AI Seminar

Bridging Data, Design, and Domain Knowledge to Build Human-Centered Systems that Support Wellbeing

Elizabeth MurnanePostdocStanford University

Today we face "wicked" societal challenges including a growing prevalence of chronic disease, educational inequalities, and unsustainable disruptions to the natural ecosystems on which all life depends. Technology has the potential to help tackle such globally shared problems by transforming how we monitor, understand, and attempt to positively influence behavior on a broad scale. To ensure the fairness, feasibility, and ultimate effectiveness of such systems, however, I argue it is crucial to take an integrative approach that employs human-centered design, grounds decisions in theory and other forms of domain knowledge, and utilizes personal data to tailor experiences. To illustrate these practices in action, I will concentrate in this talk on the health context and overview my work to build self-assessment tools, passive sensing techniques, and personalized informatics and intervention systems. Specifically, I will dive more deeply into three such projects: developing graphical and tangible interfaces for measuring pain, mobile analytics platforms for modeling alertness patterns, and data-driven narratives for motivating sustained behavior change. To conclude, I will discuss promising future directions I am excited to explore, including intelligent systems that promote creativity and collaboration, novel paradigms for embodied and immersive interaction, and research to engage with digital ethics issues, as part of responsibly developing these systems and studying their fundamental impacts on humanity.
Elizabeth Murnane is a Postdoctoral Scholar in Computer Science at Stanford University, supervised by James Landay. The overarching goal of Elizabeth's research is to design, build, and evaluate novel human-centered computing systems that support well-being, broadly construed, at individual, group, and societal levels. She is particularly compelled by applications that address societal challenges in areas including health, education, civic engagement, and sustainability. Elizabeth received her Ph.D. in Information Science from Cornell University, advised by Dan Cosley and Geri Gay. In addition to being published in top-tier venues, her research has been recognized through best paper awards and honorable mentions as well as various accolades such as an NSF Graduate Research Fellowship and a Microsoft Graduate Women's Scholarship. Before graduate school, Elizabeth co-founded and was the lead engineer of an MIT spin-off that built interactive visualization tools to help software developers make sense of complex source code and was an MIT $100K Entrepreneurship Competition semi-finalist. Prior, she received her Bachelor of Science in Mathematics with Computer Science from MIT, where her undergraduate research in the Computer Science and Artificial Intelligence Lab focused on conversational agents, recommender systems, and the semantic web.

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