| 2026 | EACL | Is This LLM Library Learning? Evaluation Must Account For Compute and Behaviour. | Ian Berlot-Attwell, Tobias Sesterhenn, Frank Rudzicz, Xujie Si |
| 2025 | ACL | APPL: A Prompt Programming Language for Harmonious Integration of Programs and Large Language Model Prompts. | Honghua Dong, Qidong Su, Yubo Gao, Zhaoyu Li, Yangjun Ruan, Gennady Pekhimenko, Chris J. Maddison, Xujie Si |
| 2025 | CAV | PyEuclid: A Versatile Formal Plane Geometry System in Python. | Zhaoyu Li, Hangrui Bi, Jialiang Sun, Zenan Li, Kaiyu Yang, Xujie Si |
| 2025 | CVPR | Decoupling Training-Free Guided Diffusion by ADMM. | Youyuan Zhang, Zehua Liu, Zenan Li, Zhaoyu Li, James J. Clark, Xujie Si |
| 2025 | ICLR | Proving Olympiad Inequalities by Synergizing LLMs and Symbolic Reasoning. | Zenan Li, Zhaoyu Li, Wen Tang, Xian Zhang, Yuan Yao, Xujie Si, Fan Yang, Kaiyu Yang, Xiaoxing Ma |
| 2025 | ICML | TypyBench: Evaluating LLM Type Inference for Untyped Python Repositories. | Honghua Dong, Jiacheng Yang, Xun Deng, Yuhe Jiang, Gennady Pekhimenko, Fan Long, Xujie Si |
| 2024 | ACML | Towards Robust Saliency Maps. | Nham Le, Arie Gurfinkel, Xujie Si, Chuqin Geng |
| 2024 | FMCAD | Modernizing SMT-Based Type Error Localization. | Max Kopinsky, Brigitte Pientka, Xujie Si |
| 2024 | ICML | Autoformalizing Euclidean Geometry. | Logan Murphy, Kaiyu Yang, Jialiang Sun, Zhaoyu Li, Anima Anandkumar, Xujie Si |
| 2023 | APLAS | TorchProbe: Fuzzing Dynamic Deep Learning Compilers. | Qidong Su, Chuqin Geng, Gennady Pekhimenko, Xujie Si |
| 2023 | ICML | Towards Reliable Neural Specifications. | Chuqin Geng, Nham Le, Xiaojie Xu, Zhaoyue Wang, Arie Gurfinkel, Xujie Si |
| 2023 | SIGCSE | Identifying Different Student Clusters in Functional Programming Assignments: From Quick Learners to Struggling Students. | Chuqin Geng, Wenwen Xu, Yingjie Xu, Brigitte Pientka, Xujie Si |
| 2022 | APLAS | Novice Type Error Diagnosis with Natural Language Models. | Chuqin Geng, Haolin Ye, Yixuan Li, Tianyu Han, Brigitte Pientka, Xujie Si |
| 2021 | FMCAD | Data-driven Optimization of Inductive Generalization. | Nham Le, Xujie Si, Arie Gurfinkel |
| 2021 | SIGCSE | Data Collection for the Learn-OCaml Programming Platform: Modelling How Students Develop Typed Functional Programs. | Alana Ceci, Hanneli C. A. Tavante, Brigitte Pientka, Xujie Si |
| 2020 | CAV | Code2Inv: A Deep Learning Framework for Program Verification. | Xujie Si, Aaditya Naik, Hanjun Dai, Mayur Naik, Le Song |
| 2019 | ICLR | Learning a Meta-Solver for Syntax-Guided Program Synthesis. | Xujie Si, Yuan Yang, Hanjun Dai, Mayur Naik, Le Song |
| 2019 | IJCAI | Synthesizing Datalog Programs using Numerical Relaxation. | Xujie Si, Mukund Raghothaman, Kihong Heo, Mayur Naik |
| 2019 | PLDI | Continuously reasoning about programs using differential Bayesian inference. | Kihong Heo, Mukund Raghothaman, Xujie Si, Mayur Naik |
| 2017 | CAV | Maximum Satisfiability in Software Analysis: Applications and Techniques. | Xujie Si, Xin Zhang, Radu Grigore, Mayur Naik |
| 2017 | PLDI | Combining the logical and the probabilistic in program analysis. | Xin Zhang, Xujie Si, Mayur Naik |
| 2016 | CP | On Incremental Core-Guided MaxSAT Solving. | Xujie Si, Xin Zhang, Vasco Manquinho, Mikols Janota, Alexey Ignatiev, Mayur Naik |