I am an Applied Scientist at Amazon. I received my Ph.D. in Computer Science at the University of South Carolina, where I was advised by Prof. Qi Zhang. I also hold a Master of Engineering in Electrical Engineering and Computer Sciences from the University of California, Berkeley, and a Bachelor of Science in Computer Science from the University of Illinois at Urbana-Champaign.

My research focuses on leveraging structural properties, such as Euclidean equivariance, present in certain reinforcement learning problems to improve sample and computational efficiency. More recently, I have become interested in adapting large language models to sequential decision-making tasks.

Professional Experience

Amazon, Seattle, WA — Jan 2025 - Present | Applied Scientist

• Finetune LLMs to detect and assess defects in Amazon’s customer service chatbot responses.

Amazon, Bellevue, WA — May 2024 - Aug 2024 | Applied Scientist Intern

• Developed models for cost-of-goods prediction to improve forecasting accuracy.

Siemens, Princeton, NJ — May 2022 - Aug 2022 | Research Intern

• Applied reinforcement learning algorithms to optimize power system reconfiguration.

Publications

• Reinforcement Learning with Euclidean Data Augmentation for State-Based Continuous Control
  Jinzhu Luo, Dingyang Chen, Qi Zhang
  Neural Information Processing Systems (NeurIPS) 2024

• Convergence Rates of Bayesian Network Policy Gradient for Cooperative Multi-Agent Reinforcement Learning
  Dingyang Chen, Zhenyu Zhang, Xiaolong Kuang, Xinyang Shen, Ozalp Ozer, Qi Zhang
  Workshop on Bayesian Decision-making and Uncertainty at NeurIPS 2024

• Efficient Sequential Decision Making with Large Language Models
  Dingyang Chen, Qi Zhang, and Yinglun Zhu
  Empirical Methods in Natural Language Processing (EMNLP) 2024

• E(3)-Equivariant Actor-Critic Methods for Cooperative Multi-Agent Reinforcement Learning
  Dingyang Chen, Qi Zhang
  International Conference on Machine Learning (ICML) 2024

• Context-Aware Bayesian Network Actor-Critic Methods for Cooperative Multi-Agent Reinforcement Learning
  Dingyang Chen, Qi Zhang
  International Conference on Machine Learning (ICML) 2023

• Communication-Efficient Actor-Critic Methods for Homogeneous Markov Games
  Dingyang Chen, Yile Li, Qi Zhang
  International Conference on Learning Representations (ICLR) 2022

• Convergence and Price of Anarchy Guarantees of the Softmax Policy Gradient in Markov Potential Games
  Dingyang Chen, Qi Zhang, Thinh T. Doan
  Decision Awareness in Reinforcement Learning Workshop at ICML 2022

• A Meta-Gradient Approach to Learning Cooperative Multi-Agent Communication Topology
  Qi Zhang, Dingyang Chen
  Workshop on Meta-Learning at NeurIPS 2021

Professional Service

Conference Reviewer / Program Committee

• AAAI 2025
• ICML 2024, 2025
• ICLR 2024
• NeurIPS 2023
• NeurIPS 2022 Deep RL Workshop

Journal Reviewer

• IEEE Transactions on Neural Networks and Learning Systems (TNNLS)

Teaching Experience

• Graduate Teaching Assistant – Visualization Tools, University of South Carolina (Fall 2024)
• Graduate Teaching Assistant – Algorithmic Design I, University of South Carolina (Fall 2023)