جست‌وجوی استاد و پژوهشگر

Samuel Johnstonمشاهده پروفایل

مدرس

• Probability
• Stochastic Processes
• Free Probability
• +۷ مورد دیگر

Samuel Johnston is a Lecturer in Probability Theory at the Department of Mathematics, King's College London, affiliated with the Faculty of Natural, Mathematical & Engineering Sciences. He joined King's in 2022 after postdoctoral roles at the University of Bath, University of Graz, and University College Dublin. MMath, University of Oxford (2014) PhD in Probability, University of Bath (2017) Johnston's research spans probability theory, with a focus on stochastic processes involving branching, coalescence, and fragmentation. He actively explores free probability, random matrices, integrable combinatorics, and combinatorial approaches to the Jacobian conjecture. His work intersects with statistical physics and asymptotic geometric analysis. Recent publications highlight coalescent structures in heavy-tailed branching processes, integrable probability models, free probability via entropic transport, and convexity in high dimensions. Keywords include universality classes, Berry-Esseen bounds, and fragmentation-scaling limits. Samuel has not been mentioned to have received specific scientific awards or honors. He advises PhD students Rohan Shiatis (2023-) and Neil Mukerji (2024-). Collaborations span institutions in the UK, USA, Mexico, Austria, and Poland, with invited talks at global conferences including Xiangtan University, Imperial College London, and UCLA.

• Stochastic integrable systems
• Asymptotic analysis
• Random matrix theory
• +۵ مورد دیگر

Evgeni Dimitrov is an Assistant Professor of Mathematics in the Department of Mathematics at the University of Southern California, housed within the USC Dana and David Dornsife College of Letters, Arts and Sciences. Before joining USC, he served as a Ritt Assistant Professor in the Mathematics Department at Columbia University. Education: PhD in Mathematics, Massachusetts Institute of Technology (MIT), advised by Alexei Borodin Undergraduate degree, Princeton University Research Interests: Dimitrov’s research sits at the intersection of probability, representation theory, and combinatorics, with a central focus on the asymptotic analysis of stochastic integrable systems . He develops hybrid techniques that blend algebraic methods from representation theory with analytic and combinatorial tools to study universal scaling limits—particularly those falling within the Kardar–Parisi–Zhang (KPZ) universality class . Key objects of study include Gibbsian line ensembles , random matrix models , log-gamma polymers , and exactly-solved stochastic particle systems such as ASEP and the six-vertex model. Publication Profile: Across 2021–2025, Dimitrov has produced a concentrated body of work addressing edge fluctuations , multi-level loop equations , and global large-deviation principles for discrete β-ensembles and related integrable systems. His papers repeatedly explore the convergence of discrete stochastic models to Airy-like universal processes, tightness questions for line ensembles, and the rigorous derivation of KPZ scaling laws, underscoring a cohesive research trajectory toward understanding universal random geometry. Scientific Awards: No awards are explicitly mentioned in the supplied material. Advising & Grants: No specific PhD students, grants, or funding details are provided in the text. Labs & Teams: No laboratory or research-group information is available from the supplied content.

• 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.

Özgün Ünlüمشاهده پروفایل

دانشیار

• Algebraic Topology
• Category Theory
• Group Actions on Manifolds

Özgün Ünlü is an Associate Professor in the Department of Mathematics at Bilkent University, Ankara, Turkey, where he has served since 2007 after completing a Britton Postdoctoral Fellowship at McMaster University (2004–2007). His academic trajectory includes promotion from Assistant Professor (2007–2025) to his current rank (2025–present), reflecting sustained contributions to mathematical research and education. Education: B.Sc. in Mathematics with a minor in Computer Engineering from Middle East Technical University. Ph.D. in Mathematics from the University of Wisconsin-Madison under Prof. Alejandro Ádem. Research Focus: Ünlü’s work centers on algebraic topology and category theory, with specialized expertise in constructing group actions on manifolds. His research bridges abstract algebraic structures with topological spaces, particularly investigating finite group actions on products of spheres, fusion systems, and equivariant cohomology. This interdisciplinary approach integrates category-theoretic frameworks to solve problems in transformation group theory and homotopy theory. Publication Trends: Analysis of his 13 most recent publications reveals a consistent focus on group actions (80% of works), with evolving methodologies from explicit sphere constructions (2004–2013) to advanced categorical and homological techniques (2016–2023). His 2023 work on differential graded modules demonstrates a shift toward minimal model theory in homotopy algebra, while earlier collaborations with E. Yalçın established foundational results in free group actions on spheres. Academic Service: Teaching responsibilities include advanced courses like Math 240 (Differential Equations) and Math 323 (Algebra I), reflecting his dual expertise in analysis and algebra. While no formal advising or grant information is documented, his extensive publication record indicates active mentorship within collaborative research frameworks.

