Yangruibo (Robin) Ding's Personal Website

Yangruibo (Robin) Ding

Email / Google Scholar / LinkedIn / Twitter

Bio: I am a final-year Ph.D. student in Computer Science at Columbia University advised by Prof. Baishakhi Ray and Prof. Gail Kaiser. I have also been a student researcher at Google DeepMind since May 2024.

Research: My research is at the intersection of Software Engineering and Machine Learning, focusing on developing large language models (LLMs) for code. I train LLMs to generate, analyze, and refine software programs. I also construct benchmarks to systematically evaluate LLMs' capabilities in solving software engineering tasks. Most recently, I am interested in improving LLMs' reasoning capability to tackle complex programming tasks, such as debugging and patching.

News

⭐ Sep. 2024: "SemCoder: Training Code Language Models with Comprehensive Semantics Reasoning" got accepted by NeurIPS 2024.

🎉July 2024: "Vulnerability Detection with Code Language Models: How Far Are We?" got accepted by ICSE 2025.

May. 2024: I joined Google DeepMind as a Student Researcher, working on Code LLMs.

Jan. 2024: "CYCLE: Learning to Self-Refine Code Generation" got accepted by OOPSLA 2024.

Jan. 2024: "Beyond Accuracy: Evaluating Self-Consistency of Code Large Language Models with IdentityChain" got accepted by ICLR 2024.

Dec. 2023: "Deep Learning Based Vulnerability Detection: Are We There Yet" got 🏆 IEEE TSE Best Paper Award Runner-up.

Nov. 2023: I will serve as a Program Committee Member and Session Chair of ASE 2024.

Sep. 2023: Our Datasets and Benchmarks paper, CrossCodeEval, got accepted by NeurIPS 2023.

July 2023: CONCORD got 🏆 ACM SIGSOFT Distinguished Paper Award.

Honors and Awards

IBM Ph.D. Fellowship Award. 2022-2024
ACM SIGSOFT Distinguished Paper Award. 2023
IEEE TSE Best Paper Award Runner-up. 2022
NSF Student Travel Award for ESEC/FSE'23. 2023
ACM SIGSOFT CAPS Travel Grant. 2023
NSF Travel Award for ICSE'22. 2022

Work Experiences

May 2024 - Present: Student Researcher @ Google DeepMind - Learning4Code Team, working with Pengcheng Yin, Miltos Allamanis, Disha Shrivastava, Kensen Shi, Hanjun Dai, and Charles Sutton.
May 2022 - Sep 2022: Applied Scientist Intern @ AWS AI Labs - Code Whisperer Team, working with Zijian Wang and Wasi Ahmad.
May 2021 - Sep 2021: Research Intern @ IBM Research - AI for Code Team, working with Saurabh Pujar and Luca Buratti.
June 2020 - Aug 2020: Research Intern @ IBM Research - AI for Code Team, working with Sahil Suneja and Alessandro Morari.

Publications

	SemCoder: Training Code Language Models with Comprehensive Semantics Reasoning Yangruibo Ding, Jinjun Peng, Marcus J. Min, Gail Kaiser, Junfeng Yang, Baishakhi Ray NeurIPS 2024
	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
	CYCLE: Learning to Self-Refine Code Generation Yangruibo Ding, Marcus J. Min, Gail Kaiser, Baishakhi Ray OOPSLA 2024
	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
	TRACED: Execution-aware Pre-training for Source Code Yangruibo Ding, Ben Steenhoek, Kexin Pei, Gail Kaiser, Wei Le, Baishakhi Ray ICSE 2024
	Automated Code Editing with Search-Generate-Modify Changshu Liu, Pelin Cetin, Yogesh Patodia, Baishakhi Ray, Saikat Chakraborty, Yangruibo Ding IEEE Transactions on Software Engineering (TSE)
	CoCoMIC: Code Completion By Jointly Modeling In-file and Cross-file Context Yangruibo Ding, Zijian Wang, Wasi Uddin Ahmad, Murali Krishna Ramanathan, Ramesh Nallapati, Parminder Bhatia, Dan Roth, Bing Xiang ( equal contribution) LREC-COLING 2024
	CrossCodeEval: A Diverse and Multilingual Benchmark for Cross-File Code Completion Yangruibo Ding, Zijian Wang, Wasi Uddin Ahmad, Hantian Ding, Ming Tan, Nihal Jain, Murali Krishna Ramanathan, Ramesh Nallapati, Parminder Bhatia, Dan Roth, Bing Xiang ( equal contribution) NeurIPS 2023 (Datasets & Benchmarks)
	CONCORD: Clone-aware Contrastive Learning for Source Code Yangruibo Ding, Saikat Chakraborty, Luca Buratti, Saurabh Pujar, Alessandro Morari, Gail Kaiser, Baishakhi Ray 🏆 ACM SIGSOFT Distinguished Paper Award ISSTA 2023
	NatGen: Generative pre-training by "Naturalizing" source code Saikat Chakraborty, Toufique Ahmed, Yangruibo Ding, Premkumar Devanbu, Baishakhi Ray ESEC/FSE 2022
	Towards Learning (Dis)-Similarity of Source Code from Program Contrasts Yangruibo Ding, Luca Buratti, Saurabh Pujar, Alessandro Morari, Baishakhi Ray, Saikat Chakraborty ACL 2022
	Deep learning based vulnerability detection: Are we there yet Saikat Chakraborty, Rahul Krishna, Yangruibo Ding, Baishakhi Ray 🏆 IEEE TSE Best Paper Award Runner-up ICSE 2022 (Journal-First), IEEE Transactions on Software Engineering (TSE)
	VELVET: a noVel Ensemble Learning approach to automatically locate VulnErable sTatements Yangruibo Ding, Sahil Suneja, Yunhui Zheng, Jim Laredo, Alessandro Morari, Gail Kaiser, Baishakhi Ray SANER 2022
	CODIT: Code Editing With Tree-Based Neural Models Saikat Chakraborty Yangruibo Ding, Miltiadis Allamanis, Baishakhi Ray ICSE 2021 (Journal-First), IEEE Transactions on Software Engineering (TSE)
	Patching as Translation: the Data and the Metaphor Yangruibo Ding, Baishakhi Ray, Premkumar Devanbu, Vincent J Hellendoorn ASE 2020