Kaiming He

Associate Professor, Department of Electrical Engineering and Computer Science

Kaiming He is an associate professor in the Department of Electrical Engineering and Computer Science at MIT. Prior to joining MIT, he was a research scientist at Facebook AI Research (FAIR). His research spans a range of topics in computer vision and deep learning, and through the lens of computer vision and building learning representation models, he works to develop generalizable and widely applicable methods. His work also includes deep residual networks, deep learning models, visual object detection and segmentation, and visual self-supervised learning. Prior to his current role, he also spent time in industry as a researcher at Microsoft Research Asia (MSRA). He received a PhD from the Chinese University of Hong Kong, and a BS from Tsinghua University.


  • Li, Y., Fan, H., Hu, R., Feichtenhofer, C., He, K., Meta AI, & FAIR. (2023). Scaling Language-Image pre-training via masking. CVPR. https://openaccess.thecvf.com/content/CVPR2023/papers/Li_Scaling_Language-Image_Pre-Training_via_Masking_CVPR_2023_paper.pdf
  • Feichtenhofer, C., Fan, H., Li, Y., Kaiming He, Meta AI, & FAIR. (2022). Masked Autoencoders As Spatiotemporal Learners. 36th Conference on Neural Information Processing Systems (NeurIPS 2022). https://proceedings.neurips.cc/paper_files/paper/2022/file/e97d1081481a4017df96b51be31001d3-Paper-Conference.pdf
  • Li, Y., Mao, H., Girshick, R., He, K. (2022). Exploring Plain Vision Transformer Backbones for Object Detection. In: Avidan, S., Brostow, G., Cissé, M., Farinella, G.M., Hassner, T. (eds) Computer Vision – ECCV 2022. ECCV 2022. Lecture Notes in Computer Science, vol 13669. Springer, Cham. https://doi.org/10.1007/978-3-031-20077-9_17