About

I am now a full professor with tenure in the School of Computer Science in Peking University. I obtained my Ph.D. from Peking University in 2006. I had been a visiting associate professor at Artificial Intelligence Laboratory of Stanford University in 2013-2014. My current research mainly concerns applications of probabilistic methods for machine learning, including Program Language Processing, Natural Language Processing, and Software Engineering.

New Papers

• [arXiv 2024] Yihong Dong, Xue Jiang, Huanyu Liu, Zhi Jin, Ge Li, Generalization or Memorization: Data Contamination and Trustworthy Evaluation for Large Language Models. arXiv preprint arXiv:2402.15938, 2024.
• [arXiv 2024] Xue Jiang, Yihong Dong, Zhi Jin, Ge Li, SEED: Customize Large Language Models with Sample-Efficient Adaptation for Code Generation, arXiv preprint arXiv:2403.00046, 2024.
• [arXiv 2024] Zhang, Kechi, Jia Li, Ge Li, Xianjie Shi, Zhi Jin, CodeAgent: Enhancing Code Generation with Tool-Integrated Agent Systems for Real-World Repo-level Coding Challenges, arXiv preprint arXiv:2401.07339, 2024.
• [arXiv 2023] Yihong Dong, Jiazheng Ding, Xue Jiang, Ge Li, Zhuo Li, Zhi Jin, Codescore: Evaluating code generation by learning code execution. arXiv preprint arXiv:2301.09043, 2023.
• [arXiv 2023] Jia Li, Ge Li, Chongyang Tao, Jia Li, Huangzhao Zhang, Fang Liu, Zhi Jin, Large Language Model-Aware In-Context Learning for Code Generation, arXiv preprint arXiv:2310.09748, 2023.
• [arXiv 2023] Jia Li, Yongmin Li, Ge Li, Zhi Jin. Structured Chain-of-Thought Prompting for Code Generation, arXiv preprint arXiv:2305.06599.
• [arXiv 2023] Jia Li, Yunfei Zhao, Yongmin Li, Ge Li, Zhi Jin. AceCoder: Utilizing Existing Code to Enhance Code Generation, arXiv preprint arXiv:2303.17780.
• [arXiv 2023] Rongao Li, Jie Fu, Bo-Wen Zhang, Tao Huang, Zhihong Sun, Chen Lyu, Guang Liu, Zhi Jin, Ge Li, TACO: Topics in Algorithmic Code Generation Dataset, arXiv preprint arXiv: 2312.14852, 2023.
• [arXiv 2023] Xue Jiang, Yihong Dong, Lecheng Wang, Zheng Fang, Qiwei Shang, Ge Li, Zhi Jin, Wenpin Jiao, Self-planning code generation with large language model, arXiv preprint arXiv:2303.06689, 2023.
• [arXiv 2023] Zhang, Kechi, Ge Li, Jia Li, Zhuo Li, Zhi Jin, ToolCoder: Teach Code Generation Models to use API search tool, arXiv preprint arXiv:2305.04032, 2023.
• [arXiv 2023] Zejun Wang, Jia Li, Ge Li, Zhi Jin. ChatCoder: Chat-based Refine Requirement Improves LLMs' Code Generation, arXiv preprint arXiv:2311.00272, 2023.
• [arXiv 2022] Zhang, Kechi, Ge Li, Zhi Jin, What does Transformer learn about source code?, arXiv preprint arXiv:2207.08466, 2022.

Selected Publications

• [FSE 2024] Bolun Li, Zhihong Sun, Tao Huang, Hongyu Zhang, Yao Wan, Ge Li, Zhi Jin, Chen Lyu, IRCoCo: Immediate Rewards-Guided Deep Reinforcement Learning for Code Completion, Proceedings of the 2024 ACM International Conference on the Foundations of Software Engineering (FSE), Porto de Galinhas, Brazil, July 15-19, 2024.
• [ICPC 2024] Tao Huang, Zhihong Sun, Zhi Jin, Ge Li, Chen Lyu, Knowledge-Aware Code Generation with Large Language Models, Proceedings of the 32nd ACM/IEEE International Conference on Program Comprehension (ICPC), Lisbon, Portugal, April 15-16, 2024.
