I am currently a Principal Scientist at Amazon, where I spearhead the development of next-generation foundational models for Amazon Stores businesses. Prior to this role, I held the position of Principal Researcher at Tencent AI Lab, specializing in the advancement of large language models. Before that, I have been working at Microsoft Research in Redmond, WA, where I delved into deep learning, natural language processing, and reinforcement learning. I earned my PhD from University of California, Los Angeles (UCLA), in June 2014, focusing on distributed learning and control.

Contact information

Email: chenjianshu at gmail dot com

Research interests

My research interests lie at the intersection of machine learning, natural language processing, and large language models. I focus on understanding and optimizing the synergy between knowledge and reasoning to develop next-generation large language model architectures and effective learning paradigms, with the objective of achieving strong compositional generalization, reasoning and planning capabilities. I am passionate about tackling large-scale AI research projects, collaborating with interdisciplinary teams to address complex challenges, and driving robust and effective innovations in AI. Additionally, I maintain an active interest in reinforcement learning and optimization.

For more details, see my publications (also google scholar)

Selected publications

1. R. Yang, X. Pan, F. Luo, S. Qiu, H. Zhong, D. Yu, Jianshu Chen, “Rewards-in-Context: Multi-objective Alignment of Foundation Models with Dynamic Preference Adjustment”, Proc. International Conference on Machine Learning (ICML), July 2024.
2. X. Zhao, H. Zhang, X. Pan, W. Yao, D. Yu, T. Wu, Jianshu Chen, “Fact-and-Reflection (FaR) Improves Confidence Calibration of Large Language Models”, arXiv preprint [arXiv:2402.17124], February, 2024.
3. J. Chen, X. Pan, K. Song, D. Yu, D. Yu, Jianshu Chen, “Skills-in-Context Prompting: Unlocking Compositionality in Large Language Models”, arXiv preprint [arXiv:2308.00304], August 2023.
4. Jianshu Chen, “Learning Language Representations with Logical Inductive Bias”, Proc. International Conference on Learning Representations (ICLR), 2023.
5. X. Pan, W. Yao, H. Zhang, D. Yu, D. Yu, Jianshu Chen, “Knowledge-in-Context: Towards Knowledgeable Semi-Parametric Language Models”, Proc. International Conference on Learning Representations (ICLR), 2023 (Spotlight).