Tsvetanka Sendovaمشاهده پروفایل

پژوهشگر

• Applied Mathematics
• Linear Algebra
• Fracture Mechanics
• +۳ مورد دیگر

Tsvetanka Sendova is a Researcher in the Department of Mathematics at Michigan State University. She holds a Ph.D. in Mathematics from Texas A&M University (2008) and a Bachelor of Science in Mathematics from Sofia University St. Kliment Ohridski, Bulgaria (2002). Her research focuses on applied mathematics, including fracture mechanics, evolutionary game theory in biological systems, and matrix analysis. She has contributed to teaching, such as MTH 421-Analysis II in Spring 2020. Education : Doctor of Philosophy in Mathematics, Texas A&M University (2008) Bachelor of Science in Mathematics, Sofia University, Bulgaria (2002) Research Interests : Dr. Sendova’s work spans fracture mechanics (surface tension effects, nanoscale modeling), applications of evolutionary game theory to cancer dynamics, and linear algebra studies on matrix numerical ranges and Gau-Wu numbers. Her research in hyperelastic materials explores constitutive models and strong ellipticity conditions. These efforts bridge theoretical mathematics with practical applications in material science and biological systems. Grants & Advising : No specific grants or student advisees are listed in her profile. She contributes to academic mentoring through initiatives like the Center for Instructional Mentoring and Math Learning Center. Labs/Teams : Her research is conducted within the Department of Mathematics at MSU, with collaborations across academic disciplines.

Simone Brugiapagliaمشاهده پروفایل

دانشیار

• Mathematics of Data Science
• Computational Mathematics
• Numerical Analysis
• +۵ مورد دیگر

Simone Brugiapaglia is an Associate Professor in the Department of Mathematics and Statistics at Concordia University in Montréal, Canada. His academic journey includes a PhD in Mathematical Models and Methods from Polytechnic University of Milan (2016), an MSc in Mathematics from University of Pisa (2012), and a BSc in Mathematics from University of Pisa (2010), all earned cum laude . Prior to his current role, he held postdoctoral positions at École polytechnique fédérale de Lausanne (2016) and Simon Fraser University (2016-2019). Dr. Brugiapaglia's research bridges mathematics, data science, and computational methods. Key interests include: Foundations of deep learning and neural networks Compressed sensing and sparse recovery algorithms High-dimensional approximation theory Numerical methods for PDEs and diffusion equations Physics-informed machine learning Optimization techniques for large-scale problems His work develops rigorous mathematical frameworks for data-driven algorithms. His publications (30+ including two books) consistently focus on high-dimensional computation , featuring recent advances in neural network theory (e.g., generalization bounds, rank collapse), compressed sensing techniques (e.g., greedy algorithms, unrolled networks), and physics-informed learning. A strong trend involves combining traditional numerical methods with deep learning for PDE solutions. Awards & Fellowships: Concordia Research Fellow (2023) Leslie Fox Prize for Numerical Analysis (2nd place, 2019) PIMS Postdoctoral Fellowship (2016-2018) Multiple INdAM scholarships during graduate/undergraduate studies He has supervised over 20 trainees across postdoctoral, graduate, and undergraduate levels. While specific grants aren't detailed, his fellowship history indicates sustained research funding. No explicit research labs or teams are mentioned, but his supervision record and collaborative publications suggest active leadership in research groups focused on computational mathematics.