• [ICSE 2024] Tao Huang, Zhihong Sun, Zhi Jin, Ge Li, Chen Lyu, KareCoder: A New Knowledge-Enriched Code Generation System, Companion Proceedings of the 46th ACM/IEEE International Conference on Software Engineering (ICSE), Lisbon, Portugal, April 14-20, 2024.
• [JSEP, 2024] Huangzhao Zhang, Shuai Lu, Zhuo Li, Zhi Jin, Lei Ma, Yang Liu, Ge Li. Codebert-Attack: Adversarial Attack against Source Code Deep Learning Models via Pre-trained Model. J. Softw. Evol. Proc. 36(3): e2571 (2024)
• [LREC-COLING 2024] Zhihong Sun, Chen Lyu, Yao Wan, Hongyu Zhang, Ge Li, Zhi Jin, Enhancing Code Generation Performance of Smaller Models by Distilling the Reasoning Ability of LLMs, Proceedings of the 2024 Joint International Conference on Computational Linguistics, Language Resources and Evaluation (LREC-COLING), Torino, Italia, May 20-25, 2024.
• [SCIS, 2024] Huangzhao Zhang, Kechi Zhang, Zhuo Li, Jia Li, Jia Li, Yongmin Li, Yunfei Zhao, Yuqi Zhu, Fang Liu, Ge Li, Zhi Jin. Deep Learning for Code Generation: A Survey. Sci. China Inf. Sci. doi: 10.1007/s11432-023-3956-3
• [TOSEM, 2024] Jia Li, Zhuo Li, Huangzhao Zhang, Ge Li, Zhi Jin, Xing Hu, Xin Xia. Poison Attack and Poison Detection on Deep Source Code Processing Models, ACM Transactions on Software Engineering and Methodology (TOSEM), Vol. 33, No. 62, March 14, 2024, pp 1-31.
• [TSE, 2024] Xin-Cheng Wen, Cuiyun Gao, Feng Luo, Haoyu Wang, Ge Li, and Qing Liao, LIVABLE: Exploring Long-Tailed Classification of Software Vulnerability Types, IEEE Transactions on Software Engineering (TSE), 2024. (Accepted)
• [AAAI 2024] Yuqi Zhu, Jia Allen Li, Ge Li, YunFei Zhao, Jia Li, Zhi Jin, Hong Mei, Hot or Cold? Adaptive Temperature Sampling for Code Generation with Large Language Models, Proceedings of the 38th Annual AAAI Conference on Artificial Intelligence, Vancouver, Canada, Feb 20-27, 2024. (Accepted)
• [ASEJ, 2023] Zejun Wang, Fang Liu, Yiyang Hao, Zhi Jin. AdaComplete: improve DL-based code completion method’s domain adaptability. Automated Software Engineering (ASEJ), Vol. 30, No. 1, Mar 06, 2023, pp 28-39.
• [Internetware 2023] Jia Li, Fang Liu, Jia Allen Li, Yunfei Zhao, Ge Li, and Zhi Jin, Mcodesearcher: Multi-view Contrastive Learning for Code Search, Proceedings of the 14th Asia-Pacific Symposium on Internetware (Internetware), Hangzhou, China, August 4-6 ACM, 2023.
• [JSEP, 2023] Huangzhao Zhang, Zhuo Li, Zhi Jin, Ge Li, WELL: Applying Bug Detectors to Bug Localization via Weakly Supervised Learning. J. Softw. Evol. Proc. doi: 10.1002/smr.2669, 2023.
• [TOSEM, 2023] Jia Allen Li, Zhuo Li, HuangZhao Zhang, Ge Li, Zhi Jin, Xing Hu, Xin Xia, Poison Attack and Poison Detection on Deep Source Code Processing Models, ACM Transactions on Software Engineering and Methodology (TOSEM), 2023. (Accepted)
• [ECAI 2023] Yihong Dong, Ge Li, Xue Jiang, Zhi Jin, Antecedent Predictions Are More Important Than You Think: An Effective Method for Tree-Based Code Generation, Proceedings of the 26th European Conference on Artificial Intelligence (ECAI), Kraków, Poland, Sept. 30 - Oct. 4, 2023.
