Abstract: 

I will walk through use cases for several different algorithms, to illustrate how the algorithms can be used in interesting and
unexpected ways. The audience should have some basic knowledge of machine learning methods and algorithms,
since this is not a workshop about machine learning in general. The audience does not need advanced knowledge, but
the presented use cases should be useful and informative even for those people too. The attendees will not need to
download or to prepare anything in advance. The applications that I discuss will cover different industries (healthcare, science,
marketing, business, IoT). Consequently, the applications are relevant to any industry, since the applications cover the basic
types of discovery from data science: Class Discovery, Trend/Correlation Discovery, Novelty (Anomaly) Discovery, and
Link (Association) Discovery -- those are applicable everywhere!

Bio: 

Kirk Borne is a data scientist and an astrophysicist who has used his talents at Booz Allen since 2015. He was professor of astrophysics and computational science at George Mason University (GMU) for 12 years. He served as undergraduate advisor for the GMU data science program and graduate advisor in the computational science and informatics Ph.D. program.

Kirk spent nearly 20 years supporting NASA projects, including NASA's Hubble Space Telescope as data archive project scientist, NASA's Astronomy Data Center, and NASA's Space Science Data Operations Office. He has extensive experience in large scientific databases and information systems, including expertise in scientific data mining. He was a contributor to the design and development of the new Large Synoptic Survey Telescope, for which he contributed in the areas of science data management, informatics and statistical science research, galaxies research, and education and public outreach.