I am currently a first-year MSML student at Carnegie Mellon University. Previously, I completed my undergraduate studies in Computer Science at University of Rochester, where I had the privilege of being advised by Prof. Jiebo Luo. I have also collaborated with Prof. Haohan Wang at UIUC DREAM Lab. In the summer of 2024, I was a visiting researcher at Washington University in St. Louis with Prof. Jiaxin Huang.
My research interests lie in improving training and inference efficiency as well as model alignment of both LLMs and VLMs.
Feel free to email me if you are interested in collaborating or discussing research ideas.
🔥 News
- 2025.04: 🎉🎉 New preprint “CrossWordBench: Evaluating the Reasoning Capabilities of LLMs and LVLMs with Controllable Puzzle Generation” is out on arxiv. Dataset and Code are also released.
- 2025.02: 🎉🎉 One paper “Taming Overconfidence in LLMs: Reward Calibration in RLHF” is accepted by ICLR 2025 (poster).
- 2024.10: 🎉🎉 New preprint: “Taming Overconfidence in LLMs: Reward Calibration in RLHF” is out on arxiv. Code is also released.
- 2024.09: 🎉🎉 One paper “S2FT: Efficient, Scalable and Generalizable LLM Fine-tuning by Structured Sparsity” is accepted by NIPS 2024 (poster).
- 2024.08: 📚📚 I will start my Master in Machine Learning at Carnegie Mellon University.
- 2024.05: 🎉🎉 “Development of UroSAM: A Machine Learning Model to Automatically Identify Kidney Stone Composition from Endoscopic Video” is accepted for publication at Journal of Endourology.
📖 Educations
- 2024.08 - Present, M.S. in Machine Learning, Carnegie Mellon University, USA
- 2020.08 - 2024.05, B.S. in Computer Science, University of Rochester, USA
📝 Publications (* denotes equal contribution)
2025
- Taming Overconfidence in LLMs: Reward Calibration in RLHFInternational Conference on Learning Representations (ICLR) 2025 | [ arXiv Code OpenReview Preview ]
2024
- S²FT: Efficient, Scalable and Generalizable LLM Fine-tuning by Structured SparsityConference on Neural Information Processing Systems (NeurIPS) 2024 | [ arXiv Code OpenReview Preview ]
- Development of UroSAM: A Machine Learning Model to Automatically Identify Kidney Stone Composition from Endoscopic VideoJournal of Endourology 2024 | [ Website ]
- Choosing Wisely and Learning Deeply: Selective Cross-Modality Distillation via CLIP for Domain Generalization
🔬 Research Experience
- 2025.01 - Present, Independent Study, Carnegie Mellon University, advised by Prof. William W. Cohen.
- 2024.06 - Present, Visiting Researcher (Onsite during summer), Washington University in St. Louis, advised by Prof. Jiaxin Huang.
- 2023.12 - 2024.05, Honor Independent Study, VIStA (Visual Intelligence & Social Multimedia Analytics), advised by Prof. Jiebo Luo and Dr. Rajat K. Jain.
- 2022.12 - 2024.05, Research Internship (Remote), Dream Lab iSchool UIUC, advised by Prof. Haohan Wang
📝 Service
- 2024.10 - Present, TMLR reviewer.
- 2022.01 - 2023.05, Teaching Assistant for CSC261/461: Database System, University of Rochester.