Li, Ang

Senior Research Scientist
Google DeepMind

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I am a Senior Research Scientist at Google DeepMind, Mountain View, CA. My research aims at developing deep and reinforced AI systems that are scalable and feasible for learning from the real world. My recent works including automatic neural network training systems and reinforcement learning based visual navigation in Google Streetview and Google Earth. Sitting at Google Headquarters, I also work closely with many engineering teams to land AI technologies into Alphabet products. A recent collaboration where I was heavily involved is applying Population Based Training into Google's self-driving cars. I am also an AI coach in Google Launchpad Accelerator to help non-profit organizations apply AI techniques to address environmental problems such as climate change.

I received my Ph.D. degree in August 2017 from the University of Maryland College Park, advised by Prof. Larry S. Davis, and B.Sc. degree in July 2011 from Nanjing University. I was a research associate in the Robotics Institute at Carnegie Mellon University in Pittsburgh from 2011 to 2012. During my past, I have worked on a variety of topics in computer vision, large scale deep learning, weakly supervised learning, language and vision, reinforcement learning and deep learning lifecycle management systems.



  • Learning to Incentivize Other Learning Agents
    Jiachen Yang, Ang Li, Mehrdad Farajtabar, Peter Sunehag, Edward Hughes and Hongyuan Zha
    AAMAS 2020 - Adaptive and Learning Agents (ALA) Workshop.
  • Orthogonal Gradient Descent for Continual LearningPDF ] [ Bibtex ]
    Mehrdad Farajtabar, Navid Azizan, Alex Mott, Ang Li
    To appear in AISTATS 2020
  • Prediction, Consistency, Curvature: Representation Learning for Locally-Linear ControlPDF ] [ Bibtex ]
    Nir Levine, Yinlam Chow, Rui Shu, Ang Li, Mohammad Ghavamzadeh, Hung Bui
    To appear in ICLR 2020
    - Poster at BayLearn 2019.
  • Improved Knowledge Distillation via Teacher AssistantPDF ] [ Bibtex ]
    Seyed-Iman Mirzadeh, Mehrdad Farajtabar, Ang Li, Nir Levine, Akihiro Matsukawa, Hassan Ghasemzadeh
    To appear in AAAI 2020.
    - Poster at BayLearn 2019


  • Data Efficient Training for Reinforcement Learning with Adaptive Behavior Policy Sharing
    Ge Liu, Heng-Tze Cheng, Rui Wu, Jing Wang, Jayiden Ooi, Lihong Li, Ang Li, Sibon Li, Craig Boutilier.
    - NeurIPS 2019 Deep Reinforcement Learning Workshop, Vancouver, BC.
    - NeurIPS 2019 Optimization Foundations for Reinforcement Learning Workshop, Vancouver, BC.
  • Cross-View Policy Learning for Street NavigationPDF ] [ Bibtex ]
    Ang Li*, Huiyi Hu*, Piotr Mirowski, Mehrdad Farajtabar
    ICCV 2019.
    - Short paper at ICML 2019 Multitask and Lifelong RL workshop, Long Beach, CA.
    - Poster at BayLearn 2019.
  • Layout-induced Video Representation for Recognizing Agent-in-Place ActionsPDF ] [ Bibtex ]
    Ruichi Yu, Hongcheng Wang, Ang Li, Jingxiao Zheng, Vlad I. Morariu, Larry S. Davis
    ICCV 2019.
    - Poster at BayLearn 2019.
  • A Generalized Framework for Population Based TrainingPDF ] [  Youtube  ] [ Bibtex ]
    Ang Li, Ola Spyra, Sagi Perel, Valentin Dalibard, Max Jaderberg, Chenjie Gu, David Budden, Tim Harley, Pramod Gupta
    KDD 2019.
    - Short paper at NeurIPS 2018 Workshop on Systems for ML, Montréal, Canada.
  • Improving Open Set Domain Adaptation Using Image-to-Image Translation
    Hongjie Zhang, Ang Li, Xu Han, Zhaoming Chen, Yang Zhang, Yanwen Guo
    ICME 2019.
  • SNR: Sub-Network Routing for Flexible Parameter Sharing in Multi-task LearningPDF ]
    Jiaqi Ma, Zhe Zhao, Jilin Chen, Ang Li, Lichan Hong, Ed H. Chi
    AAAI 2019, Honolulu, Hawaii.


  • C-WSL: Count-guided Weakly Supervised LocalizationPDF ] [ Bibtex ]
    Mingfei Gao, Ang Li, Ruichi Yu, Vlad I. Morariu, Larry S. Davis
    ECCV 2018, Munich, Germany.
  • NISP: Pruning Networks using Neuron Importance Score PropagationPDF ] [ Bibtex ]
    Ruichi Yu, Ang Li, Chun-Fu Chen, Jui-Hsin Lai, Vlad I. Morariu, Xintong Han, Mingfei Gao, Ching-Yung Lin, Larry S. Davis
    CVPR 2018, Salt Lake City, Utah.
  • Dynamic Zoom-in Network for Fast Object Detection in Large ImagesPDF ] [ Bibtex ]
    Mingfei Gao, Ruichi Yu, Ang Li, Vlad I. Morariu, Larry S. Davis
    CVPR 2018, Salt Lake City, Utah.







Professional Service

  • Journal Reviewer:
    • IEEE Transaction on Pattern Analysis and Machine Intelligence (TPAMI) 2019, 2020
    • IEEE Transactions on Neural Networks and Learning Systems (TNNLS) 2020
    • IEEE Transaction on Image Processing (TIP) 2017
    • The Visual Computer Journal (TVCJ) 2016
    • SPIE Journal of Electronic Imaging (JEI) 2014, 2016, 2018, 2019
    • IEEE Transaction on Cybernetics 2014
    • Robotics and Autonomous Systems 2018
    • Machine Learning 2019
  • Senior Program Committee:
    • International Joint Conference on Artificial Intelligence (IJCAI) 2020
    • International Conference on Autonomous Agents and Multi-Agent Systems (AAMAS) 2020
  • Program Committee or Reviewer:
    • International Conference on Learning Representations (ICLR) 2018, 2019, 2020
    • Annual Conference on Neural Information Processing Systems (NeurIPS) 2017, 2018, 2019
    • IEEE Conference on Computer Vision and Pattern Recognition (CVPR) 2017, 2018, 2019
    • International Conference on Machine Learning (ICML) 2017, 2018, 2019
    • IEEE International Conference on Computer Vision (ICCV) 2015, 2017, 2019
    • European Conference on Computer Vision (ECCV) 2018
    • International Joint Conference on Artificial Intelligence (IJCAI) 2019
    • International Conference on Artificial Intelligence and Statistics (AISTATS) 2018, 2020
    • Asian Conference on Computer Vision (ACCV) 2018
    • Asian Conference on Machine Learning (ACML) 2019
    • Winter Conference on Computer Vision (WACV) 2020
    • IEEE International Conference on Biometrics (BTAS) 2012, 2015
    • ACM International Conference on Multimedia (MM) 2014