جست‌وجوی استاد و پژوهشگر

Mahya Ghandehariمشاهده پروفایل

دانشیار

• Noncommutative Harmonic Analysis
• Harmonic Analysis on non-Abelian groups
• Fourier Analysis on Graphs
• +۴ 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) .

• Network Theory
• Dynamical Systems
• Synchronization Phenomena
• +۳ مورد دیگر

Dr. Shriya V. Nagpal is an Assistant Professor of Mathematics at Pitzer College, part of the Claremont Colleges consortium. Her academic journey includes a Ph.D. in Applied Mathematics from Cornell University (2024), where she was co-advised by Francesca Parise and Lindsay Anderson, and an undergraduate degree in Mathematics from Trinity College. Dr. Nagpal's research sits at the intersection of Network Theory and Dynamical Systems , with a particular focus on synchronization phenomena in complex networks. Her recent work examines how emergent behaviors arise in interconnected systems, especially through the lens of Kuramoto oscillator models on random graph structures. Her research has direct applications to understanding biological systems like circadian rhythms, collective behaviors such as flashing fireflies, and engineering systems including power grids. Dr. Nagpal employs sophisticated mathematical approaches, particularly graphon theory, to analyze the behavior of large-scale network systems and their convergence properties. As a recently appointed Assistant Professor (having completed her Ph.D. in 2024), Dr. Nagpal represents a promising early-career researcher contributing to the theoretical foundations of network science and dynamical systems.

Huibin Zhouمشاهده پروفایل

استاد

• Statistics
• Data Science
• High-dimensional Statistics
• +۵ مورد دیگر

Huibin Zhou is the Henry Ford II Professor of Statistics and Data Science at Yale University, where he has held faculty positions since 2004. He served as Chair of the Department of Statistics and Data Science (2017-2021) and earlier as Chair of the Department of Statistics (2012-2017). Professor Zhou earned his Ph.D. from Cornell University in 2004, progressing through academic ranks from Assistant Professor to full Professor at Yale. His research spans high-dimensional statistics, nonparametric estimation, machine learning, and statistical decision theory. Key focus areas include minimax optimality in estimation, Bayesian methods, network analysis, and functional data. Zhou's work frequently bridges theoretical foundations with applications in social networks, signal processing, and computational biology. Analysis of his publications reveals consistent emphasis on theoretical guarantees for statistical methods, with recent work exploring spectral clustering, Gaussian mixtures, variational inference, and privacy-preserving estimation. His research demonstrates deep engagement with minimax optimality, high-dimensional challenges, and computational-statistical tradeoffs. Honors & Awards: Henry Ford II Professorship at Yale University Professor Zhou has taught graduate and undergraduate courses including Probability Theory, Statistical Decision Theory, Nonparametric Estimation, Data Mining, and Functional Data Analysis. He organized several NSF workshops including the 2012 Workshop on High-Dimensional Data and the 2015 Workshop on Empirical Processes.