جستجوی استاد راهنمای دانشگاهی

Emily J. King is a tenured Associate Professor in the Department of Mathematics at Colorado State University (CSU), College of Natural Sciences. She previously held a faculty position at the University of Bremen and has been actively contributing to the mathematical community through research, mentorship, and academic leadership. Her primary research interests include Frame Theory , Harmonic Analysis , Algebraic and Geometric Combinatorics , and Data Science , with applications in signal and image processing, Earth science, and artificial intelligence. She integrates deep mathematical theory with practical data analysis challenges. Her recent scholarly output reflects a strong focus on equiangular tight frames, combinatorial structures in frames, mathematical models for attention mechanisms, and applications to satellite imagery and cloud processes. Her work often bridges pure and applied mathematics, with a growing emphasis on interpretable AI and data science foundations. Dr. King has supervised several doctoral and master’s students, including Lander ver Hoef, Sören Schulze, Harley Meade, and Kristina Moen. She is a co-PI on an NSF grant focused on cloud processes and has been recognized for mentoring excellence, as evidenced by her student Emma Slack receiving the inaugural Outstanding Undergraduate in Mathematics award. NSF Grant Co-PI (2024) Outstanding Undergraduate in Mathematics award (mentored student, 2023) She is a founding co-organizer of the international Codes and Expansions (CodEx) Seminar and has organized sessions at major conferences such as SIAM AG and the Joint Mathematics Meetings. She frequently delivers invited talks at universities and research institutes worldwide, including upcoming presentations at the Air Force Institute of Technology, SIAM AG25, and TU Clausthal. Dr. King’s academic lineage includes John Benedetto as her mathematical advisor and Chandler Davis as her mathematical grandfather. She is actively involved in interdisciplinary research, particularly in marine data science, having co-spoken for the Helmholtz School for Marine Data Science (MarDATA).

• Frame Theory
• Harmonic Analysis
• Algebraic Combinatorics
• +۷ مورد دیگر

Shamil Asgarli is an Assistant Professor in the Department of Mathematics and Computer Science at Santa Clara University (SCU), College of Arts and Sciences. He holds a B.Sc. from the University of British Columbia (UBC) and a Ph.D. from Brown University (2019), advised by Brendan Hassett. Previously, he was a postdoctoral fellow at UBC under mentors Dragos Ghioca and Zinovy Reichstein. His research focuses on algebraic and arithmetic geometry, with recent explorations into algebraic graph theory. Core themes include geometry and combinatorics over finite fields, transverse hypersurfaces, blocking sets, and maximum cliques in Cayley graphs. Notable contributions involve Bertini-type theorems, Picard group analysis of quadric intersections, and studies of Frobenius nonclassical hypersurfaces. Teaching spans courses at SCU (e.g., Cryptography, Calculus), UBC, and Brown, including roles as instructor and teaching assistant for calculus, linear algebra, and number theory. He completed the Sheridan Center's Reflective Teaching Program (Certificate I). His work bridges pure mathematics with combinatorial applications, often leveraging finite field structures. Key collaborations include projects with Dragos Ghioca, Chi Hoi Yip, and Kuan-Wen Lai on transverse subspaces, blocking curves, and geometric irreducibility. His publications frequently address problems at the intersection of algebraic geometry and finite field theory, with applications to coding and cryptography.

• Algebraic Geometry
• Arithmetic Geometry
• Finite Fields
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David Penman is a Senior Lecturer at the University of Essex's School of Mathematics, Statistics and Actuarial Science (SMSAS). He holds a first degree and Part III Mathematics from Cambridge, followed by an M.Sc. and Ph.D. in probability from Sheffield University. His research focuses on probabilistic combinatorics, random graphs, additive combinatorics, and graph theory, with notable work on Paley graphs and fixed point polynomials of permutation groups. He has supervised eight research students, including six Ph.D. candidates, and currently serves as Editor of the British Combinatorial Bulletin. Education : Bachelor's and Part III (Master's equivalent) in Mathematics, University of Cambridge M.Sc. and Ph.D. in Probability (random graphs), University of Sheffield Research Interests : Probabilistic combinatorics with correlation structures Paley graphs and pseudo-random graphs Extremal Ramsey graphs and poset analysis Quicksort algorithms and sandpile group dynamics Professional Roles : Deputy Dean (Education), Faculty of Science and Health (2013–2019) Editor of the British Combinatorial Bulletin Teaching : Current module: Graph Theory (MA314). Publications : Over 10 peer-reviewed articles in journals like Discrete Mathematics , Acta Arithmetica , and Electronic Journal of Combinatorics . His work bridges theoretical mathematics and applied combinatorics, with contributions to additive number theory and algorithmic analysis. Collaborations include projects on large deviations in random graphs and fixed point polynomials in permutation groups.

