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  • PhD Defense - Amulya Yavdav

    Thu, Apr 12, 2018 @ 01:00 PM - 03:00 PM

    Computer Science

    University Calendar


    PhD Candidate: Amulya Yadav

    Committee: Milind Tambe (Chair), Kristina Lerman, Aram Galstyan, Eric Rice, Dana Goldman

    Title: Artificial Intelligence for Low Resource Communities: Influence Maximization in an Uncertain World

    Time: April 12 (Thursday) 1:00-3:00 PM

    Location: KAP 209

    Abstract:


    The potential of Artificial Intelligence (AI) to tackle challenging problems that afflict society is enormous, particularly in the areas of healthcare, conservation and public safety and security. Many problems in these domains involve harnessing social networks of under-served communities to enable positive change, e.g., using social networks of homeless youth to raise awareness about Human Immunodeficiency Virus (HIV) and other STDs. Unfortunately, most of these real-world problems are characterized by uncertainties about social network structure and influence models, and previous research in AI fails to sufficiently address these uncertainties, as they make several unrealistic simplifying assumptions for these domains.


    This thesis addresses these shortcomings by advancing the state-of-the-art to a new generation of algorithms for interventions in social networks. In particular, this thesis describes the design and development of new influence maximization algorithms which can handle various uncertainties that commonly exist in real-world social networks (e.g., uncertainty in social network structure, evolving network state, and availability of nodes to get influenced). These algorithms utilize techniques from sequential planning problems and social network theory to develop new kinds of AI algorithms. Further, this thesis also demonstrates the real-world impact of these algorithms by describing their deployment in three pilot studies to spread awareness about HIV among actual homeless youth in Los Angeles. This represents one of the first-ever deployments of computer science based influence maximization algorithms in this domain. Our results show that our AI algorithms improved upon the state-of-the-art by 160% in the real-world. We discuss research and implementation challenges faced in deploying these algorithms, and lessons that can be gleaned for future deployment of such algorithms. The positive results from these deployments illustrate the enormous potential of AI in addressing societally relevant problems.

    Location: Kaprielian Hall (KAP) - 209

    Audiences: Everyone Is Invited

    Contact: Lizsl De Leon

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