Logo: University of Southern California

Events Calendar

  • CS Colloquium: Michael Oberst (MIT) - Rigorously Tested & Reliable Machine Learning for Health

    Thu, Apr 13, 2023 @ 04:00 PM - 05:00 PM

    Thomas Lord Department of Computer Science

    Conferences, Lectures, & Seminars

    Speaker: Michael Oberst, MIT

    Talk Title: Rigorously Tested & Reliable Machine Learning for Health

    Series: CS Colloquium

    Abstract: How do we make machine learning as rigorously tested and reliable as any medication or diagnostic test?

    Machine learning (ML) has the potential to improve decision-making in healthcare, from predicting treatment effectiveness to diagnosing disease. However, standard retrospective evaluations can give a misleading sense for how well models will perform in practice. Evaluation of ML-derived treatment policies can be biased when using observational data, and predictive models that perform well in one hospital may perform poorly in another.

    In this talk, I will introduce methods I have developed to proactively assess and improve the reliability of machine learning models. A central theme will be the application of external knowledge, including guided review of patient records, incorporation of limited clinical trial data, and interpretable stress tests. Throughout, I will discuss how evaluation can directly inform model design.

    This lecture satisfies requirements for CSCI 591: Research Colloquium

    Biography: Michael Oberst is a final-year PhD candidate in Computer Science at MIT. His research focuses on making sure that machine learning in healthcare is safe and effective, using tools from causal inference and statistics. His work has been published at a range of machine learning venues (NeurIPS / ICML / AISTATS / KDD), including work with clinical collaborators from Mass General Brigham, NYU Langone, and Beth Israel Deaconess Medical Center. He has also worked on clinical applications of machine learning, including work on learning effective antibiotic treatment policies (published in Science Translational Medicine). He earned his undergraduate degree in Statistics at Harvard.

    Host: Yan Liu

    Location: Ronald Tutor Hall of Engineering (RTH) - 526

    Audiences: Everyone Is Invited

    Contact: Assistant to CS chair

    Add to Google CalendarDownload ICS File for OutlookDownload iCal File

Return to Calendar