• Probabilistic Combinatorics
• Random Graphs
• Paley Graphs
• +۵ مورد دیگر

Madeline Dawsey is an Associate Professor of Mathematics at the University of Texas at Tyler. She holds a Ph.D. in Mathematics from Emory University (2019) and dual bachelor's degrees in Mathematics and Italian from the University of Georgia (2014). Her research focuses on the intersection of combinatorics and number theory, with a particular emphasis on partitions, prime numbers, and digital representations. She has also conducted applied mathematics research analyzing swimming data using accelerometers. Education: Ph.D., Mathematics, Emory University, 2019 B.S., Mathematics and A.B., Italian, University of Georgia, 2014 Dr. Dawsey’s research interests span number theory, combinatorics, and applied mathematics, with recent work exploring partition theory and its applications. Her publications frequently address topics like partition identities, modular forms, and algebraic structures. She has advised numerous undergraduate and graduate students on research projects, including studies on partitions, permutations, and applied mathematical modeling. Her teaching spans a wide range of courses, from calculus and statistics to abstract algebra and combinatorics. She has been recognized for her pedagogical contributions, including mentoring students who have received MathFest Outstanding Presentation Awards. Dr. Dawsey is actively involved in academic service, serving as founder and faculty advisor for the Women in Math and Science (WIMS) organization and as faculty advisor for the Pi Mu Epsilon Mathematics Honor Society at UT Tyler.

• Number Theory
• Combinatorics
• Applied Mathematics
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Dermot McCarthy is an Associate Professor in the Department of Mathematics & Statistics at Texas Tech University. His research focuses on combinatorics, number theory, and special functions, including graph theory, Ramsey theory, modular forms, Jacobi forms, hypergeometric functions, and algebraic varieties. His work is supported by a Simons Foundation grant. Recent research includes studies on hypergeometric functions over finite fields, Paley digraphs, and modular forms. He has published extensively in journals such as Research in the Mathematical Sciences and International Journal of Number Theory , often collaborating with researchers like M. Springfield and C. Monico. McCarthy teaches advanced courses in combinatorics, number theory, and modern algebra. He actively participates in academic events such as the 36th Automorphic Forms Workshop (2024) and organizes the Texas Tech Algebra and Number Theory Seminar. No scientific awards are explicitly mentioned, but his contributions to number theory and combinatorics are notable. His teaching spans foundational to graduate-level courses in algebra and number theory.

• Combinatorics
• Number Theory
• Special Functions
• +۵ مورد دیگر

Tim Kunisky is an Assistant Professor in the Department of Applied Mathematics and Statistics at Johns Hopkins University (JHU), with affiliations to the Department of Mathematics, Data Science and AI Institute, and Algorithms and Complexity Group. Previously, he was a postdoctoral associate at Yale University (2021–2024) and earned his PhD in Mathematics from NYU’s Courant Institute (2021), advised by Afonso Bandeira and Gérard Ben Arous. His research focuses on the computational complexity of statistical problems, spectral algorithms, random matrix theory, and convex optimization. He explores topics like the theoretical limits of algorithms, phase transitions in statistical estimation, and applications to data science. Key interests include: Computational thresholds for hypothesis testing Eigenvalue distributions in random matrices Algorithmic lower bounds via sum-of-squares methods Applications to community detection and compressed sensing Recent work includes advancements in low-coordinate-degree algorithms, statistical inference in directed graphs, and spectral methods for tensor analysis. He teaches courses such as Random Matrix Theory in Data Science and Probability Theory II at JHU. Upcoming engagements include workshops on combinatorics and theoretical computer science (BIRS, June 2025) and the COLT conference in Lyon (July 2025). His work bridges mathematics and computer science, addressing foundational questions in high-dimensional statistics and algorithmic efficiency.

