walter Benjamin fellowship for lab member Filip Peković Japanese Society for the Promotion of Science (JSPS) fellowship for lab member Wataru Horikawa Valkov mentors a dynamic team including postdoctoral fellows (Yevgen Levdansky, Filip Peković, David Turner, Tanner Myers) and has trained numerous postbaccalaureate researchers. His lab collaborates extensively across NIH institutes and with external academic and industry partners, including Moderna for mRNA therapeutic development. The lab maintains state-of-the-art facilities for recombinant protein production, biochemical assays, and access to advanced cryo-EM instrumentation through the Center for Structural Biology.

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Stefano Vigognaمشاهده پروفایل

دانشیار

Campus Bio-Medico University of RomeItaly

Stefano Vigogna is an Associate Professor in the Department of Mathematics at the University of Rome Tor Vergata with significant contributions to theoretical machine learning. He is affiliated with the Rome Center on Mathematics for Modeling and Data Sciences (RoMaDS), focusing on the mathematical foundations of learning algorithms. His research expertise spans: Machine Learning Statistical Learning Theory Harmonic Analysis Professor Vigogna's publication record demonstrates deep theoretical work connecting advanced mathematics to machine learning. His research investigates the spectral properties, geometric structure, and convergence behavior of neural networks using functional analysis and harmonic analysis techniques. Notable publications include his 2022 ICML paper on multiclass learning with exponential convergence rates and numerous works exploring the mathematical properties of deep learning systems through reproducing kernel spaces. He teaches Statistica for the Master's program in Environmental Biology and Statistical Learning for the Master's program in Pure and Applied Mathematics, reflecting his dual expertise in mathematical theory and practical data science applications. Professor Vigogna maintains active collaborations with leading researchers including Lorenzo Rosasco and Ernesto De Vito, advancing our fundamental understanding of learning algorithms through rigorous mathematical analysis. His work represents an essential bridge between pure mathematics and the theoretical foundations of modern artificial intelligence.

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Dominique Orbanمشاهده پروفایل

استاد

Polytechnic School of MontrealCanada

Dominique Orban is a Full Professor in the Department of Mathematics and Industrial Engineering at Polytechnique Montréal. He holds a Ph.D. from FUNDP Namur and INP Toulouse and has established himself as a leading researcher in numerical optimization. His academic affiliations include the Institute for Data Valorization (IVADO) and the Decision Analysis Study and Research Group (GERAD). Professor Orban's research focuses on numerical mathematics, particularly continuous nonlinear optimization, nonlinear systems of equations, and numerical linear algebra. His work involves designing specialized numerical algorithms for optimization problems, with particular interest in degeneracy and ill-posed problems. His research spans theoretical development of algorithms, their implementation in software, and applications to real-world problems such as image reconstruction, optimal structure design, and optimization under differential constraints. Analysis of his recent publications (2021-2025) reveals a strong focus on developing practical optimization algorithms with theoretical guarantees. His work spans multiple areas including nonsmooth optimization, iterative methods for linear systems, regularization techniques, and software implementation in Julia. A notable trend is his increasing focus on developing open-source software tools that make advanced optimization methods accessible to practitioners. Over 160 publications including journal articles, conference papers, and technical reports Multiple publications in top optimization journals each year through 2025 Strong emphasis on both theoretical foundations and practical implementation Increasing focus on Julia-based optimization software development Professor Orban has successfully supervised 9 doctoral students and 13 master's students to completion, demonstrating his commitment to mentoring the next generation of researchers. His supervision style appears to balance theoretical depth with practical implementation skills, preparing students for both academic and industry careers. His research has been supported through various institutional and collaborative grants, enabling him to maintain an active research program with multiple ongoing projects. His contributions to the field include significant software developments such as Krylov.jl, JSOSuite.jl, and DCISolver.jl, which have made advanced optimization techniques more accessible to the broader scientific community. These tools reflect his philosophy of bridging theoretical optimization with practical computational implementation.

