جستجوی استاد راهنمای دانشگاهی

Jindrich Zapletalمشاهده پروفایل

استاد

• Set Theory
• Mathematical Logic
• Descriptive Set Theory
• +۳ مورد دیگر

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.

• Algebraic Combinatorics
• Polytope Theory
• Ehrhart Theory
• +۵ مورد دیگر

Yuhan Jiang is a Research Fellow in the Department of Mathematics at the University of California, Berkeley, where they work under the mentorship of Professor Sylvie Corteel. Previously, Jiang completed their PhD at Harvard University under the supervision of Professor Lauren K. Williams. Their academic address is 931 Evans Hall, Berkeley. Education: PhD in Mathematics from Harvard University, advised by Professor Lauren K. Williams Dr. Jiang specializes in algebraic combinatorics, with research spanning polytope theory, matroid theory, and algebraic statistics. Their work connects combinatorial structures with algebraic geometry and representation theory, particularly focusing on coinvariant rings, poset dynamics, and Ehrhart theory. Jiang's research demonstrates a strong interplay between discrete mathematics and geometric structures, often revealing deep connections between seemingly disparate areas of mathematics. Analysis of Jiang's recent publications shows a consistent focus on combinatorial structures with geometric interpretations. Their work on alternating diagonal coinvariants, echelonmotion, and positroid polytopes demonstrates expertise in connecting algebraic combinatorics with geometric reasoning. The publications reveal a progression from foundational combinatorial structures to applications in probability (through Markov chain analysis) and statistics (through algebraic statistical models). A notable trend is Jiang's ability to bridge theoretical combinatorics with concrete computational results, as seen in their work on Ehrhart series and k-ellipses. Dr. Jiang teaches Math 185: Complex Analysis for Fall 2025 at UC Berkeley, indicating active participation in the department's educational mission. While specific grant information isn't provided in the available text, their productive research output across multiple mathematical domains suggests successful funding support for their scholarly activities.

Lilya Budaghyanمشاهده پروفایل

استاد

• Boolean Functions
• Cryptography
• Information Security
• +۳ مورد دیگر

Lilya Budaghyan is a Professor at the Department of Informatics, University of Bergen, Norway, leading the Boolean functions research team at the Selmer Center. She holds a PhD from the University of Magdeburg (2005) and a habilitation from Paris 8 University (2013). Her research focuses on Boolean functions, particularly APN (Almost Perfect Nonlinear) functions and their cryptographic applications, with contributions to hardware implementations, equivalence relations, and cryptographic protocol design. She has authored the book *Construction and Analysis of Cryptographic Functions* and received awards including the TMS Starting Grant (2016) and Emil Artin Junior Prize (2011). Her work includes projects like the EU-funded BoolTI (2021–2025) and collaborations across institutions in Armenia, Germany, Italy, and France. Key research themes include constructing APN functions, analyzing their properties, and optimizing their hardware implementations for secure systems. Her recent publications emphasize hardware architectures for APN permutations, equivalence between cryptographic functions, and theoretical advancements in algebraic coding and finite fields. She serves as an editor for journals in cryptography and has organized international workshops such as WCC 2013.

Laura Colmenarejoمشاهده پروفایل

استادیار

• Algebraic combinatorics
• Symmetric functions
• Enumerative combinatorics
• +۴ مورد دیگر

Laura Colmenarejo is an Assistant Professor in the Department of Mathematics at NC State University, part of the College of Sciences. She holds a PhD from the University of Sevilla (2016), a Master’s from Universidad Autónoma de Madrid (2011), and a Bachelor’s in Mathematics (2010). Her research focuses on algebraic combinatorics, symmetric functions, enumerative combinatorics, and representation theory of finite groups. She has advised multiple graduate and undergraduate students and has held previous positions at UMass Amherst, the Max Planck Institute in Leipzig, and York University. Her work spans topics like quantum cohomology, chromatic symmetric functions, and combinatorial structures such as posets and permutations. She co-organizes the Algebra and Combinatorics seminar at NC State and contributes to conferences like Triangle Lectures in Combinatorics. Her research emphasizes interactions between algebraic geometry, representation theory, and discrete mathematics. Advising: Camryn Thompson, Alex Steward, Ian Klein, Spencer Daugherty (graduate students), and Felix Hutchins, Etienne Phillips, Viviene Do (undergraduate researchers). Grants and funding details not explicitly mentioned. Labs/Teams: Part of the Algebra and Combinatorics Faculty Research Group at NC State.

