Our group's research focuses on developing new software tools to better analyze Big Data (principally, but not limited to, bio, health, and biomedical) using Artificial Intelligence (AI) and diverse computational techniques on High-Performance Computing (HPC) systems including supercomputers and cloud computing.
Bio- and Biomedical informatics research is an interdisciplinary field that develops methods and software tools for understanding biological and medical data. As an interdisciplinary field of science, it combines computer science, statistics, mathematics, and engineering to analyze and interpret biological, medical, and clinical data.
To extract knowledge from big data in bioinformatics, machine learning has been a widely used and successful methodology. Deep learning, a branch of machine learning, has recently emerged based on big data, the power of parallel and distributed computing, and sophisticated algorithms.
High-Performance Computing most generally refers to the practice of aggregating computing power in a way that delivers much higher performance than one could get out of a typical desktop computer or workstation in order to solve large problems in science, engineering, or business.