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PhD in Machine Learning at Carnegie Mellon University
Research on efficient, reliable, and aligned foundation models.
I am a first-year PhD in Machine Learning at Carnegie Mellon University. I also completed my MS in Machine Learning at CMU. Before CMU, I completed my B.S. in Computer Science at the University of Rochester, where I was advised by Prof. Jiebo Luo.
My research focuses on LLMs post-training. I am currently a Student Researcher at Google Research advised by Dr. Si Si, and I also collaborate with Prof. Jiaxin Huang and Prof. Haohan Wang.
Updates from papers, collaborations, and current roles.
🥳🥳 GroupDPO accepted by EMNLP (Findings).
🎓🎓 I will start my PhD in CMU MLD in Fall 2026.
New preprints on GroupDPO efficiency.
🥳🥳 Self-Calibration accepted by ICLR 2026.
🥳🥳 Survey paper on Reliable and Responsible Foundation Models accepted by TMLR.
🥳🥳 CrossWordBench accepted by COLM 2025.
New preprints on semi-structured LLM auditing and speculative decoding.
The 2026 Conference on Empirical Methods in Natural Language Processing (EMNLP)
International Conference on Learning Representations (ICLR)
Preprint
Second Conference on Language Modeling (COLM)
International Conference on Learning Representations (ICLR)
Conference on Neural Information Processing Systems (NeurIPS)
Pittsburgh · Rochester · St. Louis · Mountain View
The fastest way to reach me is by email. You can also find publication updates on Google Scholar, code on GitHub, and professional details on LinkedIn or DBLP.