• [ASE 2023] Jia Li, Chongyang Tao, Zhi Jin, Fang Liu, Jia Allen Li, Ge Li, ZC3 Zero-Shot Cross-Language Code Clone Detection, Proceedings of the 38th IEEE/ACM International Conference on Automated Software Engineering (ASE), Kirchberg, Luxembourg, September 11-15, 2023.
• [ACL 2023] Kechi Zhang, Zhuo Li, Jia Allen Li, Ge Li, Zhi Jin, Fault-Aware Code Editor for Code Generation with Large Language Models, Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics (ACL), Toronto, Canada, July 9-14, 2023.
• [TOSEM, 2023] Jia Allen Li, Ge Li, Zhuo Li, Zhi Jin, Xing Hu, Kechi Zhang, Zhiyi Fu, CodeEditor: Learning to Edit Source Code with Pre-trained Models, ACM Transactions on Software Engineering and Methodology (TOSEM), Vol. 32, No. 6, May 22, 2023, pp 143 - 165.
• [ISSTA 2023] Yihong Dong, Ge Li, Jiazheng Ding, Zhi Jin, CODEP: Grammatical Seq2Seq Model for General-Purpose Code Generation, the ACM Sigsoft International Symposium on Software Testing and Analysis (ISSTA'23), Seattle, Washington, United States, July 17-21, 2023.
• [ICSE 2023] Jia Allen Li, Yongmin Li, Ge Li, Zhi Jin, Xing Hu, SkCoder: A Sketch-based Approach for Automatic Code Generation, Proceedings of the 45th International Conference on Software Engineering (ICSE), Melbourne, Australia, May 14-20, 2023.
• [JSME, 2023] Huangzhao Zhang, Shuai Lu, Zhi Jin, Lei Ma, Zhuo Li, Yang Liu, Ge Li, CodeBERT-Attack: Adversarial Attack against Source Code Deep Learning Models via Pre-Trained Model, Journal of Software: Evolution and Process. May 22, 2023.
• [ICPC 2023] Kechi Zhang, Zhou Li, Zhi Jin, Ge Li, Implant Global and Local Hierarchy Information to Sequence based Code Representation Models, Proceedings of the 31st IEEE/ACM International Conference on Program Comprehension (ICPC), Melbourne Australia, May 15-16, 2023. (ACM SIGSOFT Distinguished Paper Award)
• [SANER 2023] Wenhan Wang, Kechi Zhang, Ge Li, Shangqing Liu, Anran Li, Zhi Jin, Yang Liu, Learning Program Representations with a Tree-Structured Transformer, Proceedings of the 30th IEEE International Conference on Software Analysis, Evolution and Reengineering (SANER), Macao SAR, China, March 21st-24th, 2023.
• [EMNLP 2022] Han Peng, Ge Li, Yunfei Zhao and Zhi Jin, Rethinking Positional Encoding in Tree Transformer for Code Representation, Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing(EMNLP 2022), Abu Dhabi, December 7–11, 2022, pp 3204 - 3214.
• [NeurIPS 2022] Zhang Haojie, Ge Li, Jia Allen Li, Zhongjin Zhang, Yuqi Zhu, Zhi Jin, Fine-Tuning Pre-Trained Language Models Effectively by Optimizing Subnetworks Adaptively, Proceedings of the 36th Conference on Neural Information Processing Systems (NeurIPS), Online, Nov. 29 - Dec.1, 2022.
• [FSE 2022] Sijie Shen, Xiang Zhu, Yihong Dong, Qizhi Guo, Yankun Zhen, Ge Li, Incorporating Domain Knowledge through Task Augmentation for Front-End JavaScript Code Generation, Proceedings of The ACM Joint European Software Engineering Conference and Symposium on the Foundations of Software Engineering (ESEC/FSE), Singapore, 14th - 16th November 2022.
• [FSE 2022] Lin Shi, Fangwen Mu, Xiao Chen, Song Wang, Junjie Wang, Ye Yang, Ge Li, Xin Xia, Qing Wang, We Building on the Rock? On the Importance of Data Preprocessing for Code Summarization, Proceedings of The ACM Joint European Software Engineering Conference and Symposium on the Foundations of Software Engineering (ESEC/FSE), Singapore, 14th - 16th November 2022.
