Bo Liu

I am a PhD student of Computer Science at University of Texas at Austin. I am very honored to be advised by Prof. Peter Stone and Prof. Qiang Liu. I have a broad interest in reinforcement learning, continual learning, imitation learning and their applications in robotics. Before coming to UT, I was a master student of Computer Science at Stanford University, where I was fortunate to work with Prof. Fei-Fei Li and Prof. Yuke Zhu on planning under uncertainty, and Prof. Jure Leskovec on massive data mining. I finished my undergraduate study at the Johns Hopkins University, where I was fortunate to work with Prof. Jason Eisner on generative syntactic parsing.



  • APPLD: Adaptive Planner Parameter Learning from Demonstration
    Xuesu Xiao*, Bo Liu*, Garrett Warnell, Jonathan Fink, Peter Stone
    RA-L / IROS 2020
  • DualSMC: Tunneling Differentiable Filtering and Planning under Continuous POMDPs
    Yunbo Wang*, Bo Liu*, Jiajun Wu, Yuke Zhu, Simon S. Du, Li Fei-Fei, Joshua B. Tenenbaum
    IJCAI 2020
  • Predicting pregnancy using large-scale data from a women’s health tracking mobile application
    Bo Liu*, Shuyang Shi*, Yongshang Wu*, Daniel Thomas, Laura Symul, Emma Pierson, Jure Leskovec
    WWW 2019


  • Paper review on "Learning from Untrusted Data"

Teaching Experience

  • Teaching Assistant

    Winter 2019 | Stanford, CA, USA

    CS 234: Reinforcement Learning
  • Teaching Assistant

    Fall 2016 | Baltimore, MD, USA

    EN 601.465/665: Natural Language Processing

    Fall 2015 | Baltimore, MD, USA

    EN 600.363/463: Algorithms I

Working Experience

  • Research Engineer Intern

    Jun - Sept 2018 | Sunnyvale, CA, USA

  • Software Engineer & Engineer Practicum Intern

    May - Aug 2015/2016 | Los Angeles, CA, USA