Dan Ofer
Dan Ofer received the B.Sc. degree in psychobiology, in 2013, and the dual M.Sc. degree in bioinformatics and neurobiology from The Hebrew University. He is currently a PhD Candidate with Professor's Dafna Shahaf and Michal Linial, and an AI Researcher in industry since 2015. Previously, at SparkBeyond/McKinsey he developed AI solutions in multiple industries, including insurance, finance, healthcare, and novel biomarker discovery with CRI. His research interests include Biological Foundation models, explainable AI, automated feature engineering on tabular data, Protein LLMs, and AI in healthcare.
Passionate Bookworm, geek and Photographer
Session
machine learning excels at prediction, often leaving data scientists manually sifting through feature importance lists to find truly interesting insights. This talk introduces "InterFeat," an automated pipeline that goes beyond predictive power to identify features that are novel, plausible, and useful, i.e., "Interesting". We'll demonstrate how combining classical ML, knowledge graphs, literature mining, and Large Language Models (LLMs) can operationalize the elusive concept of "interestingness." Using a case study on real world biomedical data (UK Biobank), I show how this framework automatically surfaces potentially groundbreaking hypotheses (validated by doctors) that traditional methods miss. Attendees will learn a practical approach to accelerate discovery in their own complex datasets.