• Computational Complexity of Statistical Problems
• Spectral Algorithms
• Random Matrix Theory
• +۴ مورد دیگر

Dmitriy (Tim) Kunisky is an Assistant Professor in the Department of Applied Mathematics and Statistics at Johns Hopkins University (JHU), affiliated with the Data Science and AI Institute, Department of Mathematics, and Algorithms and Complexity Group. Prior to JHU, he was a postdoctoral associate at Yale University (2021–2024) and earned his PhD in Mathematics from New York University’s Courant Institute (2021), advised by Afonso Bandeira and Gérard Ben Arous. His research focuses on the interplay between probability theory, mathematical statistics, and computational complexity, particularly in high-dimensional data and algorithmic limitations. He has held roles in software engineering (Google) and academic advising (ETH Zurich). Education: Bachelor’s in Mathematics, Princeton University PhD in Mathematics, Courant Institute, NYU Research Interests: His work examines computational thresholds in statistical problems, spectral algorithms, random matrix theory, and convex optimization. He explores the mathematical foundations of algorithmic performance, including information-computation gaps and pseudorandomness. Recent topics include nonlinear Laplacians, random circulant graphs, and low-coordinate-degree algorithms. Teaching & Outreach: In Fall 2025, he teaches Random Matrix Theory in Data Science . Past courses include Sum-of-Squares Optimization (Yale) and Mathematical Statistics (NYU). He advises graduate students like David Opalic and Nikolaus Doppelbauer on topics like Sherrington-Kirkpatrick Hamiltonians and mutually unbiased bases. Upcoming Engagements: BIRS Workshop on Combinatorics (June 2025) TTIC Workshop on Information-Computation Tradeoffs (June 2025) INFORMS Applied Probability Conference (July 2025) COLT Conference (July 2025) Labs/Teams: Active in JHU’s Data Science and AI Institute, collaborating on projects involving high-dimensional statistics, convex optimization, and algorithm design.

• Probability
• Statistics
• Mathematical Data Science
• +۵ مورد دیگر

Tung T. Nguyen is a Visiting Assistant Professor of Computer Science at Lake Forest College, with a PhD in Mathematics from the University of Chicago (2020). He will transition to a tenure-track Assistant Professor position at Elmhurst University in Fall 2025. His research bridges computational number theory, spectral graph theory, and nonlinear dynamics, with recent work exploring AI-driven theorem proving. He actively mentors students, emphasizing experimental projects that merge mathematical rigor with coding. Education: PhD in Mathematics (University of Chicago, 2020) under Prof. Kazuya Kato; Postdoc at Western University (2020-2024); Undergraduate at Vietnam National University. Research focuses on Algorithms for prime number theory Applications of algebraic structures in graph theory Nonlinear oscillator networks and synchronization phenomena Key awards: 2025 SIAM Early Career Travel Award (to present work at AG25 conference) and MAA Project NExT fellowship. Organized conferences include the virtual celebration of Prof. Moshe Rosenfeld's 85th birthday. Teaching includes Probability & Statistics and independent studies on prime number algorithms. Collaborates on initiatives like the Vietnam PolyMath REU program.

• Computational Number Theory
• Spectral Graph Theory
• Nonlinear Dynamics
• +۳ مورد دیگر