James Martinمشاهده پروفایل

مدرس

• Probability Theory
• Statistical Physics
• Random Graphs
• +۹ مورد دیگر

James Martin is a Lecturer at the Department of Statistics, University of Oxford . He is affiliated with St Hugh's College and has been actively involved in organizing probability seminars since 2018. Research Interests Probability theory Random graphs and percolation Interacting particle systems Models of random growth and coagulation-fragmentation Queueing networks Combinatorial games Teaching Courses: Prelims Probability , Part A Probability , Part B Statistical Lifetime Models , Part C Probabilistic Combinatorics His publications focus on probability theory , statistical physics , and combinatorial structures . Recent work includes studies on last-passage percolation, multispecies exclusion processes, and integrable probability models. James Martin collaborates with researchers from institutions such as Uppsala University, University of Cambridge, Imperial College London, and Kyoto University. He has been a key organizer for the Oxford Probability Seminar since 2018.

László Kozmaمشاهده پروفایل

استادیار

• Data Structures
• Algorithms
• Algorithmic Adaptivity
• +۷ مورد دیگر

László Kozma is an Assistant Professor at the Theoretical Computer Science group of the Institute of Computer Science (Freie Universität Berlin). He obtained his PhD from Saarland University under Raimund Seidel, followed by postdoctoral positions at Tel Aviv University and TU Eindhoven. His research focuses on self-adjusting data structures , adaptive algorithms , and combinatorial optimization with applications to problems like the Traveling Salesman Problem, binary search trees, and geometric data structures. Academic Affiliation: Freie Universität Berlin (since 2018) Education: PhD in Computer Science (Saarland University, 2016); postdoc at Tel Aviv University and TU Eindhoven. His work explores the intersection of data structures, combinatorial algorithms, and geometric methods. He has made significant contributions to problems involving pattern-avoidance in inputs, saddlepoint detection , and self-adjusting heaps . Key areas include: Adaptive algorithms for pattern-avoiding inputs Optimal tree and heap structures Geometric and stochastic approaches to optimization Complexity analysis of classical algorithms Recent publications highlight efficient solutions for exponential cut problems (ESA 2025), balanced TSP partitioning (EuroCG 2025), and randomized saddlepoint algorithms (ESA 2024). His research often bridges theory and practice, exemplified by the smooth heap implementation and fun projects like Recursi and Cuckoo Hashing visualization.

Allen Knutsonمشاهده پروفایل

استاد

• Algebra
• Algebraic Geometry
• Algebraic Combinatorics
• +۳ مورد دیگر

Prof. Allen Knutson is a Professor of Mathematics at Cornell University, affiliated with the College of Arts and Sciences. He earned his Ph.D. from the Massachusetts Institute of Technology in 1996. His research focuses on algebraic geometry, algebraic combinatorics, and geometric representation theory, with an emphasis on Schubert calculus, quiver varieties, and the combinatorial structures underlying geometric problems. Education: Ph.D. (1996) from MIT. His work often involves degenerating complex algebraic varieties into simpler combinatorial pieces, bridging geometry and discrete mathematics. Notable contributions include foundational results in Schubert calculus, honeycomb models, and the use of puzzles in cohomology computations. Research Interests: Algebraic geometry, algebraic combinatorics, Schubert calculus, quiver varieties, geometric representation theory, and applications to integrable systems. His interdisciplinary approach integrates algebraic, geometric, and combinatorial methods to solve problems in mathematics and theoretical physics. Advising: Current students include Portia Anderson, Raj Gandhi, and others. Past advisees have contributed to areas like flag manifolds, Frobenius splitting, and Bruhat atlases. He has taught advanced courses on topics such as symplectic resolutions, differentiable manifolds, and algebraic geometry. Labs/Teams: Collaborates widely, with contributions to projects like the ICM 2022 paper on Schubert calculus and quiver varieties. His work often involves visual tools like puzzles and pipe dreams to encode geometric invariants.

