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Yuxin Wu

Research Engineer

Facebook AI Research

About Me

I’m a research engineer at Facebook AI Research. Previously, I obtained my Master’s degree in computer vision at Carnegie Mellon University.

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.

Education

  • Master in Computer Vision, 2016

    Carnegie Mellon University

  • Bachelor in Computer Science, 2015

    Tsinghua University

Selected Publications

Render high-quality segmentation boundaries.
Tech Report, 2019

Reaching/surpassing supervised pre-training.
Tech Report, 2019

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

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

Workshop in International Conference on Learning Representations (ICLR), 2018

Conference on Neural Information Processing Systems (NeurIPS), 2017 (Oral)

Award-winning solution for training AI to play FPS games.
International Conference on Learning Representations (ICLR), 2017

Comprehensive results on low bitwidth neural network.
Tech Report, 2016

Open Source Projects

More at github.

Detectron2

FAIR’s next-generation research platform & production library for object detection, segmentation, and more.

Tensorpack

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

Tensorpack R-CNN

The best-performing open source TensorFlow reimplementation of Faster/Mask/Cascade R-CNN.

Adversarial Attack on Face Recognition

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

OpenPano

Panorama stitching written from scratch in C++.

wechat-dump

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

Ray Tracing Engine

Photorealistic rendering with Monte Carlo path tracing.

Work Experience

 
 
 
 
 

Research Engineer

Facebook AI Research

March 2017 – Present Menlo Park
Research & systems in computer vision & reinforcement learning.
 
 
 
 
 

Research Assistant

Oculus Research

January 2016 – April 2016 Pittsburgh
Generative modeling.
 
 
 
 
 

Researcher

Megvii (aka Face++)

October 2014 – August 2015 Beijing
Research & products in computer vision.
Neural network training framework.
 
 
 
 
 

Software Engineering Intern

Google

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

Awards

  • 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.

    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.