Tuan Amith

Dr. Muhammad "Tuan" Amith, PhD is an Assistant Professor with the School of Health Information Sciences. He received his PhD in Biomedical Informatics from the McWilliams School of Biomedical Informatics (SBMI) at the University of Texas Health Science Center at Houston (UTHealth). He holds a B.S. and M.S in Computer Science and Software Engineering from the University of Houston. He previously served as an Assistant Professor at SMBI UTHealth, University of North Texas, and at the Department of Biostatistics and Data Science from the University of Texas Medical Branch.

Dr. Amith's core research focus is on methods for biomedical knowledge representation (e.g., ontologies, knowledge graphs, semantic web, linked data) to support artificial intelligence/automated planning, digital archiving and FAIRness, data analytics and decision support, human computer interaction and open-sourced tool development. His targeted health interests for his methodological focus ranges from cancer vaccines, occupational health and safety, clinical workflows and processes, mental health/counseling, AI safety, diabetes, mHealth, aerospace medicine, dental health, physiology, social determinants of health, food and nutrition, HIV and PreP, informed consent, and patient-provider communication. He is currently involved in AMIA's Consumer Informatics working group, and NIH U24 working group for social behavioral ontologies and terminologies.

Dr. Amith's past research has received several best paper distinctions from the American Medical Informatics Association (AMIA), MedInfo, and IEEE, and his dissertation was a finalist for the American Medical Informatics Association for best dissertation. He has served as Co-I and PI on past and current National Institutes of Health funded projects on applied health informatics research. In addition, Dr. Amith is a member of the editorial board for Nature's Scientific Reports (Digital Health), BMC Medical Informatics and Decision Making, and the Journal of Clinical Informatics.

For publications, see: Google Scholar - Tuan Amith

GitHub: ProfTuan