Zhishuai Zhang is a computer scientist and researcher known for his work in computer vision, autonomous driving, and artificial intelligence. He is a Research Scientist at Meta's Superintelligence team, having previously worked as a software engineer at Waymo. ^{[1] [2]}

Education

Zhang earned a Bachelor of Engineering degree in Computer Science from the University of Science and Technology of China in 2016. He then moved to the United States to pursue graduate studies at Johns Hopkins University. There, he joined the Computational Cognition, Vision, and Learning (CCVL) group, conducting research under the supervision of Professor Alan Yuille. He completed his Ph.D. in Computer Science, with research focusing on topics within computer vision and machine learning. ^{[1] [4]}

Career

Zhang's career in technology and research began with an internship at Yitu Tech in Shanghai from July to October 2015. While a Ph.D. candidate at Johns Hopkins University, he worked as a Research Assistant from August 2016 to August 2020. He gained further industry experience through several internships, including a role as a Research Intern at Facebook in Menlo Park for three months in 2018. He later interned at Waymo as a Software Engineering Intern from May to December 2019. After his doctoral studies, Zhang joined Waymo's Mountain View office as a full-time Software Engineer in September 2020, contributing to the development of autonomous driving systems. In mid-2025, he was recruited to join Meta's newly established Superintelligence research group as a Research Scientist, where his work reportedly focuses on segmentation models.

Zhang has authored and co-authored numerous papers presented at major computer science conferences. His research interests include autonomous driving and computer vision, with a focus on object detection, semantic segmentation, and adversarial examples in machine learning. He also maintains public repositories on GitHub for some of his research projects, including the source code for "Robust Face Detection via Learning Small Faces on Hard Images."

His significant publications include:

• STINet: Spatio-Temporal-Interactive Network for Pedestrian Detection and Trajectory Prediction: Published in the 2020 Conference on Computer Vision and Pattern Recognition (CVPR). Zhang was the lead author on this paper.
• Robust Face Detection via Learning Small Faces on Hard Images: Presented at the 2020 Winter Conference on Applications of Computer Vision (WACV). Zhang was the lead author.
• Lesion Detection by Efficiently Bridging 3D Context: Published in the 2019 International Workshop on Machine Learning in Medical Imaging (MLMI). Zhang was the lead author.
• Single-Shot Object Detection with Enriched Semantics: Presented at the 2018 Conference on Computer Vision and Pattern Recognition (CVPR). Zhang was the lead author.
• DeepVoting: An Explainable Framework for Semantic Part Detection under Partial Occlusion: Also presented at CVPR 2018, this work was a collaboration where Zhang was credited with equal contribution.
• Adversarial Examples for Semantic Segmentation and Object Detection: Published in the 2017 International Conference on Computer Vision (ICCV), with Zhang credited with equal contribution.
• Detecting Semantic Parts on Partially Occluded Objects: Presented at the 2017 British Machine Vision Conference (BMVC), with Zhang credited with equal contribution.

These publications reflect his contributions to various areas of computer vision and machine learning. ^{[1] [3] [5] [2] [4] [6]}