Haofan Wang

Work Email: wanghaofan (at) xiaohongshu.com
School Email: haofanw (at) alumni.cmu.edu
Personal Email: haofanwang.ai (at) gmail.com
Research Interests
  • Contrastive Learning
  • Cross-Modal Analysis
  • Self-Supervised Learning
  • Interpretable Machine Learning

Haofan Wang (王浩帆) is a senior research engineer at Xiaohongshu. Before that, he worked as research assistant at Accountable System Lab (ASL) and received master degree from Carnegie Mellon University in December, 2020. His current research interests are primarily on AIGC, especially modality-conditional motion generation and prediction (highlighted ongoing projects are MotionMAE and OpenDance-3D). If you are also interested in those topics or seeking for an internship, please drop an email. Some of the earlier works he co-authored are Score-CAM.

He worked as a research engineer at MMU of Kuaishou Technology from December, 2020 to February, 2022. He was a research intern at Image and Video Understanding Laboratory (IVUL), King Abdullah University of Science and Technology (KAUST) from August, 2020 to Jan 2021, advised by Prof. Bernard Ghanem, and collaborating with Guohao Li. He was a visiting student at DATA Lab, Texas A&M University from July, 2019 to October, 2019, advised by Prof. Xia (Ben) Hu, and collaborating with Mengnan Du and Fan Yang. He exchanged at UC Berkeley from August 2017 to December 2017, and worked as undergraduate research assistant at Berkeley RISE Lab, collaborating with Corey Zumar on Clipper. He also spent time at OpenMined, RealAI, Everspry, Horizon Robotics and Institute of Software Chinese Academy of Sciences.

Our team hires research interns all over the year. Click here for details!

Selected Publications (Full List)
Jue Wang, Haofan Wang*, Weijia Wu, Jincan Deng, Yu Lu, Xiaofeng Guo, Debing Zhang,
ICML 2022 Pre-training Workshop, * means equal contribution.
[Paper] [Code] [Citation: 4]
Zifan Wang, Haofan Wang, Shakul Ramkumar, Matt Fredrikson, Piotr Mardziel, and Anupam Datta
NeurIPS 2020, Poster
[Paper] [Poster] [Code] [Citation: 16]
IEEE CVPR 2020 Workshop on Fair, Data Efficient and Trusted Computer Vision
[Project Page] [Paper] [Slides] [News] [Citation: 324]
Haofan Wang, Zhenghua Chen, and Yi Zhou,
ArXiv 1901.06778
[Project Page] [Paper] [Citation: 10]
  • [06/2021] MMU, Kuaishou. An Overview of Large Scale Pre-Training
  • Teaching
  • TA of 18-793 Image and Video Processing, Prof: Aswin C Sankaranarayanan, Summer 2020, Carnegie Mellon University
  • Professional Services
  • Journal Reviewer: IJCV
  • Conference Reviewer: NeurIPS (2021,2022), ICLR (2022,2023), ICML (2022), ECCV (2022)
  • Mentoring
  • Pengxiang Ding (BUPT, Incoming Ph.D. Student at MonashU), Zhenyu Lou (Ph.D. student at ZJU), Yifan Yao (Master at SJSU)
  • Trajectory

    DMU (Bachelor) --> CMU (Master) --> Kuaishou (Software Engineer) --> Xiaohongshu (Senior Software Engineer) --> What's NeXT?