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Kaiming He

Associate Professor with tenure, Department of EECS, MIT

Kaiming He is an Associate Professor with tenure in the Department of EECS at MIT and also works part-time as a Distinguished Scientist at Google DeepMind, with research in computer vision and deep learning. Kaiming He is first author of the 2015 ResNet paper and of the Mask R-CNN, MoCo, and Masked Autoencoders (MAE) papers, and Kaiming He's site also lists Faster R-CNN among this work on visual perception. Before joining MIT in 2024, Kaiming He was a research scientist at Facebook AI Research (2016–2024) and a researcher at Microsoft Research Asia (2011–2016). Courses taught at MIT include deep learning, computer vision, and deep generative models.12345

At a glance

Current roles
Associate Professor with tenure, Department of EECS +1 more
Sector
University
Education
MIT, Tsinghua
Work listed
4
Honors
9
Sources cited
8
Profile reviewed

Career

Kaiming He worked as a researcher at Microsoft Research Asia from 2011 to 2016, then as a research scientist at Facebook AI Research from 2016 to 2024. In 2024 Kaiming He joined the Department of EECS at MIT, where the appointment is now an associate professorship with tenure. Kaiming He also works part-time as a Distinguished Scientist at Google DeepMind.1

Research

The research of Kaiming He spans computer vision and deep learning. The 2015 ResNet paper, written with Xiangyu Zhang, Shaoqing Ren, and Jian Sun, introduced the ResNet design of shortcut connections; the MIT homepage notes that these are now used in Transformers, AlphaFold, and many generative models.12

In visual perception, Kaiming He co-authored Faster R-CNN and was first author of Mask R-CNN, which extends object detection to instance segmentation. In self-supervised learning, Kaiming He was first author of Momentum Contrast (MoCo) and Masked Autoencoders (MAE), two methods for learning visual representations.1345

More recent work at MIT turns to generative modeling, including autoregressive image generation without vector quantization, one-step generation with Mean Flows, and a 2025 report titled "Back to Basics: Let Denoising Generative Models Denoise".16

Teaching

At MIT, Kaiming He has taught Advances in Computer Vision (Spring 2024), Deep Generative Models (Fall 2024), Deep Learning (Fall 2025 and Fall 2026), and Introduction to Computer Vision (Spring 2026). Kaiming He has also given tutorials on ResNets, object detection, and visual recognition at ICML, ICCV, CVPR, and ECCV.1

Work and contributions

  • ResNet Published research

    First author, with Xiangyu Zhang, Shaoqing Ren, and Jian Sun, of the 2015 paper that introduced ResNets.21

  • Masked Autoencoders (MAE) Published research

    First author of "Masked Autoencoders Are Scalable Vision Learners" (2021) with Xinlei Chen, Saining Xie, Yanghao Li, Piotr Dollár, and Ross Girshick; the facebookresearch/mae repository, a PyTorch re-implementation of the paper, is released under the CC-BY-NC 4.0 license.57

  • Mask R-CNN and Detectron Open-source code

    First author of the Mask R-CNN paper (2017). Detectron, FAIR's Apache-2.0 object detection system that implements Mask R-CNN, lists Kaiming He among the authors in its citation entry.38

  • Mean Flows for One-step Generative Modeling Published research

    Co-author, with Zhengyang Geng, Mingyang Deng, Xingjian Bai, and J. Zico Kolter, of a 2025 paper on one-step generative modeling; it was presented as an oral at NeurIPS 2025.61

Timeline

  1. Joined Microsoft Research Asia as a researcher.1
  2. Released the ResNet paper on image recognition on arXiv.2
  3. Joined Facebook AI Research as a research scientist.1
  4. Released the Mask R-CNN paper on arXiv.3
  5. Released the Momentum Contrast (MoCo) paper on arXiv.4
  6. Released Masked Autoencoders Are Scalable Vision Learners on arXiv.5
  7. Joined the MIT Department of EECS as a faculty member.1
  8. Released Mean Flows for One-step Generative Modeling on arXiv.6

Other documented roles

Roles the sources document that are not confirmed as current.

Honors and recognition

  • CVPR Best Paper Award · IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 20091
  • CVPR Best Paper Award · IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 20161
  • ICCV Best Paper Award · International Conference on Computer Vision (ICCV), 20171
  • ICCV Best Student Paper Award · International Conference on Computer Vision (ICCV), 20171
  • PAMI Young Researcher Award · IEEE Technical Committee on Pattern Analysis and Machine Intelligence, 20181
  • Everingham Prize · International Conference on Computer Vision (ICCV), 20211
  • ICCV Test of Time Award · International Conference on Computer Vision (ICCV), 20251
  • NeurIPS Test of Time Award · Conference on Neural Information Processing Systems (NeurIPS), 20251
  • CVPR 10-year Test of Time Award · IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 20261

Where they work

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References

8 sources · profile reviewed Oct 21, 2026 · data updated Oct 21, 2026

  1. Kaiming He — homepage (MIT CSAIL)Kaiming He · Official page · accessed Oct 21, 2026
  2. Deep Residual Learning for Image Recognition (arXiv 1512.03385)arXiv · Paper · published Dec 10, 2015 · accessed Oct 21, 2026
  3. Mask R-CNN (arXiv 1703.06870)arXiv · Paper · published Mar 20, 2017 · accessed Oct 21, 2026
  4. Momentum Contrast for Unsupervised Visual Representation Learning (arXiv 1911.05722)arXiv · Paper · published Nov 13, 2019 · accessed Oct 21, 2026
  5. Masked Autoencoders Are Scalable Vision Learners (arXiv 2111.06377)arXiv · Paper · published Nov 11, 2021 · accessed Oct 21, 2026
  6. Mean Flows for One-step Generative Modeling (arXiv 2505.13447)arXiv · Paper · published May 19, 2025 · accessed Oct 21, 2026
  7. facebookresearch/mae README (citation and CC-BY-NC 4.0 license)Meta (GitHub repository) · Repository · accessed Oct 21, 2026
  8. facebookresearch/Detectron README (citation entry and license)Meta (GitHub repository) · Repository · accessed Oct 21, 2026