Jiachen Yang
Co-founder, Simular
Google Scholar | LinkedIn | GitHub | Email: last_name[0]+first_name@gmail.com
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On a mission with Li Ang at Simular to create general, reliable, and continually-learning autonomous agents.
Simular is the natural real-world extension of my journey in researching and creating 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
Publications
The Unreasonable Effectiveness of Scaling Agents for Computer Use | arXiv 2025
Gonzalo Gonzalez-Pumariega, Vincent Tu, Chih-Lun Lee, Jiachen Yang, Ang Li, Xin Eric Wang
Agent s2: A compositional generalist-specialist framework for computer use agents | COLM 2025
Saaket Agashe, Kyle Wong, Vincent Tu, Jiachen Yang, Ang Li, Xin Eric Wang
Agent s: An open agentic framework that uses computers like a human | ICLR 2025 (Best paper, Agentic AI for Science)
Saaket Agashe, Jiuzhou Han, Shuyu Gan, Jiachen Yang, Ang Li, Xin Eric Wang
Deep Symbolic Optimization: Reinforcement Learning for Symbolic Mathematics | arXiv 2025
Conor F Hayes, Felipe Leno Da Silva, Jiachen Yang, T Nathan Mundhenk, Chak Shing Lee, Jacob F Pettit, Claudio Santiago, Sookyung Kim, Joanne T Kim, Ignacio Aravena Solis, Ruben Glatt, Andre R Goncalves, Alexander Ladd, Ahmet Can Solak, Thomas Desautels, Daniel Faissol, Brenden K Petersen, Mikel Landajuela
DynAMO: Multi-agent reinforcement learning for dynamic anticipatory mesh optimization with applications to hyperbolic conservation laws | Journal of Computational Physics
Tarik Dzanic, Ketan Mittal, Dohyun Kim, Jiachen Yang, Socratis Petrides, Brendan Keith, Robert Anderson
DisCo-DSO: Coupling Discrete and Continuous Optimization for Efficient Generative Design in Hybrid Spaces | AAAI 2025
Jacob F Pettit, Chak Shing Lee, Jiachen Yang, Alex Ho, Daniel Faissol, Brenden Petersen, Mikel Landajuela
Toward Multi-Fidelity Reinforcement Learning for Symbolic Optimization | ALA Workshop, AAMAS 2023
FL Silva, Jiachen Yang, Mikel Landajuela, Andre Goncalves, Alexander Ladd, Daniel Faissol, Brenden Petersen
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 (Oral presentation)
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
Professional service
Professional service
Conferences and journals
Neural Information Processing Systems (NeurIPS)
International Conference on Learning Representations (ICLR)
Artificial Intelligence and Statistics (AISTATS)
Transactions on Machine Learning Research (TMLR)
International Joint Conference on Artificial Intelligence (IJCAI)
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