Jixuan Leng

M.S. in Machine Learning, Carnegie Mellon University (2024 to Present)
B.S. in Computer Science, University of Rochester (2020 to 2024)
8 Arborwood, Irvine, California, United States
jixuanl [at] cs.cmu.edu, jleng3 [at] u.rochester.edu, jixuanleng [at] gmail.com
Google scholar | DBLP | Github || Twitter/X | Zhihu | Wechat | Bilibili || Resume

I am currently a first-year master student in Machine Learning 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.

Research interests: Efficient LLM systems, Reliable Foundation Models, Out-Of-Distribution / Domain Generalization, Trustworthy Machine Learning, and related Applications of AI in Healthcares.

News

Oct 15, 2024	New preprint: “Taming Overconfidence in LLMs: Reward Calibration in RLHF” is out on arxiv. Code is also released.
Sep 25, 2024	One paper “S²FT: Efficient, Scalable and Generalizable LLM Fine-tuning by Structured Sparsity” accepted by NIPS 2024 (poster).
May 11, 2024	“Development of UroSAM: a machine learning model to automatically identify kidney stone composition from endoscopic video” is accepted for publication at Journal of Endourology.
Dec 10, 2023	Trying to build this webpage.

Selected Preprints (* denotes equal contribution)

Taming Overconfidence in LLMs: Reward Calibration in RLHF

Jixuan Leng , Chengsong Huang , Banghua Zhu , and Jiaxin Huang

Preprint 2024 | [ arXiv Code ]

Selected Publications (* denotes equal contribution)

S^2FT: Efficient, Scalable and Generalizable LLM Fine-tuning by Structured Sparsity

Xinyu Yang , Jixuan Leng , Geyang Guo , Jiawei Zhao , Ryumei Nakada , Linjun Zhang , Huaxiu Yao , and Beidi Chen

The Thirty-eighth Annual Conference on Neural Information Processing Systems (NeurIPS) 2024 | [ OpenReview ]
Development of UroSAM: A Machine Learning Model to Automatically Identify Kidney Stone Composition from Endoscopic Video

Jixuan Leng , Junfei Liu , Galen Cheng , Haohan Wang , Scott Quarrier , Jiebo Luo , and Rajat Jain

Journal of Endourology 2024 | [ Website ]

Introduction: Chemical composition analysis is important in prevention counseling for kidney stone disease. Advances in laser technology have made dusting techniques more prevalent, but this offers no consistent way to collect enough material to send for chemical analysis, leading many to forgo this test. We developed a novel machine learning (ML) model to effectively assess stone composition based on intraoperative endoscopic video data. Methods: Two endourologists performed ureteroscopy for kidney stones ≥ 10 mm. Representative videos were recorded intraoperatively. Individual frames were extracted from the videos, and the stone was outlined by human tracing. An ML model, UroSAM, was built and trained to automatically identify kidney stones in the images and predict the majority stone composition as follows: calcium oxalate monohydrate (COM), dihydrate (COD), calcium phosphate (CAP), or uric acid (UA). UroSAM was built on top of the publicly available Segment Anything Model (SAM) and incorporated a U-Net convolutional neural network (CNN). Discussion: A total of 78 ureteroscopy videos were collected; 50 were used for the model after exclusions (32 COM, 8 COD, 8 CAP, 2 UA). The ML model segmented the images with 94.77% precision. Dice coefficient (0.9135) and Intersection over Union (0.8496) confirmed good segmentation performance of the ML model. A video-wise evaluation demonstrated 60% correct classification of stone composition. Subgroup analysis showed correct classification in 84.4% of COM videos. A post hoc adaptive threshold technique was used to mitigate biasing of the model toward COM because of data imbalance; this improved the overall correct classification to 62% while improving the classification of COD, CAP, and UA videos. Conclusions: This study demonstrates the effective development of UroSAM, an ML model that precisely identifies kidney stones from natural endoscopic video data. More high-quality video data will improve the performance of the model in classifying the majority stone composition.
Choosing Wisely and Learning Deeply: Selective Cross-Modality Distillation via CLIP for Domain Generalization

Jixuan Leng , Yijiang Li , and Haohan Wang

Transactions on Machine Learning Research (TMLR) 2024 | [ arXiv Code OpenReview ]