Computational Laboratory for Energy And Nanoscience

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Chris Beeler

I am currently a PhD student in Mathematics and president of the Mathematics and Statistics Graduate Student Association (MSGSA) at the University of Ottawa. Formerly I was a MSc student with CLEAN in the Modelling and Computational Science program at the University of Ontario Institute of Technology and a BSc student in the Combined Honours in Chemistry and Mathematics program at Dalhousie University.

email: christopher.beeler@uottawa.ca
github: https://github.com/beelerchris
linkedin: https://www.linkedin.com/in/chris-beeler/

Projects

Nautical Navigation: I am the lead researcher for the development of the SubWorld environment and the application of dynamic programming to it.

ChemGymRL: I am directly involved in the continuing development of the ChemGymRL environment for use in the reinforcement learning field.

Maximizing Thermal Efficiency: I was the lead researcher for the development of the Heat Engine environments and the application of both evolutionary and gradient-based reinforcement learning algoritms to these environments.


PhD research

My PhD research consists of studying how the access to information revealing actions affects the behavior of reinforcement learning agents and determining how the levels of available information changes the difficulty of a given reinforcement learning problem.


Masters research

My MSc research consisted of using reinforcement learning methods based on genetic algorithms to reproduce thermodynamic cycles without prior knowledge of physics and studying optimal solutions to various common reinforcement learning environments.


Publications / Posters / Presentations

  • C. Beeler, X. Li, M. Crowley, M. Fraser, and I. Tamblyn, "Dynamic programming with partial information to overcome navigational uncertainty in a nautical environment", arXiv preprint, arXiv:2112.14657 (December 2021)
  • C. Beeler, U. Yahorau, R. Coles, K. Mills, S. Whitelam, and I. Tamblyn, "Optimizing thermodynamic trajectories using evolutionary and gradient-based reinforcement learning", Phys. Rev. E, 104, 064128 (December 2021)
  • C. Beeler, "Max-flow Min-cut and Baseball End-of-Season Elimination", Medium (October 2021)
  • C. Beeler, "Neural Networks", Lecture, A3MD, A3MD ML Bootcamp (August 2020 & September 2020)
  • K. Mills, K. Ryczko, I. Luchak, A. Domurad, C. Beeler, and I. Tamblyn, "Extensive deep neural networks for transferring small scale learning to large scale systems", Chemical Science, 10, 4129-4140 (February 2019)
  • C. Beeler, Xinkai Li, Zihan Yang, Mark Crowley, and Isaac Tamblyn, "Navigating Chemistry", Invited Oral Presentation, Ottawa-AI Workshop 2019, Ottawa-AI Alliance (November 2019)
  • C. Beeler, U. Yahorau, R. Coles, K. Mills, S. Whitelam, and I. Tamblyn, "Learning to work efficiently: Using neuroevolutionary strategies for reinforcement learning on classical thermodynamic systems", Oral Presentation, McGill University, Physics & AI Workshop (May 2019)
  • C. Beeler and I. Tamblyn, "Perpetually Playing Physics", Oral Presentation, University of Ontario Institute of Technology, Modelling and Computational Science Seminar (April 2019)
  • C. Beeler, U. Yahorau, R. Coles, K. Mills, S. Whitelam, and I. Tamblyn, "Maximizing thermal efficiency of heat engines using neuroevolutionary strategies for reinforcement learning", Oral Presentation, American Physical Society, March Meeting 2019 (March 2019)
  • K. Ryczko, C. Beeler, R. Coles, A. Domurad, C. Homenick, I. Luchak, K. Mills, D. Strubbe, U. Yahorau, and I. Tamblyn, "Machine learning for molecules", Poster Presentation, NeurIPS, 32nd Conference on Neural Information Processing Systems (December 2018)

Awards

  • University of Ottawa Admission Scholarship (2019-Present)
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