Upcoming Seminars

Other UCLA departments frequently hold seminars related to Statistics and of likely of interest to our members. Here are links to UCLA Biostatistics seminars and UCLA Biomath seminars:
https://www.biostat.ucla.edu/2019-seminars
http://www.biomath.ucla.edu/seminars/

How to Subscribe to the UCLA Statistics Seminars Mailing List
Join the UCLA Statistics seminars mailing list by sending an email to sympa@sympa.cts.ucla.edu with “subscribe stat_seminars” (without quotation marks) in the subject field and the message body blank. This needs to be done from the address that is to be subscribed. After doing that please respond to the email that you receive. An automated email will be sent which confirms that you have been added.

How to Unsubscribe from the UCLA Statistics Seminars Mailing List
You may be receiving our seminar emails because you are subscribed to our seminars mailing list (or one of our other mailing lists). You can determine which is the case by looking at the subject line of a seminar email. You may unsubscribe from the seminar mailing list by sending an email to sympa@sympa.cts.ucla.edu with “unsubscribe stat_seminars” (without quotation marks) in the subject field and the message body blank. This needs to be done from the address that is subscribed. After sending that email please follow the directions in the email response that you receive. If you are getting our seminar emails because of a subscription to one of our other mailing lists then the word “seminars” in the subject field must have the appropriate replacement.

Tuesday, 11/24/2020, Time: 11:00am – 12:15pm PST
Challenges in Developing Learning Algorithms to Personalize Treatment in Real Time

Susan Murphy, Professor of Statistics
Harvard University

Abstract:
There are a variety of formidable challenges to reinforcement learning and control for use in designing digital health interventions for individuals with chronic disorders. Challenges include settings in which most treatments delivered by a smart device have immediate nonnegative (hopefully positive) effects but the largest longer term effects tend to be negative due to user burden. Furthermore the resulting data must be amenable to conducting a variety of statistical analyses, including causal inference as well as for use in monitoring analyses. Other challenges include an immature domain science concerning the system dynamics yet the need to incorporate some domain science due to low signal to noise ratio as well as non-stationary and sparse data. Here we describe how we confront these challenges including our use of low variance proxies for the delay effects to the reward (e.g. immediate response) in an online “bandit” learning algorithm for use in personalizing mobile health interventions.

Bio:
Dr. Susan A. Murphy is a Radcliffe Alumnae Professor at the Radcliffe Institute and a professor of statistics and computer science at the Harvard John A. Paulson School of Engineering and Applied Sciences. A 2013 recipient of a MacArthur Fellowship, she was previously the H. E. Robbins Distinguished University Professor of Statistics, a research professor at the Institute for Social Research, and a professor of psychiatry, all at the University of Michigan.

Dr. Murphy earned her BS from Louisiana State University and her PhD from the University of North Carolina at Chapel Hill. Her research focuses on analytic methods to design and evaluate medical treatments that adapt to individuals, including some that use mobile devices to deliver tailored interventions for drug addicts, smokers, and heart disease patients, among others. She is a member of the National Academy of Medicine and of the National Academy of Sciences.