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.
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
- 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 - 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 - 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 - Adaptive Incentive Design with Multi-Agent Meta-Gradient Reinforcement Learning | AAMAS 2022
Jiachen Yang, Ethan Wang, Rakshit Trivedi, Tuo Zhao, Hongyuan Zha - 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 - 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 - 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 - Adaptive Incentive Design with Multi-Agent Meta-Gradient Reinforcement Learning | AAMAS 2022
Jiachen Yang, Ethan Wang, Rakshit Trivedi, Tuo Zhao, Hongyuan Zha - 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 - 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 - 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 - 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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