About Me
I am a postdoctral research associate in the Program in Applied and Computational Mathematics (PACM) at Princeton University under the supervision of Prof. Amit Singer. I obtained my Ph.D. degree in Mathematics from University of Minnesota under the supervision of Prof. Gilad Lerman.
My research interests lie at the intersection of robust estimation, nonconvex optimization, computational methods and statistics, and their applications to a variety of inverse problems in 3-D reconstruction. One particular problem I am interested in is robust group synchronization, which involves synchronization over a space with certain algebraic structure (e.g. groups), under high corruption of data. It has broad applications in 3-D reconstruction, graph matching, image alignment, community detection, phase retrieval and ranking. My current research primarily focuses on the mathematical problems that arise from cryo-electron microscopy (cryo-EM) imaging, which aims to recover the 3-D structure of macromolecules from their highly noisy 2-D images. I have been working on high performance methods (high speed and accuracy) for covariance estimation, contrast estimation, deconvolution, unsupervised denoising of cryo-EM images.
Academic Services
- Program Committee:
CVPR-2023ICLR-2023NeurIPS-2022AISTATS-2022 Top ReviewerICML-2022ICLR-2022AISTATS-2021NeurIPS-2021 - Journal Reviewer:
IEEE Robotics and Automation Letters,IEEE Transactions on Circuits and Systems for Video Technology
News
- Nov 9, 2022: I will be organizing the minisymposium “Problems and Solutions of 3-D Reconstruction” at 2023 SIAM Conference on Computational Science and Engineering at Amsterdam, Netherland!
- Nov 3, 2022: Our code for fast principal component analysis of cryo-EM images is released at https://github.com/yunpeng-shi/fast-cryoEM-PCA and will be merged into ASPIRE-python for single particle reconstruction!