• [CIKM 2022] Jia Li, Yuyuan Zhao, Zhi Jin, Ge Li, Tao Shen, Zhengwei Tao, Chongyang Tao, SK2: Integrating Implicit Sentiment Knowledge and Explicit Syntax Knowledge for Aspect-Based Sentiment Analysis, Proceedings of 31st ACM International Conference on Information and Knowledge Management, Atlanta, Georgia, USA, Oct. 17-21, 2022.
• [TOSEM, 2022] Hao Yu, Xing Hu, Ge Li, Ying Li, Qianxiang Wang, Tao Xie, Assessing and Improving an Evaluation Dataset for Detecting Semantic Code Clones via Deep Learning, ACM Transactions on Software Engineering and Methodology (TOSEM), Vol. 31, No. 4, Article 62, July, 2022, pp 1–25.
• [ICSE 2022] Fang Liu, Ge Li, Zhiyi Fu, Shuai Lu, Yiyang Hao, Zhi Jin, Learning to Recommend Method Names with Global Context, Proceedings of the 44th International Conference on Software Engineering (ICSE 2022), Pittsburgh, PA, USA, May 21-29, 2022.
• [ICSE 2022] Hao Yu, Yiling Lou, Ke Sun, Dezhi Ran, Tao Xie, Dan Hao, Ying Li, Ge Li, Qianxiang Wang, Automated Assertion Generation via Information Retrieval and Its Integration with Deep Learning, Proceedings of the 44th International Conference on Software Engineering (ICSE 2022), Pittsburgh, PA, USA, May 21-29, 2022.
• [ICPC 2022] Kechi Zhang, Wenhan Wang, Huangzhao Zhang, Ge Li, Zhi Jin, Learning to Represent Programs with Heterogeneous Graphs, Proceedings of the 30th ACM/IEEE International Conference on Program Comprehension (ICPC), Pittsburgh, PA, USA, May 16-17, 2022.
• [ESE, 2022] Fang Liu, Ge Li, Bolin Wei, Xin Xia, Zhiyi Fu, Zhi Jin, A Unified Multi-task Learning Model for AST-level and Token-level Code Completion, Empirical Software Engineering, Vol. 27, Iss. 4, Apr. 18, 2022, pp. 1-38.
• [TOSEM, 2022] Huangzhao Zhang, Zhiyi Fu, Ge Li, Lei Ma, Zhehao Zhao, Hua’an Yang, Yizhe Sun, Yang Liu, Zhi Jin, Towards Robustness of Deep Program Processing Models—Detection, Estimation, and Enhancement, ACM Transactions on Software Engineering and Methodology (TOSEM), Vol. 31, Iss. 3, Apr. 9, 2022, pp. 1-40.
• [TSE, 2022] Hui Liu, Mingzhu Shen, Jiaqi Zhu, Nan Niu , Ge Li, and Lu Zhang, Deep Learning Based Program Generation From Requirements Text: Are We There Yet? IEEE Transactions on Software Engineering (TSE), Vol. 48, Iss. 4, Apr. 1, 2022.
• [JSS, 2021] Zhehao Zhao, Bo Yang, Ge Li, Huai Liu, Zhi Jin, Precise Learning of Source Code Contextual Semantics via Hierarchical Dependence Structure and Graph Attention Networks, Journal of Systems and Software, Volume 184, February 2022.
• [NeurIPS 2021] Shuai Lu, Daya Guo, Shuo Ren, Junjie Huang, Alexey Svyatkovskiy, Ambrosio Blanco, Colin Clement, Dawn Drain, Daxin Jiang, Duyu Tang, Ge Li, Lidong Zhou, Linjun Shou, Long Zhou, Michele Tufano, Ming Gong, Ming Zhou, Nan Duan, Neel Sundaresan, Shao Kun Deng, Shengyu Fu, Shujie Liu, CodeXGLUE: A Machine Learning Benchmark Dataset for Code Understanding and Generation, Proceedings of the 35th Conference on Neural Information Processing Systems (NeurIPS), Online, December 6-14, 2021.
