Machine Learning Approaches for Annotating Biological Data

Wednesday, October 9th, 2013 
2:00pm
Research Hall, Room 163

Dr. Huzefa Rangwala
Department of Computer Science, George Mason University

Abstract

Biological systems are complex and not completely understood. New generation of high-throughput            (“Big Data”) technologies capture large volumes of complex, multi-modal data associated with these systems. Scientific discovery and advancement requires extracting useful information from these datasets, which presents unique and challenging computing problems.  Complexity within biological data arises due to heterogeneity, incompleteness, missing information, noisy nature and inter-dependencies between the input and output domains.

In this talk, I will provide an overview of my contributions related to the development of accurate and efficient mining approaches for annotating these biological datasets. I will present a multi-task learning approach that seeks to leverage the hierarchical structure present within multiple biological archives for classification. I will also describe an approach for modeling of sequential data.  I will provide a highlight of how these developed approaches are integrated within computational pipelines to solve biological problems as they relate to the fields of metagenomics (or community genomics), protein function prediction and drug discovery.

Speaker Bio

Huzefa Rangwala is an Assistant Professor at the department of Computer Science & Engineering, George Mason University. He holds an affiliate appointment with the Bioengineering Department and the School of Systems Biology, George Mason University. He received his Ph.D. in Computer Science from the University of Minnesota in the year 2008. His research interests include machine learning, bioinformatics and high performance computing. He is the recipient of the NSF Early Faculty Career Award in 2013, the 2013 Volgenau Outstanding Teaching Faculty Award, 2012 Computer Science Department Outstanding Teaching Faculty Award and 2011 Computer Science Department Outstanding Junior Researcher Award. He is Mason's 2014 SCHEV Outstanding Faculty Award, Rising Star nominee. His research is funded by NSF, NIH, DARPA, USDA and nVidia Corporation.