Machine learning for discovery: deciphering RNA splicing logic

Abstract:
Recent advances in machine learning, such as deep learning, have led to powerful tools for modeling complex data with high predictive accuracy. However, the resulting models are typically black box, limiting their usefulness in scientific discovery. I will describe an ``interpretable-by-design'' machine learning model capturing a fundamental cellular process known as RNA splicing. Our model provides a systematic understanding of RNA splicing logic, recapitulating and extending existing domain knowledge.

Bio:
Oded Regev is a Silver Professor at the Courant Institute of Mathematical Sciences at New York University. He received his Ph.D. in computer science from Tel Aviv University in 2001 under the supervision of Yossi Azar, continuing to a postdoctoral fellowship at the Institute for Advanced Study. He is a recipient of the 2019 Simons Investigator Award, the 2018 Gödel Prize, several best paper awards, and was a speaker at the 2022 International Congress of Mathematicians. His main research areas include machine learning, RNA biology, theoretical computer science, and quantum computation.