I am an AI/ML researcher at Optum AI (formerly Optum Labs/UnitedHealth Group R&D). My research focuses on the intersection of machine learning and medicine, leveraging electronic health records, physiological waveform signals, genomics, medical imaging data, and clinical text. Additionally, I am interested in large language models, causal inference, graphical models, imputation/missing data, and entrepreneurship.

I completed my PhD in computer science at UCLA, under the guidance of Professor Eran Halperin. My prior industry experience includes a summer internship at Microsoft Research working with Daniel McDuff, four years as an intern at Intel under the mentorship of Gans Srinivasa, and three years as the founding engineer at Omics Data Automation (now dātma) working with Gans. Prior to UCLA, I earned my degree in Electrical and Computer Engineering from Oregon State University.

Selected Publications

*- denotes joint authorship

Brian L. Hill, Melika Emami, Vijay S. Nori, Aldo Cordova-Palomera, Robert Tillman, Eran Halperin; “CHIRon: A Generative Foundation Model for Structured Sequential Medical Data” - Deep Generative Models for Health (DGM4H) Workshop, NeurIPS (2023). [paper]

Joel Stremmel, Brian L. Hill, Jeffrey Hertzberg, Jaime Murillo, Llewelyn Allotey, Eran Halperin; “Extend and Explain: Interpreting Very Long Language Models” - Proceedings of the 2nd Machine Learning for Health (ML4H) symposium (2022). [paper] [preprint] [code]

Mike Thompson*, Brian L. Hill*, Nadav Rakocz, Jeffrey N. Chiang, Daniel Geschwind, Sriram Sankararaman, Ira Hofer, Maxime Cannesson, Noah Zaitlen, Eran Halperin; “Methylation risk scores are associated with a collection of phenotypes within electronic health record systems” - npj Genomic Medicine (2022). [paper] [preprint] [code]

Brian L. Hill, Nadav Rakocz, Akos Rudas, Jeffrey N. Chiang, Sidong Wang, Ira Hofer, Maxime Cannesson*, Eran Halperin*; “Imputation of the continuous arterial line blood pressure waveform from non-invasive measurements using deep learning” - Scientific Reports (2021). [paper] [code]

Brian L. Hill*, Robert Brown*, Eilon Gabel, Nadav Rakocz, Christine Lee, Maxime Can- nesson, Pierre Baldi, Loes Olde Loohuis, Ruth Johnson, Brandon Jew, Uri Maoz, Aman Mahajan, Sriram Sankararaman, Ira Hofer, Eran Halperin; “An automated machine learning-based model predicts postoperative mortality using readily-extractable preoperative electronic health record data” - British Journal of Anaesthesia (2019). [paper] [preprint] [code]