Seminar - September 9, 2010

Bio:

Dr. Livingston’s research interests are machine learning applications in bioinformatics as well as other scientific domains, and he currently is an Assistant Research Professor at the University of Texas Brownsville and Texas Southmost College. He obtained his Ph.D. in Computer Science from the University of Pittsburgh in 2002. His thesis research investigated the use of machine learning to learn rules for crystallizing proteins. Dr. Livingston has also applied machine learning to several other biological or medical domains, including microarray gene expression analysis, proteomics, predicting the outcome of pneumonia patients, and physical therapy.

Dr. Livingston has published 14 peer-reviewed journal and conference papers, and he has served as a Guest Editor for the EURASIP Journal on Bioinformatics and Systems Biology. Dr. Livingston chaired a Special Session on Machine Learning and Data Mining at the 3rd Indian International Conference on Artificial Intelligence (IICAI-07), and he has served on the program committees for several international conferences and has been a reviewer for several journals and conferences. Additionally, he has given one conference tutorial on data mining applications and he has given a seminar on computational technology in genomics.

Abstract:

Machine learning is one of the major successes of artificial intelligence, and it has been used to make significant discoveries in numerous disciplines. A major area of application for machine learning is in bioinformatics, where machine learning is used to extract useful patterns from the vast amounts of information produced by biologists. This talk will briefly review machine learning and then review projects performed by members of my Machine Learning and Knowledge Discovery Group: predicting protein-protein interactions and protein function from only sequence information, inferring rules for crystallizing proteins, inferring rules for predicting the outcome of pneumonia patients given clinical data, and gene expression microarray analysis.