• [NeurIPS 2021] Han Peng, Ge Li, Wenhan Wang, Yunfei Zhao, Zhi Jin, Integrating Tree Path in Transformer for Code Representation, Proceedings of the 35th Conference on Neural Information Processing Systems (NeurIPS), Online, December 6-14, 2021.
• [ASE 2021] Jia Allen Li, Yongmin Li, Ge Li, Xing Hu, Xin Xia, Zhi Jin, EDITSUM: A Retrieve-and-Edit Framework for Source Code Summarization, Proceedings of the 36th IEEE/ACM International Conference on Automated Software Engineering (ASE), Melbourne, Australia, Sun 14 - Sat 20 November, 2021.
• [IJCAI 2020] Wenjie Zhang, Zeyu Sun, Qihao Zhu, Ge Li, Shaowei Cai, Yingfei Xiong, Lu Zhang, NLocalSAT: Boosting Local Search with Solution Prediction, Proceedings of the 29th International Joint Conference on Artificial Intelligence (IJCAI), Yokohama, Japan, January 7-15, 2021, pp. 1177-1183.
• [ASE 2020] Fang Liu, Ge Li, Yunfei Zhao, Zhi Jin, Multi-task Learning based Pre-trained Language Model for Code Completion, Proceedings of the 35th IEEE/ACM International Conference on Automated Software Engineering (ASE), Melbourne, Australia, Sep. 21-25, 2020.
• [ASE 2020] Bolin Wei, Yongmin Li, Ge Li, Xin Xia, Zhi Jin, Retrieve and Refine: Exemplar-based Neural Comment Generation, Proceedings of the 35th IEEE/ACM International Conference on Automated Software Engineering (ASE), Melbourne, Australia, Sep. 21-25, 2020.
• [TOSEM, 2020] Wenhan Wang, Ge Li, Sijie Shen, Xin Xia, Zhi Jin, Modular Tree Network for Source Code Representation Learning, ACM Transactions on Software Engineering and Methodology (TOSEM), Vol. 29, No. 4, Article 31, September 2020.
• [ICPC 2020] Fang Liu, Ge Li, Xin Xia, Bolin Wei, Zhi Jin, A Self-Attentional Neural Architecture for Code Completion with Multi-Task Learning, Proceedings of the 28th IEEE/ACM International Conference on Program Comprehension (ICPC), Seoul, South Korea, May 23-24, 2020, Pages 37–47. (ACM SIGSOFT Distinguished Paper Award)
• [SANER 2020] Wenhan Wang, Ge Li, Bo Ma, Xin Xia, Zhi Jin, Detecting Code Clones with Graph Neural Network and Flow-Augmented Abstract Syntax Tree, Proceedings of the 27th IEEE International Conference on Software Analysis (SANER), Evolution and Reengineering London, Ontario, Canada, February 18-21, 2020.
• [AAAI 2020] Huangzhao Zhang, Zhuo Li, Ge Li, Lei Ma, Yang Liu, Zhi Jin, Generating Adversarial Examples for Holding Robustness of Source Code Processing Models, Proceedings of the 34th AAAI Conference on Artificial Intelligence (AAAI), New York, USA, Feb 7-12, 2020.
• [NeurIPS 2019] Bolin Wei, Ge Li, Xin Xia, Zhiyi Fu, Zhi Jin, Code Generation as a Dual Task of Code Summarization, Proceedings of the 33rd Conference on Neural Information Processing Systems (NeurIPS), Vancouver, Canada, Dec 8-14, 2019, pp.6563-6573.
• [ICASSP 2019] Bolin Wei, Shuai Lu, Lili Mou, Hao Zhou, Pascal Poupart, Ge Li, Zhi Jin, Why Do Neural Dialog Systems Generate Short and Meaningless Replies? a Comparison between Dialog and Translation, Proceedings of 2019 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), Brighton, UK, May 12-17, 2019, pp.7290-7294.
• [COMPSAC 2019] Xing Hu, Rui Men, Ge Li, Zhi Jin, Deep-AutoCoder: Learning to Complete Code Precisely with Induced Code Tokens, Proceedings of 2019 IEEE 43rd Annual Computer Software and Applications Conference (COMPSAC), Milwaukee, Wisconsin, USA, Jul. 15-19, 2019.