Kevin Fordمشاهده پروفایل

استاد

• Number Theory
• Prime number theory
• Divisor theory
• +۵ مورد دیگر

Kevin Ford is a Professor of Mathematics at the University of Illinois at Urbana-Champaign, affiliated with the Department of Mathematics within the College of Liberal Arts & Sciences. His research focuses on Number Theory, including prime number theory, divisor theory, probabilistic methods, and sieve theory, with notable contributions to the Riemann zeta function and divisor distribution. Ford is also an editor for Research in Number Theory and the Bulletin and Journal of the London Mathematical Society . His recent work explores topics such as large gaps between primes, composite polynomial values, and the concentration of divisors. Ford’s publications often bridge analytic and probabilistic techniques to address classical number-theoretic problems. Despite extensive contributions, no specific academic awards are explicitly listed, though his editorial roles highlight his influence in the field. He advises no listed students, though his research group and collaborations with leading mathematicians like Terence Tao and Dimitris Koukoulopoulos are evident in his publications. Ford’s academic presence includes organizing the Illinois Number Theory Seminar and maintaining an active research agenda in number theory and combinatorics.

• Computational Complexity
• Boolean Functions
• Communication Complexity
• +۵ مورد دیگر

Shachar Lovett is a researcher at the University of California, San Diego (UCSD), specializing in computational complexity, combinatorics, and theoretical computer science. His work spans advanced topics in communication complexity, pseudorandomness, and coding theory, often intersecting with problems in additive combinatorics and Boolean function analysis. Education : Not explicitly detailed in the provided text. Research Interests : Lovett's research focuses on computational complexity, particularly in communication and circuit complexity, combinatorial structures like sunflowers and high-dimensional expanders, and the analysis of Boolean functions through Fourier and Gowers norms. His work explores the limits of deterministic vs. randomized computation, the structure of codes over finite fields, and the interplay between additive combinatorics and theoretical computer science. Article Trends : His recent publications address exact vs. approximate representations of Boolean functions, quasipolynomial bounds in combinatorics, hypercontractivity in high-dimensional expanders, and advancements in the log-rank conjecture. These works emphasize connections between computational complexity, discrete mathematics, and pseudorandomness, often yielding improved bounds or novel frameworks for understanding Boolean function behavior. Scientific Awards : No specific awards or honors were mentioned in the provided text. Advising and Collaborations : Lovett collaborates extensively with researchers like Hamed Hatami, Kaave Hosseini, and Jiapeng Zhang, contributing to fields such as non-malleable codes, matrix multiplication algorithms, and communication complexity. No formal student advising details were provided.

• Clustering
• Edit Distance
• Approximation Algorithms
• +۹ مورد دیگر

Debarati Das is an Assistant Professor in Computer Science and Engineering , specializing in Clustering Algorithms , Edit Distance , and Approximation Algorithms . Her research focuses on theoretical computer science, particularly in algorithm design for data streams, permutation clustering, and sequence alignment. Grants: NSF CAREER Award (2024), NSF Student Travel Grant (2023). Her work includes breakthroughs in consensus clustering, achieving sub-2-approximation, and developing space-efficient algorithms for edit distance in distributed models. Recent projects explore dynamic shortest paths in planar graphs and pseudorandomness extraction. Her publications span journals like the Journal of the ACM and conferences such as SODA, STOC, and FOCS. Collaborations include researchers from institutions in the U.S. and Europe, with applications in computational biology and parallel computing.

