Deepavali Bhagwat

Department of Computer Science
University of California, Santa Cruz
1156 High Street
Santa Cruz, CA 95064

Email:
Phone:(831)459 4458

I am a PhD candidate at the University of California, Santa Cruz. I work with the SSRC group in the Archival Storage project. My advisor is Prof. Darrell Long.

I completed my Masters in Computer Science at UCSC under the guidance of Prof. Alkis Polyzotis.
Before coming to grad school I worked as a software consultant in the San Francisco Bay Area. I did my undergrad in Computer Science at the D. Y. Patil College of Engineering, Pune, India. Here is my resume.

My broad topics of interests include long-term archival storage systems and distributed storage sytems. Specifically, I am interested in scalability with respect to data de-duplication, indexing and retrieval in large-scale storage systems.

Publications

  1. Deepavali Bhagwat, Kave Eshghi, and Pankaj Mehra, Content-based Document Routing and Index Partitioning for Scalable Similarity-based Searches in a Large Corpus, In Proceedings of the Thirteenth ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD) 2007, San Jose, CA (pdf)

  2. Deepavali Bhagwat, Kristal Pollack, Darrell D. E. Long, Thomas Schwarz S.J., Ethan L. Miller, and Jehan-François Pâris, Providing High Reliability in a Minimum Redundancy Archival System, In Proceedings of the 14th International Symposium on Modeling, Analysis, and Simulation of Computer and Telecommunication Systems (MASCOTS) 2006, Monterey, CA (pdf)

  3. Deepavali Bhagwat and Neoklis Polyzotis, Searching a File System using Inferred Semantic Links , ACM Hypertext 2005, Salzburg, Austria.(pdf)

  4. Deepavali Bhagwat, Laura Chiticariu, Gaurav Vijayvargiya and Wang-Chiew Tan, An Annotation Management System for Relational Databases, VLDB Journal Special Issue (Best papers of 2004). (pdf)

  5. Deepavali Bhagwat, Laura Chiticariu, Gaurav Vijayvargiya and Wang-Chiew Tan, An Annotation Management System for Relational Databases, International Conference on Very Large Databases (VLDB) 2004, Toronto, Canada. (pdf)