Prof. Kaiming He

Professor He's research covers a wide range of topics in computer vision and deep learning. Through the lens of computer vision problems, he aims to develop generalizable methods applicable to various domains. Research currently focuses on building computer models that can learn representations and develop intelligence from and for the complex world. The long-term goal of He's research is to augment human intelligence with more capable artificial intelligence.

Professor He is best known for his work on Deep Residual Networks (ResNets), with the residual connections therein now being used everywhere in modern deep learning models, including Transformers (e.g., GPT, ChatGPT), AlphaGo Zero, AlphaFold, Diffusion Models, and more. He is also known for work on visual object detection and segmentation (e.g., Faster R-CNN, Mask R-CNN) and visual self-supervised learning (e.g., MoCo, MAE). Professor He is a recipient of several prestigious awards, including the PAMI Young Researcher Award in 2018, the Best Paper Award in CVPR 2009, CVPR 2016, ICCV 2017, the Best Student Paper Award in ICCV 2017, the Best Paper Honorable Mention in ECCV 2018, CVPR 2021, and the Everingham Prize in ICCV 2021. My publications have over 500,000 citations (as of Nov. 2023) with an increase of over 100,000 per year.

Before joining MIT, He was a Research Scientist at Facebook AI Research (FAIR) from 2016 to 2024, and a Researcher at Microsoft Research Asia (MSRA) from 2011 to 2016. He received his Ph.D. degree from the Chinese University of Hong Kong in 2011, and my B.S. degree from Tsinghua University in 2007.