person no image

Nick Fong

Data Scientist

Biography

Nick brings experience from petaflop-scale supercomputers and software engineering roles in Silicon Valley to his work as a Data Scientist in the Department of Anesthesia and Perioperative Care at UCSF. Since joining the department in 2018, he has applied this technical background to diverse research projects, spanning from deep learning and ensemble machine learning models for ICP prediction, to predictive modeling for acute kidney injury, to pragmatic clinical trial analyses.

His recent work on the environmental impact of volatile anesthetics—analyzing over 2.7 billion rows of data—earned him the 2025 Kosaka Top Scholars Abstract Award from the International Anesthesia Research Society (IARS).

Nick's primary research interests focus on responsibly building AI-powered models to assist in clinical prognostication. Specifically, he works on developing methods to account for systemic data disparities, optimizing the clinician-AI interface, and personalizing models to prioritize meaningful functional recovery, ultimately making them more patient-centered and individualized. As a key contributor to the Open Oximetry project, he led the aggregation and publication of a foundational open-access dataset that provides the global research community with resources to ensure pulse oximeter accuracy and reliability across all skin pigmentations.

His clinical focus includes perioperative and critical care, particularly resuscitation science, ethics, and optimizing patient-centered decision-making and communication in end-of-life scenarios.

Education

05/2021 - Diversity, Equity, and Inclusion Champion Training, University of California

Publications