Song Wei - Machine Learning PhD Student - School of Industrial and Systems Engineering

Date: May 2nd

Time: 12:45 PM – 3:00 PM ET

Location: Groseclose 403

Meeting Link: https://gatech.zoom.us/j/6286168510?pwd=UmVacUhQL1RhZHY0VSt6TjRtMGFoQT09

Committee

Dr. Yao Xie (Advisor, School of Industrial and Systems Engineering, Georgia Tech)

Dr. Rishikesan Kamaleswaran (Department of Surgery, Duke University)

Dr. Feng Qiu (Argonne National Laboratory)

Dr. Yajun Mei (School of Industrial and Systems Engineering, Georgia Tech)

Dr. Gari Clifford (Department of Biomedical Informatics, Emory University & Department of Biomedical Engineering, Georgia Tech)

Abstract

Explainable prediction algorithms have become increasingly important in automated surveillance systems within the healthcare context, as they offer actionable insights for clinicians on duty to respond to predicted adverse events. In this thesis, I will present a real study on sepsis prediction, and several novel methods motivated by it. Those methods, developed with the help of recent advancements in statistics and optimization, enjoy strong theoretical guarantees and exhibit promising empirical performance. Importantly, with the numerical demonstration on the real data, I hope the developed methods can be extended to a broader range of real applications.