LSST project Data Mining Research working group
Office: 115 Planetary Hall Phone: 703-993-8402
Office Hours: by appointment
Personal Website: http://classweb.gmu.edu/kborne/
Current Research Interests
1. Astronomical and astrophysical research: both observational and theoretical. Main research interests include the dynamics and evolution of galaxies and groups of galaxies, particularly the effects of tidal encounters on the structure and evolution of such systems, with special attention given to rings galaxies (including the Cartwheel Ring Galaxy), galaxy mergers, the progenitors of mergers, the galactic-scale consequences of merger episodes, the numerical simulation of merger events, and the properties of merger remnants. Observational astronomy projects with HST and the Chandra X-ray Observatory have recently focused on how gas-rich galaxy-galaxy collisions induce and affect the phenomenally strong starbursts seen in the incredible sample of ultraluminous IR galaxies (ULIRGs). The ULIRGs may represent the link between quasars and normal quiescent elliptical galaxies, and they demonstrate extraordinarily high rates of star formation accompanying strong signatures of tidal interaction. Research on multiple-merging ULIRGs indicates that they may represent the final collapsed state of compact groups of galaxies.
2. Scientific data mining: As a senior member of the NVO and LSST (Large Synoptic Survey Telescope) project teams, research has focused on mining of very large scientific databases, distributed data mining in the National Virtual Observatory (NVO), classification of real-time astronomical events from very large sky survey projects, data mining related to extragalactic and colliding galaxy research problems, mining of large databases for new knowledge nuggets, development of algorithms for distributed mining of distributed data, novel information retrieval algorithms, scientific database development, archival research with large databases, and science education research specifically focused on inquiry-based science using real science data in the classroom
- "Science & Archiving for Space Science," T. Eastman, K.Borne, et. al., Data Science Journal, 4, 67 (2005).
- "Distributed Data Mining for Astronomy Catalogs," C. Gianella, H. Dutta, K. Borne, R.,Wolff, & H. Kargupta, in SIAM Scientific Data Mining, peer-reviewed proceedings (2006).
- "Collaborative Knowledge-Sharing for E-Science," K. Borne & T. Eastman, in AAAI Semantic Web for Collaborative Knowledge Acquisition, peer-reviewed proceedings (2006).
- "Distributed Top-K Outlier Detection from Astronomy Catalogs," H. Dutta, C. Gianella, K. Borne, & H. Kargupta, in SIAM Scientific Data Mining, peer-reviewed proceedings (2007).
- "A Machine Learning Classification Broker for Petascale Mining of Large-scale Astronomy Sky Survey Databases", K. Borne, to be published in the proceedings of the Next Generation Data Mining 2007 NGDM'07 conference (2007).
- "A Machine Learning Classification Broker for the LSST Transient Database", K.Borne, Astronomische Nachrichten, in press (2008)
My science education research specifically focuses on inquiry-based science using real science data in the classroom.
CSI 710 Scientific databases
CSI 991 Space sciences Seminar