CSE Speaker Series – Dr. Joe Song

March 4, 2016 11:00-12:00

Cramer 221

New Mexico Institute of Technology

Socorro, NM


Title: Inference of Causality and Rewiring in Molecular Networks


Joe Song
Department of Computer Science

New Mexico State University


Abstract: Molecular networks that drive biological processes in most organisms are still elusive due to the complexity of life and obstacles in collecting data. Advances in experimentation has enabled measurements of hundreds of thousands of molecules in a given sample, on the way to making genomics the largest data source in the coming decade. This has created an urgent need for automated network inference algorithms and software. Responding to this challenge, we have established a novel statistical computing framework for network inference based on nonparametric functions. This framework has been theoretically proven for its optimality and empirically evaluated for its effectiveness. In contrast to parametric differential equation models, the power of nonparametric methods has emerged as the sample sizes of biological studies increase toward big data scale. Our innovative computational methods for biological network inference, rewiring, and evolution have been rigorously benchmarked for effectiveness over alternative approaches, and have enabled novel insights into cell cycle control, cerebellar development, yeast detoxification for advanced biofuels, and cancer development. I will introduce these methods developed by my lab to learn biological networks and their rewiring from observed data. These methods provide a new avenue to reveal fundamental changes in molecular mechanisms that underpin development, disease, ecological changes, and evolution.


Brief Biography


Joe Song received his Ph.D. in 2002 and M.S. in 1999, both in Electrical Engineering from the University of Washington at Seattle. He obtained his B.S. in Electrical Engineering from Beijing University of Posts and Telecommunications in 1992. He was Assistant Professor of Computer Science with Queens College and the Graduate Center of City University of New York from 2002 to 2005. He joined the Department of Computer Science at New Mexico State University as an Assistant Professor in 2005 and has been an Associate Professor since 2010. His research areas include statistical computing, quantitative biology, bioinformatics, and computer vision. His research has attracted funding from NSF, NIH, USDA, and Los Alamos National Lab. He has received Interdisciplinary Research Grant, Graduate Research Enhancement Grant, and Undergraduate Research Initiative Grant from New Mexico State University Office of the Vice President for Research. His current research projects in systems biology involve inference of large dynamic biological networks at the molecular level. He has collaborated with life scientists around the world to discover biology networks underlying biofuels, cancer, neuroscience, and microbiology. His research partners include scientists at Fred Hutchinson Cancer Research Center and USDA. In HPN-DREAM Breast Cancer Network Inference Challenges in 2013 in Toronto, Canada, the FunChisq method developed by his lab took the first place among 80 international teams and received the Best Performer award.