• Number Theory
• Cryptography
• Finite Fields
• +۴ مورد دیگر

Professor Igor Shparlinski is a faculty member at the University of New South Wales (UNSW) within the School of Mathematics & Statistics, holding the rank of Professor with an active research profile as evidenced by publications dated 2025. His work bridges pure number theory and cryptographic applications, maintaining international collaborations with institutions like the University of Sydney and Max Planck Institute. Research interests include: Analytic number theory (exponential/character sums) Cryptographic security (Diffie-Hellman, RSA, and pseudorandom generators) Finite field arithmetic and polynomial dynamics Arithmetic combinatorics and additive number theory Distribution of primes/smooth numbers modulo p Elliptic curve structures over finite fields His publications consistently address foundational problems with computational implications, particularly focusing on sum bounds and modular arithmetic. Recent trends (2023-2025) show intensified work on multilinear character sums, matrix statistics over finite fields, and dynamical system orbits. Key journals include Journal of Number Theory and Finite Fields and their Applications , with frequent co-authorship on exponential sum bounds and cryptographic vulnerabilities. Scientific awards: No awards, prizes, or fellowships are documented in the provided text. Advising and grants: No student listings or grant information appears in the source material. Research output suggests sustained funding but lacks explicit details.

• Probability Theory
• Quantum Dynamics
• Random Matrix Theory
• +۳ مورد دیگر

Horng-Tzer Yau is the Merton Professor of Mathematics at Harvard University's Department of Mathematics. His research focuses on probability theory, quantum dynamics, random matrices, differential equations, and nonequilibrium physics. He serves as Editor in Chief of Communications of Mathematical Physics and organizes the Random Matrix Seminar. His work explores fundamental questions in mathematical physics, particularly in understanding universality phenomena in random systems. Key research areas include spectral properties of random matrices, quantum chaos, and statistical mechanics models like spin glasses. His recent studies address delocalization in random band matrices, edge universality in regular graphs, and high-dimensional spectral statistics. Yau's contributions span theoretical frameworks for quantum dynamics, rigorous analysis of eigenvalue distributions, and interdisciplinary applications bridging probability, combinatorics, and physics. Despite the extensive publication record, no specific student advisement or grants are explicitly mentioned in the provided materials.

Bojan Moharمشاهده پروفایل

استاد

• Topological Graph Theory
• Graph Minors
• Graph Coloring
• +۳ مورد دیگر

Bojan Mohar is a Professor in the Department of Mathematics at Simon Fraser University (SFU), within the Faculty of Science. He holds a Ph.D. in Mathematics from the University of Ljubljana, Slovenia (1986). His research focuses on advanced topics in graph theory, including topological graph theory (graphs on surfaces, planar graphs), graph minors, graph coloring (list coloring, edge-coloring, nowhere-zero flows), algebraic graph theory (Laplace eigenvalues, spectral analysis), and graph algorithms. Mohar's work bridges theoretical foundations with computational methods, emphasizing interdisciplinary applications. His research interests span diverse areas such as the spectral properties of infinite graphs, graph embeddings, and combinatorial optimization. He is affiliated with the Centre for Operations Research and Decision Sciences (CORDS) at SFU. Mohar has made significant contributions to understanding graph structures, eigenvalues, and algorithm design, with a particular emphasis on topological and algebraic aspects. His recent work includes proofs of long-standing conjectures in graph theory and the development of efficient approximation algorithms for graph genus calculations. Mohar’s academic contributions are reflected in his extensive publication record, focusing on graph minors, eigenvalue analysis, and topological embeddings. He teaches courses such as MATH 345 D100: Introduction to Graph Theory (Fall 2025). No formal advising relationships or awards are explicitly listed in the provided materials, though his research impact is evident through collaborations and symposium participations like the International Symposium on Computational Geometry (SoCG).

Michael E. O'Sullivanمشاهده پروفایل

استاد

• Coding Theory
• Algebraic Geometry
• Error-Correcting Codes
• +۶ مورد دیگر

Michael E. O'Sullivan is a Professor and Chair in the Department of Mathematics and Statistics at San Diego State University. He has taught advanced courses such as Commutative Algebra and Algebraic Geometry (Math 621) and Groups, Rings, and Fields (Math 620), with a focus on integrating computational tools like Sage, Maple, and Magma into his teaching. Research Focus: His work centers on Coding Theory , particularly Algebraic Geometry Codes , Hermitian Codes , and LDPC Codes . His research includes developing decoding algorithms (e.g., Groebner basis methods, Sudan's list decoding), analyzing code properties (e.g., minimum distance, redundancy), and optimizing code constructions. Publications: His most recent articles (2014–2006) address unique decoding of AG codes, list decoding techniques, and applications of algebraic structures to code optimization. These span journals like IEEE Transactions on Information Theory and Designs, Codes and Cryptography , as well as conferences such as IEEE International Symposium on Information Theory and Canadian Workshop on Information Theory . Emails: Contact him at mosullivan@mail.sdsu.edu or m.osullivan@math.sdsu.edu .

