KEYNOTE SPEAKER

Machine Learning Methods for Proteomic Analysis

Dr. Jaime G. Carbonell
Carnegie Mellon University, USA

Abstract

Many challenges in structural biology may be addressed by existing and emerging
machine learning approaches. These challenges include protein functional family/sub-family
classification, inferring secondary, tertiary and quaternary 3D structure from 1D sequence,
inferring within-species and cross-species (host-pathogen) protein-protein interactions. The
talk addresses machine leaning methods from simple classifiers to linked conditional
random fields and their role in-silico bio-informatics, including methods and empirical
results to date.

Biography of the Keynote Speaker

Dr. Jaime Carbonell is the Director of the Language Technologies Institute and Allen Newell Professor of Computer Science at Carnegie Mellon University. He received SB degrees in Physics and Mathematics from MIT, and MS and PhD degrees in Computer Science from Yale University. His current research includes machine learning, computational proteomics, data mining (primarily in healthcare and finance), text mining and machine translation. He recently invented Proactive Machine Learning, including its underlying decision-theoretic framework. He is also known for the Maximal Marginal Relevance principle in information retrieval, for derivational analogy in problem solving and for example-based machine translation and for machine learning in structural biology. Overall, he has published some 300 papers and books and supervised over 40 PhD dissertations. Dr. Carbonell has served on multiple governmental advisory committees such as the Human Genome Committee of the National Institutes of Health, the Oakridge National Laboratories Scientific Advisory Board, the National Institute of Standards and Technology Interactive Systems Scientific Advisory Board, and the German National Artificial Intelligence (DFKI) Scientific Advisory Board. He is also the chairman of Carnegie Speech Company, which produces intelligent language tutoring software.