Bio: I am currently a Postdoctoral Scientist at AWS Agentic AI and an incoming Assistant Professor in the Department of Computer Science at University of California, Los Angeles (UCLA).
I completed my Ph.D. in Computer Science at Columbia University, advised by Prof. Baishakhi Ray and Prof. Gail Kaiser. Previoulsy, I have also had wonderful experiences at Google DeepMind, Amazon AWS AI Labs, and IBM Research.
Research: My research focuses on training large language models (LLMs) and developing agentic systems for software engineering (SE). Most recently, I am interested in teaching LLMs to perform advanced symbolic reasoning (e.g., debugging, testing, program analysis, verification) to building efficient, collaborative agentic systems for complex software development and maintenance tasks.
For an overview of the research that I have done, this video might help.
🎯 I am actively looking for students to join my research group @ UCLA CS. Solid coding skills and experiences in large language models (pre-/post-training or agentic systems), program analysis and verification, or security are strongly preferred.
🤝 If you are interested in working with me, drop me an email with (1) your CV and (2) a brief introduction of your research interests and background.
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DevOps-Gym: Benchmarking AI Agents in Software DevOps Cycle
Yuheng Tang, Kaijie Zhu, Bonan Ruan, Chuqi Zhang, Michael Yang, Hongwei Li, Suyue Guo, Tianneng Shi, Zekun Li, Christopher Kruegel, Giovanni Vigna, Dawn Song, William Yang Wang, Lun Wang, Yangruibo Ding, Zhenkai Liang, Wenbo Guo
ICLR 2026
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Co-PatcheR: Collaborative Software Patching with Component(s)-specific Small Reasoning Models
Yuheng Tang, Hongwei Li, Kaijie Zhu, Michael Yang, Yangruibo Ding, Wenbo Guo
NeurIPS 2025
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SemCoder: Training Code Language Models with Comprehensive Semantics Reasoning
Yangruibo Ding,
Jinjun Peng, Marcus J. Min, Gail Kaiser, Junfeng Yang, Baishakhi Ray
NeurIPS 2024
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Vulnerability Detection with Code Language Models: How Far Are We?
Yangruibo Ding, Yanjun Fu, Omniyyah Ibrahim, Chawin Sitawarin, Xinyun Chen, Basel Alomair,
David Wagner, Baishakhi Ray, Yizheng Chen
ICSE 2025
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CYCLE: Learning to Self-Refine Code Generation
Yangruibo Ding,
Marcus J. Min, Gail Kaiser, Baishakhi Ray
OOPSLA 2024
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Beyond Accuracy: Evaluating Self-Consistency of Code Large Language Models with IdentityChain
Marcus J. Min, Yangruibo Ding, Luca Buratti, Saurabh Pujar, Gail Kaiser, Suman Jana, Baishakhi Ray
ICLR 2024
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TRACED: Execution-aware Pre-training for Source Code
Yangruibo Ding,
Ben Steenhoek, Kexin Pei, Gail Kaiser, Wei Le, Baishakhi Ray
ICSE 2024
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Honors and Awards
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IBM Ph.D. Fellowship Award. 2022-2024
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ACM SIGSOFT Distinguished Paper Award. 2023
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IEEE TSE Best Paper Award Runner-up. 2022
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Ph.D. Service Award, Columbia CS. 2025
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NSF Travel Award. 2022, 2023
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ACM SIGSOFT CAPS Travel Grant. 2023
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Recent Talks
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Feb. - Apr. 2025: "From Code Generation Towards Software Engineering: Advancing Code Intelligence w/ Language Models" @ UW, UMD, CMU, UCLA, UTD, JHU, Georgia Tech, Stony Brook, Dartmouth, NUS.
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Oct. 2024: "Training Code Language Models with Comprehensive Semantics Reasoning" @ UIUC.
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Oct. 2024: "Semantic-aware Source Code Modeling" @ UMD, NCSU, ASE'24.
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Aug. 2024: "Training Code Language Models with Comprehensive Semantics Reasoning" @ Google DeepMind.
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Apr. 2024: "Vulnerability Detection with Code Language Models: How Far Are We?" @ Columbia SSS Seminar.
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Services
Program Committee
Conference Reviewer
Journal Reviewer
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Last Updated: Jan 2026.
Photo by Lingyi. Website Template by Jon Barron
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Yangruibo (Robin) Ding