• Probability Theory
• Quantum Stochastic Processes
• Orthogonal Polynomials
• +۷ مورد دیگر

Wojciech Matysiak is an Assistant Professor in the Faculty of Mathematics and Information Science at Warsaw University of Technology. His research lies at the intersection of probability theory, quantum stochastic processes, and algebraic structures in mathematics. Institution: Warsaw University of Technology School: Faculty of Mathematics and Information Science Position: Assistant Professor of Mathematics Email: matysiak@mini.pw.edu.pl Office: 439, Mathematics Building, ul. Koszykowa 75, Warsaw, Poland His primary research interests include Probability Theory , Quantum Stochastic Processes , Orthogonal Polynomials , and Noncommutative Probability . He investigates structures such as quadratic harnesses, quantum Bessel processes, and random fields with linear regressions, often using operator-theoretic and algebraic methods. The analysis of his recent publications reveals a strong focus on the interplay between algebra and probability, particularly through q-commutation relations, martingale polynomials, and generalized stochastic processes. His work spans pure mathematics with applications in mathematical physics and has extended into interdisciplinary domains such as soil science and oncology. Wojciech Matysiak has published in prestigious journals including Transactions of the American Mathematical Society , Stochastic Processes and their Applications , and Journal of Theoretical Probability . His recent work continues to explore deep connections between algebraic identities and probabilistic models. He collaborates with researchers such as Włodzimierz Bryc, Jacek Wesołowski, and Marcin Świeca. He is involved in the Probability Seminar at his institution and contributes to teaching materials for mathematics and engineering students. No scientific awards or grants are mentioned in the provided texts. He maintains a research webpage and is actively publishing, indicating ongoing scholarly activity in mathematical probability and its applications.

• Cryptography
• Coding Theory
• Sequences and Their Applications
• +۵ مورد دیگر

Oğuz Yayla is a full-time faculty member at Middle East Technical University (METU), Türkiye, active in research and graduate supervision. His scholarly output comprises 57 Web-of-Science-indexed publications, 31 peer-reviewed conference papers and numerous invited talks, with a WoS h-index of 13 and Scopus h-index of 3 (as of the latest data dump). Research Interests Algebraic and applied cryptography, including post-quantum schemes Coding theory, particularly algebraic-geometry codes and codes over rings Sequences with good correlation properties for CDMA, stream ciphers and 5G/6G applications Blockchain security, distributed consensus, and IoT-medical data integrity Hardware-oriented security: TRNG design, side-channel countermeasures on FPGA/SoC Over the past decade, his publications reveal a clear evolution from foundational work on finite fields and pseudorandom sequences toward practical cryptographic engineering, including verifiable timed signatures, threshold multi-secret sharing for blockchain storage, and FPGA-based true random number generators compliant with AIS-20/31. Scientific Awards & Recognition Consistently high citation impact: >34 WoS citations and >20 Scopus citations PlumX reader and capture metrics place him in top percentiles for cryptography outputs in Türkiye Principal investigator / senior researcher on 7 funded national projects Graduate Advising & Collaborative Grants Yayla has formally supervised 15 graduate theses (MS and PhD) in mathematics and cryptography. He leads or co-leads multiple TÜBİTAK and BAP-funded projects on post-quantum cryptography, secure IoT medical devices, and code-based cryptography. Laboratory & Teams He heads the Cryptography & Information Security Research Group at METU, where a multidisciplinary team of graduate students and post-doctoral researchers work on lattice-based cryptography, side-channel analysis, and blockchain protocol design.

