Nathan Lawrence

Nathan Lawrence

Nathan is a PhD student in applied mathematics at UBC. Prior to moving to Vancouver, he earned his Bachelor's and Master's degrees in mathematics at Portland State University. He is interested in the interplay between reinforcement learning and control. More specifically, his work aims to develop actionable methods based on deep reinforcement learning for maintenance-free PID control and MPC of industrial processes. Outside of research, he enjoys boardgames and ice skating.



📚 Program/Degree
PhD, since 2018
📝 Research
Deep Reinforcement Learning Algorithms for Maintenance-free Control in Industrial Applications
👥 Co-supervisor(s)
Philip Loewen (Math)
📨 Contact
Lawrence@math.ubc.ca

DAIS Lab Publications

  1. Conference Proceedings
    Almost Surely Stable Deep Dynamics
    Nathan P. Lawrence, Philip D. Loewen, Michael G. Forbes, Johan U. Backstrom and R. Bhushan Gopaluni
    In Proceedings of Advances in Neural Information Processing Systems 33 (NeurIPS 2020, To Appear). 2020 [PDF] [Poster]
  2. Conference Proceedings
    Modern Machine Learning Tools for Monitoring and Control of Industrial Processes: A Survey
    R. Bhushan Gopaluni, Aditya Tulsyan, Benoit Chachuat, Biao Huang, Jong Min Lee, Faraz Amjad, Seshu Kumar Damarla, Jong Woo Kim, Nathan P. Lawrence
    In proceedings of IFAC World Congress (To Appear). 2020 [PDF] [Supplementary Info]
  3. Conference Proceedings
    Optimal PID and Antiwindup Control Design as a Reinforcement Learning Problem
    Nathan P. Lawrence, Gregory E. Stewart, Philip D. Loewen, Michael G. Forbes, Johan U. Backstrom, R. Bhushan Gopaluni
    In proceedings of IFAC World Congress (To Appear). 2020 [PDF] [Video]
  4. Conference Proceedings
    Reinforcement Learning based Design of Linear Fixed Structure Controllers
    Nathan P. Lawrence, Gregory E. Stewart, Philip D. Loewen, Michael G. Forbes, Johan U. Backstrom, R. Bhushan Gopaluni
    In proceedings of IFAC World Congress (To Appear). 2020 [PDF] [Video]
  5. Journal Paper Top 10% Most Downloaded
    Towards Self-Driving Processes: A Deep Reinforcement Learning Approach to Control
    Steven P. Spielberg, Aditya Tulsyan, Nathan P. Lawrence, Philip D. Loewen, R. Bhushan Gopaluni
    AIChE Journal. 2019 [PDF]

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