Colloquium Details
Challenges and Advances in Disease Prediction Across Medical Data Modalities
Speaker: Eran Halperin, UCLA
Location: 60 Fifth Avenue 150
Date: March 18, 2025, 11 a.m.
Host: Sumit Chopra
Synopsis:
Predicting disease onset and progression is a fundamental challenge in computational medicine, with important implications for chronic disease management and treatment planning. While AI methods from other domains have been applied to disease prediction with some success, several key challenges make this task uniquely difficult. Unlike many AI/ML problems where human experts can provide ground-truth labels, disease prediction often remains unresolved even by clinicians. Additionally, medical data modalities—such as electronic health records (EHR), medical imaging, and genomic data—exhibit distinctive characteristics, including systematic biases, missingness, and interpretability challenges. Without addressing these factors, predictive models risk being suboptimal, lacking explainability, or failing to account for critical data limitations.
In this talk, I will present recent advances in disease prediction across three distinct modalities. Specifically, I will discuss (1) the adaptation of the GPT architecture for disease prediction using EHR data, (2) the identification of disease biomarkers from 3D medical imaging, and (3) the application of methylation risk scores to reduce missingness in EHR data and improve predictive performance. Through these examples, I will highlight key methodological considerations and challenges in developing AI models for medical applications.
Note: In-person attendance only available to those with active NYU ID cards
Speaker Bio:
Dr. Eran Halperin is an Adjunct Professor in the Department of Computer Science at UCLA. His lab focuses on developing machine learning methods for analyzing medical data in the context of disease, incorporating genomic data (genetics, epigenetics, and microbiome), electronic health records, medical imaging, and physiological waveforms. Previously, Dr. Halperin was a Professor at UCLA, holding joint appointments in the Departments of Computer Science, Computational Medicine, Anesthesiology, and Human Genetics. He also held research and postdoctoral positions at the International Computer Science Institute in Berkeley, Tel Aviv University, and Princeton University. Beyond academia, Dr. Halperin has held leadership roles in industry, most recently serving as SVP of AI/ML at Optum Labs, where he established a research department dedicated to innovations in machine learning applications in healthcare. He has authored over 170 peer-reviewed papers and received numerous honors for his contributions, including the Rothschild Fellowship, the Technion-Juludan Prize for technological advancements in medicine, and the Krill Prize. He was also elected a Fellow of the International Society of Computational Biology (ISCB).