I am currently a principal researcher at Tencent AI Lab, working on machine learning and natural language processing. Before joining Tencent in March 2018, I worked at Microsoft Research, Redmond, WA. I completed my PhD in Electrical Engineering at University of California, Los Angeles (UCLA), in June 2014, where I worked in Adaptive Systems Laboratory (ASL), supervised by Prof. Ali H. Sayed.

Contact information

Email: chenjianshu at gmail dot com OR jianshuchen at global dot tencent 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 and reasoning 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. Jiaao Chen, Xiaoman Pan, Kaiqiang Song, Dian Yu, Dong Yu, Jianshu Chen, “Skills-in-Context Prompting: Unlocking Compositionality in Large Language Models”, arXiv preprint [arXiv:2308.00304], August 2023.
2. Jianshu Chen, “Learning Language Representations with Logical Inductive Bias”, Proc. International Conference on Learning Representations (ICLR), 2023.
3. 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).
4. Y. Yang, W. Yao, H. Zhang, X. Wang, D. Yu, Jianshu Chen, “Z-LaVI: Zero-Shot Language Solver Fueled by Visual Imagination”, Proc. Conference on Empirical Methods in Natural Language Processing (EMNLP), 2022.
5. W. Chen, H. Wang, Jianshu Chen, Y. Zhang, H. Wang, S. Li, X. Zhou, W. Y. Wang, “TabFact: A Large-scale Dataset for Table-based Fact Verification”, Proc. International Conference on Learning Representations (ICLR), 2020
6. A. Liu, Jianshu Chen, M. Yu, Y. Zhai, X. Zhou, and J. Liu, “Watch the Unobserved: A Simple Approach to Parallelizing Monte Carlo Tree Search”, Proc. International Conference on Learning Representations (ICLR), April 2020. (Oral)
7. B. Dai, A. Shaw, L. Li, L. Xiao, N. He, Z. Liu, Jianshu Chen, L. Song, “SBEED Learning: Convergent Control with Nonlinear Function Approximation”, Proc. International Conference on Machine Learning (ICML), 2018.
8. S. Du, Jianshu Chen, L. Li, L. Xiao, D. Zhou, ``Stochastic Variance Reduction Methods for Policy Evaluation’’, Proc. International Conference on Machine Learning (ICML), 2017.