Dr. Saria is the John C. Malone Assistant Professor at Johns Hopkins University. Her work enables new classes of diagnostic and treatment planning tools for healthcare—tools that use statistical machine learning to tease out subtle information from “messy” observational datasets, and provide reliable inferences for individualizing care decisions. She has received many awards including the DARPA Young Faculty Award (2016), MIT Technology Review’s ‘35 Innovators under 35’ (2017), the Sloan Research Fellowship in CS (2018), the World Economic Forum Young Global Leader (2018), and the National Academies of Medicine Emerging Leader in Health and Medicine (2018).
Dr. Murphy is Professor of Statistics at Harvard University, Radcliffe Alumnae Professor at the Radcliffe Institute, Harvard University, and Professor of Computer Science at the Harvard John A. Paulson School of Engineering and Applied Sciences. Her research focuses on improving sequential, individualized, decision making in health, in particular on clinical trial design and data analysis to inform the development of mobile health interventions. She is a MacArthur Fellow, a member of the US National Academy of Sciences and US National Academy of Medicine, and President of the Institute of Mathematical Statistics.
Abstract: There are a variety of formidable challenges to reinforcement learning and control for use in designing mobile health interventions for individuals with chronic disorders. Challenges include settings in which most treatments delivered by a mobile device have immediate nonnegative (hopefully positive) effects compared to no treatment but 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 monitoring analyses. Other challenges include an immature domain science concerning the system dynamics but the need to incorporate some domain science due to low signal to noise ratio as well as non-stationary and limited data on individuals with chronic disorders. 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 the learning algorithm.
Ms. Moorosi is a Software Engineer and Researcher at Google AI in Accra, Ghana. She focuses on ML research and applications with interests mainly in computer vision and fairness. Before Google, she was a senior Data Science researcher at South Africa’s national science lab, Council for Scientific and Industrial Research, working on projects with real-world impact including rhino poaching prevention with park rangers. She is an active member of Women in Machine Learning, Black in Artificial Intelligence, and a co-organizer of the Deep Learning Indaba.
Dr. Gros is a member of the Facebook’s Spatial Computing Team and has a background in complex systems. Born in Ethiopia and studying in Germany and the US, Dr. Gros brings a global perspective to problems such as creating spatial maps with very detailed building and population information based on satellite data. His work is being used by several non-governmental and academic organizations. He also works on spatial demographic questions and as a postdoc at the New England Complex Systems Institute, he worked on spatial socio-economic systems, including ethnic violence.