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. Stabilizing reinforcement learning control: A modular framework for optimizing over all stable behavior
      , , , ,
      Automatica. 2024.
      [Link] [arXiv] | In press
    2. Machine learning techniques for industrial sensing and control: A survey and practical perspective
      , , , , , , , , ,
      Control Engineering Practice. 2024.
      [Link] [arXiv]
    3. 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]
    4. Meta-reinforcement learning for the tuning of PI controllers: An offline approach
      , , , , ,
      Journal of Process Control. 2022.
      [Link] [arXiv]
    5. Deep reinforcement learning with shallow controllers: An experimental application to PID tuning
      , , , , ,
      Control Engineering Practice. 2022.
      [Link] [arXiv]
    6. 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]