Events Calendar

AI Seminar-Kiri Wagstaff: "Automated data prioritization and explanation for scientific discovery of Martian minerals, exoplanets, and more"

Speaker: Kiri Wagstaff, Jet Propulsion Laboratory-NASA

Talk Title: "Automated data prioritization and explanation for scientific discovery of Martian minerals, exoplanets, and more"

Series: Artificial Intelligence Seminar

Abstract: Inundated by terabytes of data flowing from telescopes, microscopes, DNA sequencers, etc., scientists in various disciplines have a need for automated methods for prioritizing data for review. Which observations are most interesting or unusual, and why?

I will describe DEMUD (Discovery by Eigenbasis Modeling of Uninteresting Data), which iteratively prioritizes items from large data sets to provide a diverse traversal of interesting items. By modeling what the user already knows and/or has already seen, DEMUD can focus attention on the unexpected, facilitating new discoveries. Uniquely, DEMUD also provides a domain-relevant explanation for each selected item that indicates why it stands out. DEMUD's explanations offer a first step towards automated interpretation of scientific data discoveries.

We are using DEMUD in collaboration with scientists from the Mars Science Laboratory, the Mars Reconnaissance Orbiter, the Kepler exoplanet telescope, Earth orbiters, and more. It provides scalable performance, interpretable output, and new insights into very large data sets from diverse disciplines.

This is joint work with James Bedell, Nina L. Lanza, Tom G. Dietterich, Martha S. Gilmore, and David R. Thompson.



Biography: Kiri L. Wagstaff is a senior researcher in artificial intelligence and machine learning and a tactical activity planner for the Mars rover Opportunity at the Jet Propulsion Laboratory. Her research focuses on developing new machine learning and data analysis methods, particularly those that can be used for in situ analysis onboard spacecraft such as orbiters, landers, rovers, and so on. She holds a Ph.D. in Computer Science from Cornell University and an M.S. in Geological Sciences from the University of Southern California. She received a 2008 Lew Allen Award for Excellence in Research for work on the sensitivity of machine learning methods to high-radiation space environments and a 2012 NASA Exceptional Technology Achievement award for work on transient detection methods in radio astronomy data. She is passionate about keeping machine learning relevant to real-world problems and is co-editing a special issue of the Machine Learning journal on Machine Learning for Science and Society.

http://www.wkiri.com/

Host: Yolanda Gil

Location: 11th Flr Conf Rm # 1135, Marina Del Rey

Audiences: Everyone Is Invited

Contact: Peter Zamar