• [EMSE, 2019] Xing Hu, Ge Li, Xin Xia, David Lo, Zhi Jin, Deep Code Comment Generation with Hybrid Lexical and Syntactical Information, Empirical Software Engineering (EMSE), 18, June, 2019.
• [ICPC 2019] Hao Yu, Wing Lam, Long Chen, Ge Li, Tao Xie, Qianxiang Wang, Neural Detection of Semantic Code Clones via Tree-based Convolution, Proceedings of the 27th International Conference on Program Comprehension (ICPC), Montreal, QC, Canada, May 25-31, 2019, pp. 70-80.
• [AAAI 2019] Zeyu Sun, Qihao Zhu, Lili Mou, Yingfei Xiong, Ge Li, Lu Zhang, A Grammar-Based Structural CNN Decoder for Code Generation, Proceedings of the 33rd AAAI Conference on Artificial Intelligence (AAAI), Honolulu, Hawaii, USA, Jan. 27 – Feb. 1, 2019.
• [ICPC 2019] Xiaochen Li, He Jiang, Dong Liu, Zhilei Ren, Ge Li, Unsupervised deep bug report summarization, Proceedings of the 26th Conference on Program Comprehension (ICPC), 2018. pp. 144-155.
• [IJCAI 2018] Xing Hu, Ge Li, Xin Xia, David Lo, Shuai Lu, Zhi Jin, Summarizing Source Code with Transferred API Knowledge, Proceedings of the 27th International Joint Conference on Artificial Intelligence (IJCAI), July 13-19, 2018, Stockholm, Sweden. pp. 2269-2275.
• [ICPC 2018] Xing Hu, Ge Li, Xia Xin, David Lo, Zhi Jin, Deep Code Comment Generation, 2018 IEEE/ACM 26th International Conference on Program Comprehension (ICPC), 27-28 May 2018, Gothenburg, Sweden. pp.200-210. (ACM SIGSOFT Distinguished Paper Award)
• [KSEM 2017] Yunchuan Chen, Ge Li and Zhi Jin, Learning Sparse Overcomplete Word Vectors without Intermediate Dense Representations, The 10th International Conference on Knowledge Science, Engineering and Management (KSEM), Melbourne, Australia, August,19-20, 2017.
• [KSEM 2017] Yangyang Lu, Ge Li, Zelong Zhao, Lingfeng Wen and Zhi Jin, Learning To Infer API Mappings From API Documents, The 10th International Conference on Knowledge Science, Engineering and Management (KSEM), Melbourne, Australia, August,19-20, 2017.
• [KSEM 2017] Wenhao Huang, Ge Li and Zhi Jin, Improved Knowledge Base Completion by the Path-Augmented TransR Model, The 10th International Conference on Knowledge Science, Engineering and Management (KSEM), Melbourne, Australia, August,19-20, 2017.
• [COLING 2016] Lili Mou, Yiping Song, Rui Yan, Ge Li, Lu Zhang, Zhi Jin, Sequence to Backward and Forward Sequences: A Content-Introducing Approach to Generative Short-Text Conversation, Proceedings of the 26th International Conference on Computational Linguistics (COLING), Osaka, Japan, December 11-17, 2016, pp. 3349–3358.
• [COLING 2016] Yan Xu, Ran Jia, Lili Mou, Ge Li, Yunchuan Chen, Yangyang Lu and Zhi Jin, Improved Relation Classification by Deep Recurrent Neural Networks with Data Augmentation, Proceedings of the 26th International Conference on Computational Linguistics (COLING), Osaka, Japan, December 11-17, 2016, pp. 1461–1470.
• [EMNLP 2016] Lili Mou, Zhao Meng, Rui Yan, Ge Li, Yan Xu, Lu Zhang, Zhi Jin, How Transferable are Neural Networks in NLP Applications? Proceedings of the 2016 Conference on Empirical Methods in Natural Language Processing (EMNLP), Austin, Texas, November 1-5, 2016, pp. 479–489.
• [ACL 2016] Yunchuan Chen, Lili Mou, Yan Xu, Ge Li, Zhi Jin, Compressing Neural Language Models by Sparse Word Representations, Proceedings of the 54th Annual Meeting of the Association for Computational Linguistics (ACL), Berlin, Germany, August 7-12, 2016, pp. 226–235.