Jeffrey Hogan is an Associate Professor in the School of Information and Physical Sciences (Mathematics), University of Newcastle. He holds academic appointments including Head of Mathematics Discipline and former Deputy Head of the School of Mathematical and Physical Sciences. His research focuses on harmonic analysis, wavelet theory, and applications in signal processing, including bandlimited signals, prolate spheroidal wave functions, and quaternion-valued wavelets. Hogan earned his BSc (Hons) and PhD from the University of New South Wales, followed by postdoctoral roles at institutions like the University of Texas and University of Arkansas before joining Newcastle in 2009. Education: Bachelor of Science, University of New South Wales PhD (Mathematics), University of New South Wales Research Interests: Jeffrey Hogan explores pure, applied, and computational harmonic analysis, including singular integrals, wavelet frames, Clifford-Fourier theory, and applications in mineral fractionation. His work emphasizes spatio-spectral limiting, multidimensional wavelet construction, and quaternionic signal processing. Key projects involve developing algorithms for EEG analysis, optimizing wavelet design via projection methods, and analyzing signals on graphs and hypercubes. Grant Funding: A$546,913: Enhanced Fractionation of Mineral Particles According to Density (ARC Discovery Project, 2020–2022) A$593,621: Relaxed Reflection Methods for Feasibility and Matrix Completion Problems (ARC Discovery Project, 2016–2018) Collaborations & Teams: Hogan collaborates with experts like Prof. Kevin Galvin (University of Newcastle), Prof. Joseph Lakey (New Mexico State University), and Dr. Matthew Tam (University of Göttingen). His work integrates interdisciplinary approaches, combining pure mathematics with applications in engineering and mineral processing. His lab focuses on computational harmonic analysis and optimization-driven wavelet design.

• Harmonic Analysis
• Signal Processing
• Wavelet Analysis
• +۵ مورد دیگر

Dmitriy (Tim) Kunisky is an Assistant Professor in the Department of Applied Mathematics and Statistics at Johns Hopkins University's Whiting School of Engineering. He is also affiliated with the Data Science and AI Institute, the Department of Mathematics, and the Algorithms and Complexity Group at Johns Hopkins. Dr. Kunisky received his bachelor's degree in mathematics from Princeton University, worked as a software engineer for Google, earned his PhD in mathematics from the Courant Institute at NYU under the supervision of Afonso Bandeira and Gérard Ben Arous, and was a postdoctoral associate in computer science at Yale University before joining Johns Hopkins. His research broadly concerns how probability theory and mathematical statistics interact with computational complexity and the theory of algorithms. He investigates the mathematical phenomena that govern the power and limitations of algorithms processing massive and high-dimensional inputs, drawing on asymptotic statistics, convex geometry, random matrix theory, statistical physics, and representation theory. His work includes studying convex relaxation algorithms on combinatorial optimization problems, computational intractability in high-dimensional statistics, pseudorandomness, and experimental approaches to number theory and combinatorics. His recent publications demonstrate a consistent focus on the intersection of computational complexity, statistical inference, and random matrix theory. There's a clear trajectory from theoretical foundations to practical algorithmic applications, with particular emphasis on information-computation gaps, spectral methods, and the sum-of-squares hierarchy. His work often bridges theoretical computer science with statistical physics approaches. Dr. Kunisky actively advises graduate students at Johns Hopkins, including PhD candidates in Applied Mathematics and Statistics. He has taught courses on Random Matrix Theory in Data Science and Statistics, Probability Theory, Sum-of-Squares Optimization, and Modern Probability for Theoretical Computer Science, demonstrating his commitment to both research and education in mathematical data science.

• Probability
• Statistics
• Mathematical Data Science
• +۷ مورد دیگر

Dr. Mahya Ghandehari is an Associate Professor in the Department of Mathematical Sciences at the University of Delaware . Her work bridges Noncommutative Harmonic Analysis , Graph Limit Theory , and Data Science , with a focus on mathematical frameworks for network analysis and signal processing on graphs. PhD in Mathematics, University of Waterloo Her research addresses challenges in seriation and graph signal processing (GSP) , particularly through graphon-based models. Recent work explores spectral methods, Fourier bases for stochastic block model graphs, and wavelet characterizations of network structures. Dr. Ghandehari’s research is supported by active NSF grants (DMS-2408008, CCF-2427965) and Simons Travel Support . She supervises PhD student Caroline McCrorey and has mentored several PhD graduates now holding academic positions at institutions like Tufts University and SUNY Oswego. Scientific honors include the Fields Postdoctoral Fellowship . She organizes seminars such as the Analysis Seminar at Delaware and the Northeastern Analysis Meeting (NEAM 2025) .

• Noncommutative Harmonic Analysis
• Harmonic Analysis on non-Abelian groups
• Fourier Analysis on Graphs
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