Padhraic Smyth

Chancellor's Professor, Computer Science
Donald Bren School of Information and Computer Sciences

B.E., National University of Ireland, Electronic Engineering
PhD, Electrical Engineering, Caltech

Phone: (949) 824-2558
Fax: (949) 824-4056

University of California, Irvine
ICS 414E
Mail Code: 3425
Irvine, CA 92697
Research Interests
Machine learning, artificial intelligence, applied statistics, pattern recognition
Academic Distinctions
ACM Fellow
AAAI Fellow
Research Abstract
Data sets with millions of records and thousands of fields are increasingly common in business, medicine, engineering, and the sciences. The problem of extracting useful information from such data sets is an important practical problem. Research on this topic focuses on key questions such as how can one build useful descriptive models which are both accurate and understandable. The fields of applied statistics, pattern recognition, machine learning, information theory, and artificial intelligence, are all of relevance to this endeavor. Probabilistic and statistical methods, in particular, are central to our research, providing both a sound theoretical basis and a practical framework for developing useful data analysis algorithms.
Available Technologies
Predictive profiles for transaction data using finite mixture models. I. V. Cadez, P. Smyth, E. Ip, and H. Mannila, Technical Report UCI-ICS 01-67, December 2001.
Classification of disorders of anemia on the basis of mixture model parameters. C. E. McLaren, I. V. Cadez, P. Smyth, and G. J. McLachlan, Technical Report UCI-ICS 01-56, November 2001.
Hidden Markov models for endpoint detection in plasma etch processes. X. Ge and P. Smyth, Technical Report UCI-ICS 01-54, September 2001.
The distribution of loop lengths in graphical models for turbo decoding.
X. Ge, D. Eppstein, and P. Smyth. IEEE Transactions on Information Theory, vol. 47, no. 6, pp. 2549--2553, Sep 2001.
Data mining at the interface of computer science and statistics. P. Smyth, revised version to appear as an invited chapter in Data Mining for Scientific and Engineering Applications , in press.
Professional Societies
American Statistical Association
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