About Me

At Facebook AI Research (FAIR) I work on research problems in computer vision, deep learning, reinforcement learning. I also enjoy building powerful frameworks and infrastructure for research, such as detectron2 and tensorpack.


  • Master in Computer Vision, 2016

    Carnegie Mellon University

  • Bachelor in Computer Science, 2015

    Tsinghua University

Selected Publications

Rethinking "Batch" in BatchNorm

Tech Review, 2021

Momentum Contrast for Unsupervised Visual Representation Learning

Computer Vision and Pattern Recognition (CVPR), 2020 (Oral)
Best Paper Nomination (top 30)

PointRend: Image Segmentation as Rendering

Computer Vision and Pattern Recognition (CVPR), 2020 (Oral)

Feature Denoising for Improving Adversarial Robustness

The first ImageNet classifier that survives strong white-box adversarial attacks.
Computer Vision and Pattern Recognition (CVPR), 2019

Group Normalization

A batch-independent alternative to Batch Normalization.
European Conference on Computer Vision (ECCV), 2018 (Oral)
Best Paper Honorable Mention (top 3)

House3D: A Rich and Realistic 3D Environment

International Conference on Learning Representations (ICLR), 2018

ELF: An Extensive, Lightweight and Flexible Research Platform for Real-time Strategy Games

Platform that powers large-scale RL projects such as OpenGO.
Neural Information Processing Systems (NeurIPS), 2017 (Oral)

Open Source Projects

More at github.


Research platform & production library for object detection, segmentation, and more. Widely used in research community and dozens of Facebook products & services.


A neural net training interface on TensorFlow, with focus on speed + flexibility.

Adversarial Attack on Face Recognition

Black-box adversarial attacks on AWS/Azure’s public face recognition APIs, the first successful attack of its kind.


Panorama stitching written from scratch in C++.


Cracking encrypted wechat message history from Android phones by reverse-engineering.

Work Experience


Research Engineer

Facebook AI Research

March 2017 – Present Menlo Park
Build systems and perform research in computer vision & reinforcement learning.

Research Assistant

Oculus Research

January 2016 – April 2016 Pittsburgh
Research about face modeling and GANs towards VR avatars.


Megvii (aka Face++)

October 2014 – August 2015 Beijing
In-house deep learning training framework.
Research & products in computer vision, including OCR, model quantization, etc.

Software Engineering Intern


April 2014 – July 2014 Beijing
SfM (structure from motion) from videos.


  • Winner of defense track in Competition on Adversarial Attacks and Defenses (CAAD) 2018.

    We trained an ImageNet classifier with state-of-the-art robustness against adversarial attacks. Competition

  • Winner of CTF in Competition on Adversarial Attacks and Defenses (CAAD) CTF 2018.

    We performed successful adversarial attacks / defenses against other teams during the on-site competition. Competition Media (Chinese)

  • Best Paper Honorable Mention award in ECCV 2018. Announcement

  • Google Open Source Peer Bonus in 2017. Announcement

  • Winner of VizDoom AI Competition in CIG 2016.

    Our Doom bot, “F1”, beat all other competitors in a death match by a large margin. Competition Media Video

  • Champion of Student Cluster Competition in both ISC 2015 and ASC 2015.

    Optimize software and hardware for low-power high-performance computation. Media Media

  • Finalist in SIGMOD Programming Contest in SIGMOD 2014, as team leader of “blxlrsmb”.

    Design and implement a large social network database to support efficient queries. Competition Slides Poster

  • Capture the Flags (CTF, a form of security competition): 11th in SECCON CTF 2014, 8th in Codegate CTF 2015, 5th in DEFCON CTF 2015 as a team member of “blue-lotus”.