Machine learning methods for annotating and extending big, heterogeneous genomic data

Thursday, October 27, 2016 -
1:30pm to 2:30pm
Biotechnology Center Auditorium, 425 Henry Mall

Speaker Name: 

William S. Noble

Speaker Institution: 

Prof. Dept. of Genomic Sciences; Computer Science & Engineering, Univ. of Washington




Research Interests:
Our research group develops and applies computational techniques for modeling and understanding biological processes at the molecular level. Our research emphasizes the application of statistical and machine learning techniques, such as hidden Markov models and support vector machines. We apply these techniques to various types of biological data, including protein and DNA sequences, data from high-throughput genomic assays such as ChIP-seq and Hi-C, and tandem mass spectrometry. We are currently developing methods for analyzing shotgun proteomics data, for characterizing protein function, structure and interactions, and for understanding the structure and regulatory influence of chromatin.

Brief Bio:
William Stafford Noble (formerly William Noble Grundy) was raised in Naperville, IL, and graduated from Stanford University in 1991 with a degree in Symbolic Systems. Between undergraduate and graduate school, he worked in the speech group at SRI International in Menlo Park, CA, and at Entropic Research Laboratory in Palo Alto, CA. He also spent two years teaching high school math, physics and English literature with the US Peace Corps in Lesotho, Africa. In 1994, he entered graduate school at the University of California, San Diego, where he studied with Charles Elkan. He received the Ph.D. in computer science and cognitive science in 1998. He then spent one year as a Sloan/DOE Postdoctoral Fellow with David Haussler at the University of California, Santa Cruz. From 1999 until 2002, Noble was an Assistant Professor in the Department of Computer Science at Columbia University, with a joint appointment at the Columbia Genome Center. In 2002, he joined the faculty of the Department of Genome Sciences at the University of Washington, where he has adjunct appointments in the Department of Computer Science and Engineering, the Department of Medicine, and the Department of Biomedical Informatics and Medical Education. His research group develops and applies statistical and machine learning techniques for modeling and understanding biological processes at the molecular level. Noble is the recipient of an NSF CAREER award and is a Sloan Research Fellow, and is a former member of the Board of Directors of the International Society for Computational Biology. He is currently the Director of the UW Computational Molecular Biology Program and Co-Director of the UW Center for Nuclear Organization and Function.