• [ACL 2016] Lili Mou, Rui Men, Ge Li, Yan Xu, Lu Zhang, Rui Yan, Zhi Jin, Natural Language Inference by Tree-Based Convolution and Heuristic Matching, Proceedings of the 54th Annual Meeting of the Association for Computational Linguistics (ACL), Berlin, Germany, August 7-12, 2016, pp. 130–136.
• [AAAI 2016] Lili Mou, Ge Li, Lu Zhang, Tao Wang, Zhi Jin, "Convolutional Neural Networks over Tree Structures for Programming Language Processing." Proceedings of 2016 AAAI Conference on Artificial Intelligence, pages 1287-1293, Phoenix, USA, January 12-18, 2016.
• [CIKM 2016] Lili Mou, Ran Jia, Yan Xu, Ge Li, Lu Zhang, Zhi Jin, Distilling Word Embeddings: An Encoding Approach, Proceedings of the 25th ACM International Conference on Information and Knowledge Management, Indianapolis, USA, October 24-28, 2016.
• [KSEM 2016] Zhao Meng, Lili Mou, Ge Li and Zhi Jin, Context-Aware Tree-Based Convolutional Neural Networks for Natural Language Inference, Proceedings of 9th International Conference on Knowledge Science, Engineering and Management, Passau, Germany, October 4-8, 2016, LNAI 9983, pp. 515–526.
• [KSEM 2016] Yangyang Lu, Ge Li, Rui Miao and Zhi Jin, Learning Embeddings Of API Tokens To Facilitate Deep Learning Based Program Processing, Proceedings of 9th International Conference on Knowledge Science, Engineering and Management, Passau, Germany, October 4-8, 2016, LNAI 9983, pp. 527–539.
• [EMNLP 2015] Hao Peng, Lili Mou, Ge Li, Yan Xu, Lu Zhang, Zhi Jin, "A Comparative Study on Regularization Strategies for Embedding-based Neural Networks." The 2015 Conference on Empirical Methods in Natural Language Processing, Lisboa, Portugal, September 17–21, 2015.
• [KSEM 2015] Hao Peng, Lili Mou, Ge Li, Yuxuan Liu, Lu Zhang and Zhi Jin, "Building Program Vector Representations for Deep Learning." arXiv:1409.3358,The 8th International Conference on Knowledge Science, Engineering and Management, Chongqing, China October 28-30, 2015. pp. 547-553.
• [EMNLP 2015] Lili Mou, Hao Peng, Ge Li, Yan Xu, Lu Zhang, Zhi Jin, Discriminative Neural Sentence Modeling by Tree-Based Convolution. Proceedings of the 2015 Conference on Empirical Methods in Natural Language Processing, Lisbon, Portugal, 17-21 September, 2015. pp. 2315–2325.
• [EMNLP 2015] Yan Xu, Lili Mou, Ge Li, Lu Zhang, Zhi Jin, "Classifying Relations via Long Short Term Memory Networks along Shortest Dependency Paths." Proceedings of the 2015 Conference on Empirical Methods in Natural Language Processing, Lisboa, Portugal, September 17–21, 2015.
• [arXiv 2015] Lili Mou, Rui Men, Ge Li, Lu Zhang, Zhi Jin, On End-to-End Program Generation from User Intention by Deep Neural Networks, arXiv preprint arXiv:1510.07211, 2015.
• [KSEM 2014] Lili Mou, Ge Li, Zhi Jin and Lu Zhang, "Verification based on Hyponymy Hierarchical Characteristics for Web-based Hyponymy Discovery." International Conference on Knowledge Science, Engineering and Management 2014, Lecture Notes in Computer Science Volume 8793, 2014, pp 81-92.
• [IJSEKE, 2014] Yan Xu, Ge Li, Lili Mou, Yangyang Lu, "Learning Non-taxonomy Relations on Demand for Ontology Extension." International Journal of Software Engineering and Knowledge Engineering, October 2014, Vol.24, No.08, pp.1159-1175.
• [arXiv 2014]Lili Mou, Ge Li, Zhi Jin, Lu Zhang, Tao Wang, TBCNN: A Tree-Based Convolutional Neural Network for Programming Language Processing, arXiv preprint arXiv:1409.5718, 2014.
