Plenary Lecture
Applications of Signal Processing and Machine Learning Techniques to Metagenomics
Professor Erchin Serpedin
Texas A&M University
USA
E-mail: eserpedin@tamu.edu
Abstract: Recent advances in high-throughput sequencing technologies open a new era of genomics studies, called metagenomics. Recently, metagenomics presented itself as the standard approach for characterizing the compositional and functional capacity of microbial communities by direct study of the genetic contents recovered from environmental samples without prior culturing. Although these advancements enable researchers to sequence bacterial populations at a reasonable budget, analyzing these massive metagenomic datasets present significant challenges. This talk presents novel computational tools, based on signal processing and machine learning theories, to enable the investigation of biological systems. The key problem addressed herein talk concerns the identification of the potential metagenomic biomarkers, which play a critical role in understanding the biological process under study and in developing possible therapies.
Brief Biography of the Speaker: Erchin Serpedin received the specialization degree in signal processing and transmission of information from Ecole Superieure D’Electricite (SUPELEC), Paris, France, in 1992, the M.Sc. degree from the Georgia Institute of Technology, Atlanta, in 1992, and the Ph.D. degree in electrical engineering from the University of Virginia, Charlottesville, in January 1999. He is currently a professor in the Department of Electrical and Computer Engineering at Texas AM University, College Station. He is the author of 3 research monographs, 1 textbook, 140 journal papers and 250 conference papers, and serves currently as associate editor for the IEEE Signal Processing Magazine and as editor-in-chief of EURASIP Journal on Bioinformatics and Systems Biology, an online journal edited by Springer-Nature. He served as associate editor for a dozen of journals such as IEEE Transactions on Signal Processing, IEEE Transactions on Communications, IEEE Transactions on Information Theory, Signal Processing (Elsevier), EURASIP Journal on Advances in Signal Processing, Physical Communication (Elsevier), and IEEE Signal Processing Letters. His research interests include signal processing, wireless communications, bioinformatics, and machine learning. He is an IEEE Fellow.