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
- 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 - Cooperation in Multi-Agent Reinforcement Learning | PhD thesis 2021
Jiachen Yang - Permutation Invariant Policy Optimization for Mean-Field Multi-Agent Reinforcement Learning: A Principled Approach | arXiv 2021
Yan Li, Lingxiao Wang, Jiachen Yang, Ethan Wang, Zhaoran Wang, Tuo Zhao, Hongyuan Zha - Learning to Incentivize Other Learning Agents | NeurIPS 2020
Jiachen Yang, Ang Li, Mehrdad Farajtabar, Peter Sunehag, Edward Hughes, Hongyuan Zha - Graphopt: Learning optimization models of graph formation | ICML 2020
Rakshit Trivedi, Jiachen Yang, Hongyuan Zha - Hierarchical Cooperative Multi-Agent Reinforcement Learning with Skill Discovery | AAMAS 2020
Jiachen Yang, Igor Borovikov, Hongyuan Zha - Integrating independent and centralized multi-agent reinforcement learning for traffic signal network optimization | AAMAS 2020
Zhi Zhang, Jiachen Yang, Hongyuan Zha - CM3: Cooperative multi-goal multi-stage multi-agent reinforcement learning | ICLR 2020
Jiachen Yang, Alireza Nakhaei, David Isele, Kikuo Fujimura, Hongyuan Zha - Cooperative multi-goal, multi-agent, multi-stage reinforcement learning | US patent 2020
Jiachen Yang, Alireza Nakhaei Sarvedani, David Francis Isele, Kikuo Fujimura - Single Episode Policy Transfer in Reinforcement Learning | ICLR 2020
Jiachen Yang, Brenden Petersen, Hongyuan Zha, Daniel Faissol - Deep Reinforcement Learning and Simulation as a Path Toward Precision Medicine | Journal of Computational Biology 2019
Brenden K Petersen, Jiachen Yang, Will S Grathwohl, Chase Cockrell, Claudio Santiago, Gary An, Daniel M Faissol - Learning Deep Mean Field Games for Modeling Large Population Behavior | ICLR 2018
Jiachen Yang, Xiaojing Ye, Rakshit Trivedi, Huan Xu, Hongyuan Zha - Fake news mitigation via point process based intervention | ICML 2017
Mehrdad Farajtabar, Jiachen Yang, Xiaojing Ye, Huan Xu, Rakshit Trivedi, Elias Khalil, Shuang Li, Le Song, Hongyuan Zha
Academic Service
- 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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