Sihem Mesnagerمشاهده پروفایل

استاد

• Symmetric Cryptography
• Coding Theory
• Finite Fields
• +۱۰ مورد دیگر

Sihem Mesnager is a University Professor of Mathematics at the University of Paris VIII, affiliated with the Laboratory of Analysis, Geometry and Applications (LAGA) at Paris XIII (CNRS) and the AGC3 research group (Algebra, Geometry, Combinatorics) . She holds an adjunct professorship at Télécom Paris within the MIC2 Mathematics team of the Computer Science and Networks department (INFRES). Her work bridges pure mathematics and applied cryptography, focusing on Boolean functions, bent functions, and coding theory for secure communication and data protection. PhD in Mathematics, University of Pierre and Marie Curie (Paris VI), Sorbonne University (2002) Habilitation (HDR) in Mathematics, University of Paris VIII (2012) Research Interests Dr. Mesnager specializes in symmetric cryptography and coding theory , particularly their applications to secure digital communication, error correction, and post-quantum cryptographic protocols. Her algebraic approach employs finite fields, exponential sums, algebraic geometry, and finite geometry to analyze and construct cryptographic primitives like S-boxes, APN functions, and optimal linear codes. She also investigates algorithmic aspects of computer algebra in these domains. Scientific Awards George Boole International Prize (2020) PEDR Excellence Scientific Award (2019-2022) PEDR Excellence Scientific Award (2014-2017) Publications & Projects Her recent work includes constructing weightwise perfectly balanced Boolean functions for the FLIP cipher, optimizing Inner Product Masking schemes via coding theory, and developing post-quantum secure functional encryption using multivariate cryptography. She has contributed to Reed-Muller codes, BCH codes, and Gaussian sum-based linear codes with one-dimensional hulls, emphasizing their applications in side-channel attack resistance and quantum error correction.

• Zeta Functions
• Group Theory
• Representation Theory
• +۴ مورد دیگر

Prof. Dr. Christopher Voll is a full Professor at the Faculty of Mathematics, Bielefeld University , Germany. He leads multiple subprojects in the Transregional Collaborative Research Center TRR 358 'Integral Structures in Geometry and Representation Theory' , including A1 (lattices), A4 (Euler products), and A6 (quiver zeta functions). His research bridges algebra, number theory, and combinatorics through zeta functions, subgroup growth, and representation theory.

Olya Mandelshtamمشاهده پروفایل

استادیار

• Algebraic Combinatorics
• Enumerative Combinatorics
• Symmetric Functions
• +۳ مورد دیگر

Olya Mandelshtam is an Assistant Professor in the Department of Combinatorics and Optimization at the University of Waterloo . Her research focuses on algebraic combinatorics, particularly symmetric and quasisymmetric functions, with connections to probability and interacting particle systems. Education: Ph.D. in Mathematics, University of California, Berkeley (2016), advised by Lauren Williams Presidential Postdoctoral Scholar at UCLA (2016–2017) Tamarkin Assistant Professor and NSF Postdoctoral Fellow, Brown University (2017–2021) Research Interests: Her work bridges algebraic structures (e.g., Macdonald polynomials, Koornwinder polynomials) and probabilistic models like the asymmetric simple exclusion process (ASEP) and zero-range processes (TAZRP). She explores combinatorial bijections, multiline queues, and integrable systems to study these connections. Recent Activities: She organizes the Combinatorics Seminar at Waterloo and participates in conferences such as ICERM workshops on Category Theory and Machine Learning, ICECA, and events on integrable systems in algebraic combinatorics. Advising: Current graduate students include Kartik Singh, Jerónimo Valencia Porras, Guilherme Zeus Dantas e Moura, and Harper Niergarth, with past advisee William Chan. Research collaborations include work on multiline queues, non-attacking fillings, and particle system dynamics.

