chufan2 [at] illinois.com
University of Illinois Urbana-Champaign
Thomas M. Siebel Center for Computer Science
201 North Goodwin Avenue
Urbana, IL 61801, USA
Hello! I am 3rd year PhD Candidate at University of Illinois Urbana-Champaign advised by
Professor Jimeng Sun.
My focus areas include Information Extraction, Tabular Data, and Machine Learning for Healthcare.
I am also broadly interested in less-than-supervised Machine Learning and memory-augmented models.
In my Masters, I was advised by
Professor Artur Dubrawski
at Carnegie Mellon University as a part of
AutonLab
where I completed my thesis: Addressing Time-series Signal Quality in Healthcare Data.
I enjoy working on projects that involve a combination of machine learning, natural language processing, and healthcare data. Shoot me a message on Linkedin or email if you want to chat!
Curently seeking Research Internship Opportunities for 2025 and beyond!
Internship Experiences
-
NLP Research Intern @ Optum (UnitedHealth Group)
Remote, Summer 2024
Clinical Note Entity Recognition
Advised by Sanjit Batra and Robert Tillman
-
Research Intern @ Medidata Solutions (Dassault Systèmes)
New York, NY, Summer 2023
Synthetic clinical trial data generation
Advised by Mandis Beigi and Afrah Shafquat
Selected Publications
* indicates equal contributionPeer-reviewed
- C. Gao, X. Wang*, J. Sun*, "TTM-RE: Memory-Augmented Document-Level Relation Extraction," in ACL, 2024
- Z. Wang*, C. Gao*, C. Xiao, J. Sun, "MediTab: Scaling Medical Tabular Data Predictors via Data Consolidation, Enrichment, and Refinement," in IJCAI, 2024
- Hanyin, W., C. Gao, C. Dantona, B. Hull, J. Sun, "DRG-LLaMA: tuning LLaMA model to predict diagnosis related group for hospitalized patient," in Nature Digitial Medicine, 2024
- C. Gao, N. Gisolfi, and A. Dubrawski, "Signal quality auditing for time-series data" in AAAI Fall Symposium: Artificial Intelligence for Predictive Maintenance, 2022
- C. Gao, "Addressing time-series signal quality in healthcare data," Master's thesis, Carnegie Mellon University, 2022
- C. Gao*, M. Goswami*, J. Chen, and A. Dubrawski, "Classifying unstructured clinical notes via automatic weak supervision," in Machine Learning for Healthcare, 2022
- C. Gao, F. Falck, M. Goswami, A. Wertz, M. R. Pinsky, and A. Dubrawski, "Detecting patterns of physiological response to hemodynamic stress via unsupervised deep learning," in NeurIPS ML4H Workshop, 2019
- C. Gao, S. Clark, J. Furst, and D. Raicu, "Augmenting LIDC dataset using 3D generative adversarial networks to improve lung nodule detection." In Medical Imaging 2019: Computer-Aided Diagnosis, vol. 10950, pp. 398-407. SPIE, 2019.
- Gao, C., Fan, X., Sun, J., Wang, X. (2023). "PromptRE: Weakly-Supervised Document-Level Relation Extraction via Prompting-Based Data Programming," In ArXiv, 2023
- Z. Wang*, C. Gao*, J. Sun, "A Survey: In Silico Trials" In ArXiv, 2023
- M. Goswami*, C. Gao*, B. Boecking, A. Dubrawski, "Active Learning for Weakly Supervised Model Refinement" In ArXiv, 2022
- S. Ray*, S. Lakdawala*, M. Goswami*, and C. Gao*, "Learning graph neural networks for multivariate time series anomaly detection" In ArXiv, 2022
Miscellaneous
- "Alumni Spotlight: Chufan Gao," The Journal of Purdue Undergraduate Research: Vol. 13, Article 52.
- Fractal tree generation: I limited it to 50 trees for crashing reasons. Much thanks to Eura Shin (PhD Student @ Harvard!) for the base tree generation code!