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Ramya Korlakai Vinayakمشاهده پروفایل

استادیار

University of South CarolinaUnited States

Ramya Korlakai Vinayak is the Dugald C. Jackson Assistant Professor in the Department of Electrical and Computer Engineering at the University of Wisconsin-Madison's College of Engineering. Her research bridges theoretical machine learning with human-centered AI applications. Education: Postdoctoral Researcher, Paul G. Allen School of Computer Science and Engineering, University of Washington Ph.D. and M.S. in Electrical Engineering, California Institute of Technology (Caltech) B.Tech. in Electrical Engineering, Indian Institute of Technology Madras (IIT M) Research Focus: Her work centers on machine learning foundations with emphasis on statistical inference, crowdsourcing, and human-AI collaboration. Key themes include modeling heterogeneous human preferences, developing robust out-of-distribution detection systems, and creating culturally-aware generative models. She pioneers methods for pluralistic alignment where AI systems accommodate diverse human values through ideal point modeling and adaptive feedback mechanisms. Publication Trends: Recent publications (2023-2025) reveal three converging trajectories: (1) Human-in-the-loop frameworks for reducing false positives in safety-critical systems, (2) Metric learning innovations using limited preference data, and (3) Critical examinations of cultural limitations in generative AI. Her work consistently addresses real-world challenges like cognitive overload in crowdsourcing and biases in face generation systems. Research Infrastructure: She leads an active research group with a dedicated lab website (https://ramyakv.github.io/) focusing on developing theoretically-grounded yet practically-deployable AI systems that respect human plurality.

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Oleg Davydovمشاهده پروفایل

استاد

Justus-Liebig-University GiessenGermany

Professor Oleg Davydov holds a Professorship for Numerical Analysis at the Department of Mathematics, University of Giessen, Germany. His research focuses on developing advanced numerical methods with strong theoretical foundations and practical applications. He maintains an active research program with numerous recent publications and international collaborations. Position: Professor of Numerical Analysis Institution: University of Giessen, Department of Mathematics Contact: Heinrich-Buff-Ring 44, 35392 Giessen, HRZ Room 117 Email: oleg.davydov@math.uni-giessen.de Homepage: https://oleg-davydov.de/ Professor Davydov's research interests center around meshless numerical methods, approximation theory, and computational mathematics. His primary focus areas include: Meshless Finite Difference Method - Developing robust meshless techniques that avoid the need for structured grids Finite Element Method - Particularly Bernstein-Bézier finite elements and specialized approaches for complex geometries Scattered Data Fitting - Creating efficient algorithms for approximating data on irregular domains Approximation Theory - Investigating theoretical properties of splines, radial basis functions, and other approximation tools His research has resulted in several software packages including mFDlab (Meshless Finite Difference Method), BBFEM (Bernstein-Bézier Finite Elements), and TSFIT (Two-Stage Scattered Data Fitting), demonstrating the practical implementation of his theoretical work. Analysis of Professor Davydov's recent publications shows a consistent focus on improving meshless methods, particularly in stencil selection, error analysis, and applications to complex problems. His work spans both theoretical developments (like error bounds and optimal approximation orders) and practical implementations (for fluid dynamics, manifold learning, and interface problems). A notable trend is the increasing sophistication of adaptive techniques and the handling of challenging geometries. Professor Davydov has supervised several doctoral students to completion, including: Gaelle Andriamaro Fabien Rabarison Abid Saeed Wee Ping Yeo His research group appears to maintain active collaborations with institutions worldwide, as evidenced by his extensive co-authorship network. The group focuses on developing both theoretical foundations and practical implementations of numerical methods, with particular attention to problems involving irregular domains, singularities, and complex geometries. Students in his group would gain experience in both theoretical analysis and software development for numerical methods.

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Agnieszka Świętosławskaمشاهده پروفایل

استادیار

University of LodzPoland

Dr. Agnieszka Świętosławska is an Assistant Professor in the Department of History of Painting and Sculpture at the Faculty of Philosophy and History, University of Lodz. With over 40 publications and active research profile, she specializes in 19th century Polish art with particular focus on Lviv's artistic milieu, genre painting, and art criticism during the partitions period. Her research interests span art history, Polish art, 19th century painting, genre painting, visual culture, iconography, biography, and social history. She has conducted extensive studies on artistic migration between Lviv and Vienna, the development of art criticism in Galicia, and the representation of national identity through visual means during the partition era. Analysis of her 15 most recent publications reveals a consistent focus on Polish art history with particular emphasis on the 19th century, especially the Lviv artistic community. Her work demonstrates expertise in art criticism, iconography, and the social context of artistic production during the partitions, with recurring themes of national identity, artistic migration, and the relationship between local artistic communities and broader European trends. Documented 2 unspecified achievements in university records Active participant in academic community with 19 documented activities As an active faculty member with regular office hours, Dr. Świętosławska contributes to academic life through teaching in the Department of History of Painting and Sculpture. Her work bridges historical scholarship with contemporary art historical methodology, providing valuable insights into Poland's artistic heritage during critical historical periods.

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