• Representation Theory
• Algebraic Combinatorics
• Algebraic Geometry
• +۳ مورد دیگر

Dr. Lim Kay Jin is a Senior Lecturer at the Division of Mathematical Sciences, School of Physical & Mathematical Sciences, Nanyang Technological University (NTU). He serves as Director of the MSc (Analytics) program. His academic journey includes a PhD in Mathematics from the University of Aberdeen (2009), an MSc (2007), and a BSc (Hons) in Mathematics from the National University of Singapore (2005). His research focuses on representation theory of finite-dimensional algebras, with connections to algebraic combinatorics and algebraic geometry. Key areas include descent algebras, Lie modules, and the interplay between module structures and group actions. His work often explores cohomology, filtration properties, and combinatorial invariants like signed Kostka matrices and Young tableaux. Teaching responsibilities include advanced modules in algebra and analytics. His publications (2009–2024) span topics like Jantzen filtrations, seminormal basis vectors, and Brauer constructions. No explicit awards or grants are listed, though his work contributes to foundational algebraic research. Contact via limkj@ntu.edu.sg or SPMS-MAS-05-16, NTU campus.

Noah Williamsمشاهده پروفایل

دانشیار

• Probability Theory
• Random Polynomials
• Random Matrices
• +۵ مورد دیگر

Noah Williams is a tenured Associate Professor in the Department of Mathematical Sciences at Appalachian State University, part of the College of Arts and Sciences. He has completed seven years of service as of fall 2025, actively contributing to both teaching and research in probability and its applications. Research Interests: Dr. Williams specializes in probability theory , particularly focusing on random polynomials and random matrices . His work investigates the asymptotic behavior of roots and critical points, pairing phenomena, and eigenvalue statistics. He also explores combinatorial problems arising in genome rearrangement, applying probabilistic and discrete methods to model evolutionary distance. Recent Research Trends: His recent publications reveal a strong focus on the interplay between complex analysis and probability, especially in the context of random polynomials. He frequently collaborates with Sean O'Rourke, producing rigorous results on the Gauss-Lucas theorem and local root-critical point pairing. A secondary thread involves discrete-to-continuous transitions in arithmetic progressions and genome evolution models under operations like DCJ and deletion-insertion. No scientific awards or honors are mentioned in the available text. Teaching & Academic Service: Dr. Williams teaches a wide range of courses including Calculus I, Techniques of Proof, Linear Algebra, and graduate-level Computational Probability. He previously served in multiple instructional roles at the University of Colorado Boulder, including Instructor of Record, Assistant Coordinator for large calculus sections, and Lead Graduate Teacher for pedagogical training. His teaching philosophy incorporates Inquiry-Based Learning (IBL), supported by resources from established IBL initiatives. Laboratories & Research Teams: While no formal lab is mentioned, Dr. Williams is part of a collaborative research network, primarily with Sean O'Rourke (University of Colorado Boulder), and has co-authored work with other mathematicians such as T. Greenwood, J. Kariv, A. Brisbin, M. Riehl, and J. Christy. These collaborations suggest an active research group or team in probabilistic combinatorics and random matrix theory.

Mostafa Nasriمشاهده پروفایل

مدرس

• Mathematical Analysis
• Operations Research
• Functional Analysis
• +۳ مورد دیگر

Mostafa Nasri serves as an Instructor in the Department of Mathematics and Statistics at the University of Winnipeg within the Mathematics and Statistics Faculty. Holding a Ph.D. from IMPA, he completed postdoctoral fellowships at Laval University, University of Montreal, and McGill University before joining his current institution. His academic credentials include: Ph.D. in Mathematics, IMPA- Instituto Nacional de Matemática Pura e Aplicada (2008) M.Sc. in Mathematics, Amirkabir University of Technology (2004) B.Sc. in Mathematics, Sharif University of Technology (2002) Dr. Nasri's research integrates Mathematical Analysis and Operations Research with specialized expertise in Functional Analysis, Optimization, and Applied Mathematics. His theoretical work examines Schauder bases, composition operators, and Dirichlet spaces, while his Optimization research develops augmented Lagrangian methods for equilibrium problems and variational inequalities. Applied contributions include contact dynamics modeling for multibody systems and HIV infection dynamics. Analysis of his 2023-2025 publications reveals intensified focus on Functional Analysis (particularly Dirichlet spaces and operator theory) alongside persistent development of optimization algorithms, with applied mechanics and mathematical biology research continuing at reduced frequency. No scientific awards are documented in available materials. Current information indicates no graduate student advising or active research grants at the University of Winnipeg. Prior industry collaboration with CM Labs Simulations Inc. and Canadian university departments demonstrates applied research experience, though no current laboratories or dedicated research teams are specified.