Welcome to Liadan O'Callaghan's page. I recently graduated from the Computer Science Ph.D. program. My advisor is Rajeev Motwani. Here is a copy of my thesis.
I mainly work on approximation algorithms for datamining problems, especially clustering problems. I am particularly interested in data stream algorithms. I have also done a little work related to game theory.
Some papers I have co-authored:

Clustering Data Streams, by Sudipto Guha, Nina Mishra, Rajeev Motwani, and Liadan O'Callaghan, which appeared in FOCS 2000.
Streaming-Data Algorithms for High-Quality Clustering, by Liadan O'Callaghan, Nina Mishra, Adam Meyerson, Sudipto Guha, and Rajeev Motwani, which appeared in ICDE 2002.
Clustering Data Streams: Theory and Practice, by Sudipto Guha, Adam Meyerson, Nina Mishra, Rajeev Motwani, and Liadan O'Callaghan, a journal version of the above two papers, which appeared in a TKDE special issue on clustering, vol. 15, 2003.
Representing Graph Metrics with Fewest Edges , by Tomas Feder, Adam Meyerson, Rajeev Motwani, Liadan O'Callaghan, and Rina Panigrahy, which appeared in STACS 2003.
Computing Shortest Paths with Uncertainty , by Tomas Feder, Rajeev Motwani, Liadan O'Callaghan, Chris Olston, and Rina Panigrahy, which appeared in STACS 2003.
Towards the Visualization of Overlapping Sets , by Xavier Boyen, Nina Mishra, and Liadan O'Callaghan, presented at DIMACS Computational Geometry Workshop, November, 2002.
Maintaining Variance and k-Medians over Data Stream Windows, by Brian Babcock, Mayur Datar, Rajeev Motwani, and Liadan O'Callaghan, which appeared in PODS 2003.
Better Streaming Algorithms for Clustering Problems, by Moses Charikar, Liadan O'Callaghan, and Rina Panigrahy, which appeared in STOC 2003.

US patent 6684177, issued January 27, 2004: "Computer Implemented Scalable, Incremental, And Parallel Clustering Based On Weighted Divide And Conquer" with Sudipto Guha, Nina Mishra, and Rajeev Motwani.
C++ code for a local search clustering algorithm akin to the one detailed in "Streaming-Data Algorithms for High-Quality Clustering."

The code, as written, computes a clustering of a given set of points, and can easily be used as the clustering subroutine of the divide-and-conquer algorithm given in the paper.

This code is provided for examination by anyone who may be interested in implementing local search clustering algorithms; it is not intended to be run, but to be used as a starting point for anyone who wants to write his or her own implementation. In particular: We, the authors, do not provide a license for the use of this code. If you use this code instead of writing your own, we don't want to know :-). We do not claim that it works, and will not maintain it or provide support to those who use it. Use of this code is at the risk of the user. The code is copyrighted 2001 by Adam Meyerson and Liadan O'Callaghan.

Useful links:
Digital Libraries (Access restricted): IEEE Electronic Library ACM Digital Library