My research focuses on developing large language models (LLMs) and agentic systems for software engineering (SE). Most recently, I am interested in training LLMs with advanced symbolic reasoning capabilities (e.g., debugging, testing, program analysis, verification) and building efficient, collaborative agentic systems for complex software development and maintenance tasks.

• NeurIPS’24] Ding, Y., Peng, J., Min, M.J., Kaiser, G., Yang J., Ray B., 2024. SemCoder: Training Code Language Models with Comprehensive Semantics. Accepted to Thirty-Eighth Annual Conference on Neural Information Processing Systems (NeurIPS).
• [ICSE’25] Ding, Y., Fu, Y., Ibrahim, O., Sitawarin, C., Chen, X., Alomair, B.,Wagner, D., Ray, B., Chen, Y., 2024. Vulnerability Detection with Code Language Models: How Far Are We? Accepted to The 47th IEEE/ACM International Conference on Software Engineering (ICSE).
• [OOPSLA’24] Ding, Y., Min, M.J., Kaiser, G., Ray B., 2024. CYCLE: Learning to Self-Refine Code Generation. In Proceedings of the ACM on Programming Languages, Volume 8, Issue OOPSLA1 (OOPSLA). 12. [ICLR’24] Min, M.J., Ding, Y., Buratti, L., Pujar, S., Kaiser, G., Jana S., Ray B., 2024 Beyond Accuracy: Evaluating Self-Consistency of Code LLMs. Twelfth International Conference on Learning Representations (ICLR).
• [ICSE’24] Ding, Y., Steenhoek, B., Pei, K., Kaiser, G.E., Le, W., Ray, B. 2023. TRACED: Execution-aware Pre-training for Source Code. In Proceedings of the 46th IEEE/ACM International Conference on Software Engineering (ICSE).
• [TSE’24] Liu, C., Cetin, P., Patodia, Y., Ray, B., Chakraborty, S., and Ding, Y., 2024. Automated Code Editing With Search-Generate-Modify. IEEE Transactions on Software Engineering.
• [ISSTA’23] Ding, Y., Chakraborty, S., Buratti, L., Pujar, S., Morari, A., Kaiser, G.E., and Ray, B. (2023). CONCORD: Clone-Aware Contrastive Learning for Source Code. In Proceedings of the 32nd ACM SIGSOFT International Symposium on Software Testing and Analysis (ISSTA). (ACM SIGSOFT Distinguished Paper Award)
• [TSE’21] Chakraborty, S., Krishna, R., Ding, Y., Ray, B., 2021. Deep learning based vulnerability detection: Are we there yet. IEEE Transactions on Software Engineering. (IEEE TSE Best Paper Award Runner-up)

PhD in Computer Science, Columbia University, 2025

• IBM Ph.D. Fellowship Award, 2022-2024
• ACM SIGSOFT Distinguished Paper Award, 2023
• IEEE TSE Best Paper Award Runner-up, 2022
• Ph.D. Service Excellence Award, Columbia CS, 2025