• [arXiv 2014] Lili Mou, Ge Li, Yuxuan Liu, Hao Peng, Zhi Jin, Yan Xu, and Lu Zhang, Building program vector representations for deep learning, arXiv preprint arXiv:1409.3358, 2014.

Researching Awards

• CVIC SE Software Talent Award
• First Prize of Science and Technology Progress Award of the Ministry of Education of China
• First Prize of Science and Technological Invention Award of China Computer Federation
• Second Prize of Science and Technology Invention Award of Beijing City
• Second Prize of Science and Technology Progress Award of Beijing City
• Winner of Beijing Youth Talent Program
• Winner of Jinan Quancheng Scholars Program
• 23' ACM SIGSOFT Distinguished Paper Award
• 20' ACM SIGSOFT Distinguished Paper Award
• 18' ACM SIGSOFT Distinguished Paper Award
• First Prize of the 3rd China Software Valley Innovation and Entrepreneurship Competition (aiXcoder)
• Second Prize of the 6th IChuang Cup Internet Innovation and Entrepreneurship Competition (aiXcoder)

Teaching

• Undergraduate Courses
• Offline Courses
• Introduction to Computing (ID: 04830041)
[Fall 2021], [Fall 2020], [Fall 2019], [Fall 2018], [Fall 2017], [Fall 2016], [Fall 2015], [Fall 2014], [Fall 2012], [Fall 2011], [Fall 2010], [Fall 2009], [Fall 2008], [Fall 2007]
• MOOC Courses
• Coursera.com
• PartA: https://www.coursera.org/learn/jisuanji-biancheng
• PartA: https://www.coursera.org/learn/c-chengxu-sheji
• ChineseMooc.org
• PartA: http://www.chinesemooc.org/mooc/4399
• PartB: http://www.chinesemooc.org/mooc/4745
• ICourse163.org
• https://www.icourse163.org/course/PKU-1002529002

• Postgraduate courses
• Deep Learning Technique and Appplication
[Spring 2021], [Spring 2020], [Spring 2019], [Spring 2018], [Spring 2017], [Spring 2016], [Spring 2015]

Teaching Awards

• National First-Class Undergraduate Offline Course (Leader of the Teaching Team)
• National First-Class Undergraduate Online Course (Leader of the Teaching Team)
• Excellent Teaching Team Award of Peking University (Leader of the Teaching Team)
• National Excellent Online Open Course (Leader of the Teaching Team)
• Advanced Teacher of the Department of Computer Science and Technology of Peking University
• Teaching Achievement Award of Peking University
• Teaching Excellence Award of Peking University
• Exemplary Teacher on Innovation of Education in Beijing City
• Teaching Expert in Beijing City
• Grand Prize in Teaching Exchange Activities of Association of Fundamental Computing Education in Chinese Universities
• First Prize of the Teaching Skills Competition for Young Teachers in Universities in Beijing
• First Prize of the Teaching Skills Competition for Young Teachers in Peking University

Services

• Secretary General of Software Engineering Committee of China Computer Federation
• Co-chair of the Program Committee：
  • Asia-Pacific Symposium on Internetware (Internetware)
  • CCF National Conference on Software (ChinaSoft)
• Co-Guest-Editor:
  • Journal of System and Software (JSS), Special Issue Intelligent Software Engineering.
  • Information and Software Technology (IST), Special Section on Intelligent Software Engineering.
  • Journal of Computer Science and Technology (JCST), Special Section on Software Systems.
  • Journal of Software, Special Issue on New Technology of Intelligentized Software.
• PC Member:
  • The AAAI Conference on Artificial Intelligence (AAAI)
  • The IEEE/ACM International Conference on Automated Software Engineering (ASE)
  • The IEEE International Conference on Software Analysis, Evolution and Reengineering (SANER)
  • The International Conference on Knowledge Science, Engineering and Management (KSEM)
  • The International Conference on Deep Learning Theory and Applications (DeLTA)
  • 26th European Conference on Artificial Intelligence (ECAI 2023)
  • NeurIPS 2023 Datasets and Benchmarks