"In theory, there is no difference between theory and practice. But, in practice, there is."

Nina Mishra
Associate Professor of Computer Science

Department of Computer Science
School of Engineering and Applied Science
University of Virginia
151 Engineer's Way, P.O. Box 400740
Charlottesville, Virginia 22904-4740

Phone: (434) 243-2146
Fax: (434) 982-2214
Email: nmishra@cs.virginia.edu
Office: 226B Olsson Hall, UVa

Home page of Nina Mishra

Areas of Interest

Data mining, machine learning, and privacy-preserving algorithms

Biographical Sketch

ina Mishra earned her PhD in Computer Science from the University of Illinois at Urbana-Champaign in 1997. She held joint appointments as a Senior Research Scientist at HP Labs, and as an Acting Faculty member at Stanford University. She joined the Department of Computer Science at the University of Virginia as Associate Professor in 2005. She was Program Chair of the International Conference on Machine Learning and has served on numerous data mining and machine learning program committees. She also serves on the Editorial Board of IEEE Transactions on Knowledge and Data Engineering, IEEE Intelligent Systems, and the Machine Learning journal. She is the author of over twenty refereed articles, book chapters, and books.

Research

ishra's interests are in the design and analysis of algorithms for unearthing patterns in massively large, dynamic datasets. Organizations now maintain large quantities of personal information. Consequently, there is a growing need to find ways to keep this confidential information private. The need for privacy directly competes with the need to use this data for the discovery of patterns. Mishra investigates clustering algorithms, including clustering data streams, sublinear clustering, the design of new clustering objectives, identifying cluster descriptions, and graph clustering. On the other hand, because so much data now contains private information, she also seeks provably privacy-preserving algorithms that strike a fine balance between simultaneously enabling the discovery of large-scale statistical patterns, while disabling the recovery of private information. She investigates several kinds of privacy-preserving techniques, including input perturbation, auditing algorithms and secure computation.

Selected Publications

Clustering Data Streams: Theory and Practice, Sudipto Guha, Adam Meyerson, Nina Mishra, Rajeev Motwani, and Liadan O'Callaghan, IEEE Transactions on Knowledge and Data Engineering, Special issue on Online Analysis and Querying of Continuous Data Streams, Volume 15, Number 3, 2003, pp. 515-528.

A New Conceptual Clustering Framework, Nina Mishra, Dana Ron, and Ram Swaminathan, Machine Learning journal, Kluwer Academic Publishers, Volume 56, Number 1-3, 2004, pp. 115-151.

Secure Computation of the kth Ranked Element, Gagan Aggarwal, Nina Mishra, and Benny Pinkas, Advances in Cryptology - EUROCRYPT: International Conference on the Theory and Applications of Cryptographic Techniques, Interlaken, Switzerland, May 2004, pp. 40-55.

Simulatable Auditing, Krishnaram Kenthapadi, Nina Mishra, and Kobbi Nissim, ACM Symposium on Principles of Database Systems, Baltimore, Maryland, June 2005, pp. 118-127.

Privacy via Pseudorandom Sketches, Nina Mishra, and Mark Sandler, ACM Symposium on Principles of Database Systems, 2006, Chicago, IL, 2006.

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