[Mobilizeplans-starstudents] Mobilize Center Seminar, Ambuj Tewari

Diane Bush dbush1 at stanford.edu
Tue May 30 14:13:04 PDT 2017

Our next Mobilize Center Seminar is scheduled for Thursday, June 8th, and features Ambuj Tewari from the University of Michigan.  He will be presenting “From Ads to Interventions: Contextual Bandits in Mobile Health.”

Our seminars are held on a monthly basis. We look forward to seeing you next week and at future seminars!

From Ads to Interventions: Contextual Bandits in Mobile Health

Thursday, June 8
noon - 1 pm

Y2E2 300, Stanford University

A key problem in designing just-in-time adaptive interventions (JITAIs) for mobile health is to learn decision rules from data that can map tailoring variables (user mood, time of day, weather conditions) to intervention options (should we send a message to the user’s phone right now?). Contextual bandit algorithms attempt to construct such decision rules with the goal of maximizing some numerical outcome following every decision point (say, the number of steps walked in 30 minutes after sending an activity encouraging message).

The first paper on contextual bandits was written by Michael Woodroofe in 1979 but the term “contextual bandits” was invented only recently in 2008 by Langford and Zhang. Woodroofe’s motivating application was clinical trials whereas modern interest in this problem was driven to a great extent by problems on the internet, such as online ad and online news article placement. We have now come full circle because contextual bandits provide a natural framework for sequential decision making in mobile health. We will survey the contextual bandits literature with a focus on modifications needed to adapt existing approaches to the mobile health setting. We discuss specific challenges in this direction such as: good initialization of the learning algorithm, finding interpretable policies, assessing usefulness of tailoring variables, computational considerations, robustness to failure of assumptions, and dealing with variables that are costly to acquire or missing.

Ambuj Tewari is an assistant professor in the Department of Statistics and the Department of EECS (by courtesy) at the University of Michigan, Ann Arbor. His is also affiliated with the Michigan Institute for Data Science (MIDAS). He obtained his PhD under the supervision of Peter Bartlett at the University of California at Berkeley. His research interests lie in machine learning including statistical learning theory, online learning, reinforcement learning and control theory, network analysis, and optimization for machine learning. He collaborates with scientists to seek novel applications of machine learning in mobile health, learning analytics, and computational chemistry. His research has been recognized with paper awards at COLT 2005, COLT 2011, and AISTATS 2015. He received an NSF CAREER award in 2015 and a Sloan Research Fellowship in 2017.

To keep up-to-date on upcoming speakers and the dates, visit Mobilize Events<http://mobilize.stanford.edu/events/>.

Diane Bush
Assistant to Professor Scott Delp
NMBL, Mobilize Center, OpenSim
Stanford University
dbush1 at stanford.edu<mailto:dbush1 at stanford.edu>

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