Publications

Most papers are linked to arXiv or a DOI. If you can’t find one, feel free to get in touch. You can also find my articles on my Google Scholar profile.

  1. Journal Papers
    1. Automated deep reinforcement learning for real-time scheduling strategy of multi-energy system integrated with post-carbon and direct-air carbon captured system
      , , , ,
      Applied Energy. 2023.
      [Link] [arXiv]
    2. Meta-reinforcement learning for the tuning of PI controllers: An offline approach
      , , , , ,
      Journal of Process Control. 2022.
      [Link] [arXiv]
    3. Deep reinforcement learning with shallow controllers: An experimental application to PID tuning
      , , , , ,
      Control Engineering Practice. 2022.
      [Link] [arXiv]
    4. Toward self‐driving processes: A deep reinforcement learning approach to control
      , , , ,
      AIChE Journal. 2019.
      [Link] [arXiv]
  2. Conference Proceedings
    1. Reinforcement learning with partial parametric model knowledge
      , , , ,
      IFAC World Congress. 2023.
      [Link] [arXiv]
    2. A modular framework for stabilizing deep reinforcement learning control
      , , , ,
      IFAC World Congress. 2023.
      [Link] [arXiv]
    3. Meta-reinforcement learning for adaptive control of second order systems
      , , , , ,
      AdCONIP. 2022.
      [Link] [arXiv]
    4. A meta-reinforcement learning approach to process control
      , , , , ,
      AdCHEM. 2021.
      [Link] [arXiv] | Keynote Paper
    5. Reinforcement learning based design of linear fixed structure controllers
      , , , , ,
      IFAC World Congress. 2020.
      [Link] [arXiv]
    6. Optimal PID and antiwindup control design as a reinforcement learning problem
      , , , , ,
      IFAC World Congress. 2020.
      [Link] [arXiv]
    7. Almost Surely Stable Deep Dynamics
      , , , ,
      NeurIPS. 2020.
      [Link] [arXiv] | NeurIPS Spotlight
    8. Modern machine learning tools for monitoring and control of industrial processes: A survey
      , , , , , , , ,
      IFAC World Congress. 2020.
      [Link] [arXiv]
  3. Theses
    1. Deep reinforcement learning agents for industrial control system design

      The University of British Columbia. 2023.
      [Link]
    2. Convex and nonconvex optimization techniques for the constrained Fermat-Torricelli problem

      Portland State University. 2016.
      [Link]