Muhammad (Tuan) Amith joined SBMI in September 2019 after earning his PhD in Biomedical Informatics from University of Texas Health Science Center at Houston. He holds bachelors and masters in Computer Science and Software Engineering from the University of Houston. Previously, he was a predoctoral research fellow with the UTHealth Innovation in Cancer Prevention Research for the Cancer Prevention Research Institute of Texas. Before graduate school, he worked over a decade as an engineer at several start-ups in various industries.
His research straddles between health consumer ontology-based AI and human computer interaction. It encompasses areas such as natural language processing/machine learning, public health, mHealth, network analysis, affective computing, and intelligent agent architectures. His dissertation focused on the application of ontology-based technologies for automating speech counseling of HPV vaccine and impacting health beliefs toward the vaccine. One of his current research activities involves semantic network approaches in examining vaccine adverse events in clinical notes. Concurrently, he continues to explore the domain of applied health consumer ontologies for intelligent agents and tools for physical environments. His endeavors have included collaboration with clinicians, public health researchers, and researchers from other disciplines.
Tuan is a member of Association of Computing and Machinery (ACM) and the American Medical Informatics Association (AMIA), and has served as a reviewer for journals and conferences on medical informatics and semantic web. He has published over 30 peer-reviewed papers and has received a best of mHealth distinction for the 2012 AMIA Annual Symposium for a conference paper.
"There is an interest in fusing statistical-based AI (i.e. machine learning) with symbolic-based AI, like ontologies. My research focused on the latter, so it will be exciting what new opportunities this will bring for AI in health care research".