Tae-Hyuk (Ted) Ahn, PhD
Tae-Hyuk (Ted) Ahn, PhD
Department of Computer Science
Program of Bioinformatics and Computational Biology
Saint Louis University
Tae-Hyuk (Ted) Ahn, PhD, is an Associate Professor in the Department of Computer Science at Saint Louis University (SLU). He is also a core faculty member in the graduate program of Bioinformatics and Computational Biology. His research interests include bioinformatics, biomedical informatics, high-performance computing, big data analytics, and machine & deep learning. Before joining SLU in 2015, he worked at Oak Ridge National Laboratory as a postdoctoral researcher. He received the Ph.D. degree in Computer Science from Virginia Tech in 2012, the M.S. degree in Electrical and Computer Engineering from Northwestern University in 2007, and the B.S. degree in Electrical Engineering from Yonsei University in 2000. From 2000 to 2004, he worked in the industry at Samsung SDS in South Korea.
Advanced Computational Techniques for Real-Time Biological Sequence Analyses
The exponential advances in sequencing technologies and informatics tools for generating and processing large biological data sets are promoting a paradigm shift in the way we approach in bioinformatics and biomedical problems. The vast amount of resulting sequencing data can help the diagnosis and precision medicine but poses a challenge: accurately characterizing a sample in a computationally feasible fashion, despite specimen diversity. In this talk, I will describe how to solve bio- and biomedical informatics research problems using advanced computational techniques for aiming real-time biological sequence analyses. First, I will present metagenomic sequence analysis by classifying metagenomic samples using microbiota information and machine learning techniques to identify and predict disease samples precisely. Second, I will introduce immune cell sequencing analysis. Sequencing T-cell receptors (TCR) can provide us with a robust resource for understanding whether an individual has been infected with a pathogen after the infection occurred. Even after the infection is eliminated, pathogen-specific immune cells and their receptor sequences are present at higher frequencies than prior to infection, and their increase in frequency prevents secondary infections. We developed a scalable deep neural network model to advance our capabilities of identifying infections from pathogen-specific TCR sequences in circulation over time with exceptional sensitivity.