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  • ECE Seminar: Machine Learning for Precision Health: A Holistic Approach

    Thu, Mar 17, 2022 @ 10:00 AM - 11:00 PM

    Ming Hsieh Department of Electrical and Computer Engineering

    Conferences, Lectures, & Seminars

    Speaker: Dr. Ahmed Alaa, Postdoctoral Associate, Broad Institute of MIT & Harvard, MIT CSAIL

    Talk Title: Machine Learning for Precision Health: A Holistic Approach

    Abstract: Machine learning (ML) methods, combined with large-scale electronic health databases, could enable a personalized approach to healthcare by improving patient-specific diagnosis, prognostic predictions, and treatment decisions. If successful, this approach would be transformative for clinical research and practice. In this talk, I will describe a holistic approach to ML for precision health that comprises a three-step procedure: (1) data characterization and understanding, (2) model development and (3) model deployment. Next, I will demonstrate one instantiation of this approach in the context of developing ML models for predicting patient-level response to therapies using observational data. I will focus on a multi-task learning model that uses Gaussian processes to estimate the causal effects of a treatment on individual patients and discuss its application in various disease areas. Finally, I will discuss exciting avenues for future work, including ML methods for learning from unannotated clinical data, generating synthetic data and integrating clinical knowledge into data-driven modeling.

    Biography: Dr. Ahmed Alaa is a postdoctoral associate at Massachusetts Institute of Technology (MIT) and the Broad Institute of MIT and Harvard University. Previously, he was a joint postdoctoral scholar at Cambridge University, Cambridge Center for AI in Medicine and the University of California, Los Angeles (UCLA). He obtained his Ph.D. in Electrical and Computer Engineering from UCLA, where he was also a recognized (visiting) Ph.D. student at Oxford University. His research focuses on developing machine learning (ML) methods that can leverage healthcare data to enable a patient-centric approach to medicine, whereby ML models can inform disease diagnosis, prognosis and treatment decisions based on the characteristics of individual patients. He is the recipient of the (school-wide) 2021 Edward K. Rice Outstanding Doctoral Student Award at UCLA.

    Host: Dr. Ashutosh Nayyar, ashutosn@usc.edu

    Webcast: https://usc.zoom.us/j/94383946134?pwd=U1N4emFRaDBnc0pTd2VXUHMwSkVidz09

    Location: Hughes Aircraft Electrical Engineering Center (EEB) - 248

    WebCast Link: https://usc.zoom.us/j/94383946134?pwd=U1N4emFRaDBnc0pTd2VXUHMwSkVidz09

    Audiences: Everyone Is Invited

    Contact: Mayumi Thrasher

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