Gaurav Rattanمشاهده پروفایل

استادیار

• Graph Theory
• Machine Learning on Graphs
• Graph Neural Networks
• +۵ مورد دیگر

Gaurav Rattan is an Assistant Professor in the Department of Applied Mathematics at the University of Twente's Faculty of Electrical Engineering, Mathematics and Computer Science (EEMCS), where he joined in May 2024. His research focuses on the mathematical foundations of machine learning on graphs and discrete structures, with particular emphasis on theoretical aspects of graph neural networks. University of Twente, Department of Applied Mathematics (May 2024-present) TU Darmstadt, Postdoctoral Researcher in Pascal Schweitzer's group RWTH Aachen, DFG Eigene Stelle Researcher in Martin Grohe's group Dr. Rattan completed his PhD at IMSc Chennai under V. Arvind and earned his B. Tech. from IIT Bombay, establishing a strong foundation in theoretical computer science and mathematics. His research spans graph theory, algorithms, and machine learning on graphs, with specific expertise in graph isomorphism, graph homomorphisms, and the theoretical underpinnings of graph neural networks. He applies mathematical techniques from logic and algebra to develop theory-driven approaches for graph learning systems, with practical applications in optimization, bioinformatics, and databases. Dr. Rattan's publication record reveals a consistent focus on the intersection of theoretical computer science and machine learning. His recent work explores Weisfeiler-Leman algorithms, symmetry breaking techniques, and parameterized complexity of graph problems, demonstrating how classical graph algorithms connect with modern graph learning methodologies. His research provides crucial theoretical foundations for understanding the capabilities and limitations of graph neural networks. Active in the academic community, Dr. Rattan regularly presents at conferences including the Netherlands Mathematical Congress, SIGAlgo, LOGAMS, and specialized workshops on graph learning. Recent presentations include "From Graph Homomorphisms Densities to Graph Learning" at the Graph Learning Workshop at NITMB Chicago and "Color Refinement: One Algorithm, Many Facets" at SIGAlgo 2024.

Anuran Makurمشاهده پروفایل

استادیار

• Machine Learning Theory
• Information Theory
• Applied Probability
• +۴ مورد دیگر

Anuran Makur is an active Assistant Professor at Purdue University with dual appointments in the Department of Computer Science (College of Science) and the Elmore Family School of Electrical and Computer Engineering (College of Engineering). He is affiliated with the Institute for Control, Optimization and Networks (ICON) and teaches foundational courses in machine learning and data science. His educational background includes a B.S. in Electrical Engineering and Computer Sciences from UC Berkeley (2013, summa cum laude), an S.M. in Electrical Engineering and Computer Science from MIT (2015), and a Sc.D. from MIT (2019). B.S., UC Berkeley, 2013 S.M., MIT, 2015 Sc.D., MIT, 2019 Makur's research bridges theoretical machine learning, information theory, and applied probability. Key interests include ranking/preference learning, optimization for ML, non-parametric inference, information measures, permutation channel limits, broadcasting on graphs, and reliable computation. His work emphasizes fundamental theoretical limits and mathematical rigor in complex systems. Recent publications reveal strong trends in statistical learning theory (40%), information-theoretic methods (35%), and networked systems (25%), with growing emphasis on privacy-aware inference and high-dimensional statistics. His scientific achievements are recognized by prestigious awards: Arthur M. Hopkin Award (UC Berkeley, 2013) Ernst A. Guillemin Master's Thesis Award (MIT, 2015) Jin Au Kong Doctoral Thesis Award (MIT, 2020) Thomas M. Cover Dissertation Award (IEEE, 2021) NSF CAREER Award (2023) While specific advising details aren't public, his research leadership is evident through ICON affiliation and collaborations with MIT's LIDS/IDSS groups. The NSF CAREER grant supports his work on information-theoretic foundations of machine learning. He maintains active roles in theoretical computer science and information theory communities through conference organization and editorial work. Makur leads research within ICON, focusing on control-theoretic approaches to networked learning systems. His work integrates probabilistic modeling with optimization theory, particularly for distributed inference and networked decision-making under uncertainty.