معرفی
Zhizhen Jane Zhao is an Associate Professor in the Department of Electrical and Computer Engineering at the University of Illinois at Urbana-Champaign, where she holds the William L. Everitt Faculty Fellow position. She is affiliated with the Coordinated Science Laboratory and the National Center for Supercomputing Applications, and serves as an affiliate faculty member in both the Department of Mathematics and the Department of Statistics.
Dr. Zhao received her PhD in Physics from Princeton University in 2013, working with Amit Singer. She completed her bachelor's and master's degrees in Physics at Trinity College, Cambridge University, graduating in 2008. Prior to joining the University of Illinois in 2016, she was a Courant Instructor at the Courant Institute of Mathematical Sciences at New York University.
Her research focuses on geometric data analysis, dimensionality reduction, mathematical signal processing, scientific computing, and machine learning, with applications to imaging sciences and inverse problems. Specific application areas include cryo-electron microscopy image processing, data-driven methods for dynamical systems, and uncertainty quantification. Her methodology bridges theoretical mathematics with practical applications in biomedical imaging and scientific computing.
Analysis of Dr. Zhao's recent publications reveals a strong focus on machine learning approaches to imaging science problems, particularly in cryo-electron microscopy. She has developed innovative methods using geometric analysis, flow matching models, and multi-frequency approaches to solve inverse problems. Her research increasingly integrates physics-driven constraints with deep learning frameworks, creating more robust and interpretable models for scientific applications across biomedical imaging, climate science, and quantum computing.
Dr. Zhao has received several notable honors including:
- William L. Everitt Faculty Fellow
Dr. Zhao actively teaches courses ranging from foundational signal processing to advanced topics in machine learning and high-dimensional geometric data analysis. She has mentored numerous graduate students and postdocs working on problems at the intersection of mathematics, computer science, and domain-specific applications. Her research has been supported by various grants enabling interdisciplinary collaborations across engineering, mathematics, and computational sciences.
She is an active member of the Coordinated Science Laboratory research community at UIUC, collaborating with researchers across disciplines on projects involving imaging science, machine learning, and computational methods. Her work often involves interdisciplinary teams combining expertise in mathematics, computer science, and domain-specific applications in biomedical imaging, climate science, and quantum physics.
