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Faculty
Cheng Peng
Cheng Peng is an assistant professor of data science at the University of Virginia. His work focuses on enabling machines to perceive and understand the world through images and videos, particularly in unconstrained and high-stake scenarios related to health, national security, etc. Peng's research has been frequently published at major peer-reviewed venues around Computer Vision, Machine Learning, and Medical Image Analysis (i.e., CVPR, ECCV, ICCV, NIPS, ICLR, MICCAI). His ongoing research topics are on 3D/4D reconstruction, generative modeling, Vision-Language understanding.
Prior to joining UVA, Peng was an Assistant Research Professor at The Johns Hopkins Mathematical Institute for Data Science. He holds a Ph.D. in Computer Science from Johns Hopkins University and an M.S/B.S. in Electrical and Computer Engineering from the University of Maryland.
Education
- Ph.D. in Computer Science, Johns Hopkins University
- M.S./B.S. in Electrical and Computer Engineering, University of Maryland
Data Science Domains
Analytics
Areas of Practice
Artificial Intelligence and Machine Learning; Biomedical, Health, and Life Sciences; Engineering, Robotics, and Physical Sciences ; Sports Science and Analytics; Theory, Foundations, and Advanced Methodologies
Selected Publications
Lu, Taiming, Tianmin Shu, Junfei Xiao, Luoxin Ye, Jiahao Wang, Cheng Peng, Chen Wei, Daniel Khashabi, Rama Chellappa, Alan L. Yuille and Jieneng Chen. “GenEx: Generating an Explorable World.” arXiv preprint, January 2025. arXiv:2412.09624.
Peng, Cheng, Yutao Tang, Yifan Zhou, Nengyu Wang, Xijun Liu, Deming Li and Rama Chellappa. “BAGS: Blur Agnostic Gaussian Splatting Through Multi-Scale Kernel Modeling.” arXiv preprint, March 2024. arXiv:2403.04926.
Peng, Cheng, Pengfei Guo, S. Kevin Zhou, Vishal Patel and Rama Chellappa. “Towards Performant and Reliable Undersampled MR Reconstruction via Diffusion Model Sampling.” arXiv preprint, March 2022. arXiv:2203.04292.