Jiachen Yang

Co-Founder and CTO @ Simular

Bio

I pioneer the creation of autonomous agents for the long-term needs of diverse real-world applications. As a staff scientist at the Lawrence Livermore National Laboratory, I drove the advance of novel machine learning algorithms for computational science. I led the creation of agents with emergent cooperative behavior and skills to solve social dilemmas, team sports games, and multi-agent driving challenges at DeepMind, Electronic Arts, and Honda Research Institute. My main area of expertise is multi-agent deep reinforcement learning, with other publications on deep learning, meta-learning, and AI for science. I have a consistent track record of independently creating research agendas and owning projects from ideation to paper publication.

I completed a PhD in Machine Learning at the Georgia Institute of Technology, supervised by Prof. Hongyuan Zha and Prof. Tuo Zhao, with a dissertation on Cooperation in Multi-Agent Reinforcement Learning. I received an M.S. in CS from Georgia Tech and a B.S. in EECS from UC Berkeley.

Publications

  1. Multi-Agent Reinforcement Learning for Adaptive Mesh Refinement | AAMAS 2023

    Jiachen Yang, Ketan Mittal, Tarik Dzanic, Socratis Petrides, Brendan Keith, Brenden Petersen, Daniel Faissol, Robert Anderson
  2. Reinforcement Learning for Adaptive Mesh Refinement | AISTATS 2023

    Jiachen Yang, Tarik Dzanic, Brenden Petersen, Jun Kudo, Ketan Mittal, Vladimir Tomov, Jean-Sylvain Camier, Tuo Zhao, Hongyuan Zha, Tzanio Kolev, Robert Anderson, Daniel Faissol
  3. A Unified Framework for Deep Symbolic Regression | NeurIPS 2022

    Mikel Landajuela, Chak Lee, Jiachen Yang, Ruben Glatt, Claudio P. Santiago, Ignacio Aravena, Terrell N. Mundhenk, Garrett Mulcahy, Brenden K. Petersen
  4. Adaptive Incentive Design with Multi-Agent Meta-Gradient Reinforcement Learning | AAMAS 2022

    Jiachen Yang, Ethan Wang, Rakshit Trivedi, Tuo Zhao, Hongyuan Zha
  5. Multi-Agent Reinforcement Learning for Adaptive Mesh Refinement | AAMAS 2023

    Jiachen Yang, Ketan Mittal, Tarik Dzanic, Socratis Petrides, Brendan Keith, Brenden Petersen, Daniel Faissol, Robert Anderson
  6. Reinforcement Learning for Adaptive Mesh Refinement | AISTATS 2023

    Jiachen Yang, Tarik Dzanic, Brenden Petersen, Jun Kudo, Ketan Mittal, Vladimir Tomov, Jean-Sylvain Camier, Tuo Zhao, Hongyuan Zha, Tzanio Kolev, Robert Anderson, Daniel Faissol
  7. A Unified Framework for Deep Symbolic Regression | NeurIPS 2022

    Mikel Landajuela, Chak Lee, Jiachen Yang, Ruben Glatt, Claudio P. Santiago, Ignacio Aravena, Terrell N. Mundhenk, Garrett Mulcahy, Brenden K. Petersen
  8. Adaptive Incentive Design with Multi-Agent Meta-Gradient Reinforcement Learning | AAMAS 2022
    Jiachen Yang, Ethan Wang, Rakshit Trivedi, Tuo Zhao, Hongyuan Zha
  9. Multi-Agent Reinforcement Learning for Adaptive Mesh Refinement | AAMAS 2023

    Jiachen Yang, Ketan Mittal, Tarik Dzanic, Socratis Petrides, Brendan Keith, Brenden Petersen, Daniel Faissol, Robert Anderson
  10. Reinforcement Learning for Adaptive Mesh Refinement | AISTATS 2023

    Jiachen Yang, Tarik Dzanic, Brenden Petersen, Jun Kudo, Ketan Mittal, Vladimir Tomov, Jean-Sylvain Camier, Tuo Zhao, Hongyuan Zha, Tzanio Kolev, Robert Anderson, Daniel Faissol
  11. A Unified Framework for Deep Symbolic Regression | NeurIPS 2022
    Mikel Landajuela, Chak Lee, Jiachen Yang, Ruben Glatt, Claudio P. Santiago, Ignacio Aravena, Terrell N. Mundhenk, Garrett Mulcahy, Brenden K. Petersen
  12. Adaptive Incentive Design with Multi-Agent Meta-Gradient Reinforcement Learning | AAMAS 2022
    Jiachen Yang, Ethan Wang, Rakshit Trivedi, Tuo Zhao, Hongyuan Zha

Academic Servicet

  • Reviewer:
    • Neural Information Processing Systems (NeurIPS)
    • International Conference on Learning Representations (ICLR)
    • Artificial Intelligence and Statistics (AISTATS)
    • Transactions on Machine Learning Research (TMLR)
  • Program committee:
    • International Joint Conference on Artificial Intelligence (IJCAI)

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