CAREER: New Frontiers in Sequential Decision Making with a View Towards Mobile Health Applications

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This NSF CAREER project uses machine learning to advance the science of health interventions delivered via mobile devices. The field of mobile health (mhealth) is advancing rapidly spurred by the widespread use of mobile devices across the world. Mhealth researchers are using mobile devices to diagnose, manage, and treat a variety of health conditions including, but by no means limited to, alcohol abuse, depression, obesity, and smoking. The project creates new learning algorithms that learn to personalize mhealth interventions to the changing needs of individual users. It also develops techniques that use the social network among users to intervene so that the entire network is steered towards positive health outcomes. The project will give undergraduate researchers the opportunity to work on cutting edge data science driven by socially relevant problems. It will develop new graduate level courses on sequential decision making. Software including simulations specific to health domains, implementations of learning algorithms, and mobile apps that deliver interventions will be publicly released. Research results will be communicated to, and feedback solicited from, the general public under the auspices of the newly formed Michigan Institute for Data Science (MIDAS). Involvement of under-represented minorities will be encouraged via the NextProf Science workshop and Faculty Allies for Diversity program at the University of Michigan.The project involves solving three technical challenges. First, existing algorithms for sequential decision making, such as contextual bandit algorithms, need to be redesigned to make them suitable for adoption in mhealth. The project will develop actor-critic contextual bandit algorithms that separate the representation of the policy from the representation of the reward function. Having an interpretable, low dimensional policy space is crucial for interpretability of the learned policy by the domain scientist. Second, most of the literature on bandit problems and reinforcement learning assumes stationarity, an unreasonable assumption in mhealth. Using recent advances in the field of no-regret learning, the project will develop learning algorithms that can deal with non-stationarity. Third, the science of network interventions is in its infancy. The project will serve a catalyst for its development by synthesizing recent advances in high dimensional statistical learning and control of complex networks to design learning algorithms that intervene at a small number of nodes in a time evolving network to achieve a desired long term objective.