Chengrui Gao (高成睿) Ph.D. Student LAMDA Group School of Artificial Intelligence National Key Laboratory for Novel Software Technology Nanjing University email: gaocr at lamda.nju.edu.cn

Currently I am a Ph.D. student of School of Artificial Intelligence in Nanjing University, supervised by Prof. Chao Qian, and a member of LAMDA Group, led by professor Zhi-Hua Zhou.

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Education

Sep 2018 - Jun 2022 : B.Sc. in Artificial Intelligence, School of Computer Science and Engineering, Southeast University;

Sep 2022 - Now : Ph.D. candidate in Computer Science, School of Artificial Intelligence, Nanjing University.

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Research Interest

My research lies at the intersection of Learning to Optimize (L2O) and combinatorial optimization, with a focus on developing deep learning-driven solutions to address computationally hard discrete problems. I currently work on advancing this paradigm across two critical domains: operations research and electronic design automation (EDA), aiming to bridge AI/ML with practical industrial applications.

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Preprints

(* indicates equal contribution)

Yunqi Shi*, Chengrui Gao*, Wanqi Ren, Siyuan Xu, Ke Xue, Mingxuan Yuan, Chao Qian, and Zhi-Hua Zhou. Open3DBench: Open-Source Benchmark for 3D-IC Backend Implementation and PPA Evaluation. In: Arxiv preprint , 2025. [PDF]

Chengrui Gao, Haopu Shang, Yuyang Jiang, Ke Xue, Weiyong Yang, and Chao Qian. Adaptive Constrained Optimization for Neural Vehicle Routing. 2025. [PDF]

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Publications

(* indicates equal contribution)

Chengrui Gao*, Yunqi Shi*, Ke Xue, Ruo-Tong Chen, Siyuan Xu, Mingxuan Yuan, Chao Qian, and Zhi-Hua Zhou. Expertise Can Be Helpful for Reinforcement Learning-based Macro Placement.
In: Proceedings of the 14th International Conference on Learning Representations (ICLR'26), Rio de Janeiro, Brazil, 2026.

Wanqi Ren*, Chengrui Gao*, Yunqi Shi, Mingzhou Fan, Siyuan Xu, Ke Xue, Chenjian Ding, Mingxuan Yuan and Chao Qian. Reinforcement Learning for Hybrid Bonding Terminal Legalization in 3D ICs.
In: Proceedings of the Design, Automation & Test in Europe (DATE'26) [Top EDA Conference], Verona, Italy, 2026.

Ruo-Tong Chen, Chengrui Gao, Siyuan Xu, Ke Xue, Yunqi Shi, Xi Lin, Mingxuan Yuan, Chao Qian and Zhi-Hua Zhou. Timing-driven Detailed Placement via TimingMask-guided Path-level Optimization.
In: Proceedings of the Design, Automation & Test in Europe (DATE'26) [Top EDA Conference], Verona, Italy, 2026.

Chengrui Gao*, Haopu Shang*, Ke Xue, and Chao Qian. Neural Solver Selection for Combinatorial Optimization.
In: Proceedings of the 42nd International Conference on Machine Learning (ICML'25), Vancouver, Canada, 2025. [PDF]

Erlong Liu, Yu-Chang Wu, Xiaobin Huang,Chengrui Gao, Ren-Jian Wang, Ke Xue, and Chao Qian. Pareto Set Learning for Multi-Objective Reinforcement Learning.
In: Proceedings of the 39th AAAI Conference on Artificial Intelligence (AAAI'25) , Philadelphia, PA, 2025. [PDF]

Chengrui Gao, Haopu Shang, Ke Xue, Dong Li, and Chao Qian. Towards Generalizable Neural Solvers for Vehicle Routing Problems via Ensemble with Transferrable Local Policy.
In: Proceedings of the 33rd International Joint Conference on Artificial Intelligence (IJCAI'24), Jeju, South Korea, 2024. [PDF]

Contact:
National Key Laboratory for Novel Software Technology, Nanjing University, Xianlin Campus Mailbox 603, 163 Xianlin Avenue, Qixia District, Nanjing 210023, China
(南京市栖霞区仙林大道163号，南京大学仙林校区603信箱，软件新技术国家重点实验室，210023)

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