Chao Yan, PhD, MS, joined as Research Instructor in the Department of Biomedical Informatics in Feb. 2025. Prior to his primary faculty appointment, Dr. Yan completed a postdoctoral fellowship in the Department of Biomedical Informatics, Vanderbilt University Medical Center. Dr. Yan’s research focuses on synthetic health data generation and evaluation, predictive modeling, differential medical AI outcomes across groups, and privacy protection in health data use.
He received his PhD in Computer Science from Vanderbilt University in 2022. Dr. Yan has received multiple research honors, including one of the best article on clinical research informatics published in 2020, which was included in the 2021 International Medical Informatics Association Yearbook, and the distinguished paper award in 2019 AMIA Annual Symposium. He has been invited to give multiple presentations on his research, including the JAMIA Journal Club Webinar. Dr. Yan was a member of the JAMIA Student Editorial Board.
ORCiD: - https://orcid.org/0000-0002-6991-2687
Scopus Author ID: 57189348887
https://scholar.google.com/citations?hl=en&user=UCJrWSMAAAAJ
Dr. Mohammed Al-Garadi is a Research Assistant Professor in the Department of Biomedical Informatics at Vanderbilt University Medical Center. He previously worked as a Postdoctoral Researcher at Emory University, focusing on natural language processing (NLP), machine learning (ML), deep learning and large language models (LLMs) for healthcare applications. His research focuses on extracting insights from unstructured healthcare data, particularly unstructured notes, using NLP and machine learning techniques. By developing modules and pipelines, he has created systems to efficiently process diverse healthcare data streams. He worked on NIH and CDC grants involving the application of NLP and machine learning to analyze large-scale clinical narratives and public health data.
To date, Dr. Al-Garadi has authored and co-authored over 50 papers in high-impact scientific journals. Currently, Dr. Al-Garadi is exploring the potential of NLP, ML, and LLMs on unstructured EHR clinical notes for various healthcare applications. These include extracting, predicting, and detecting causes of death, postoperative infections, COPD exacerbations, kidney disease, peripheral artery disease, and tele-dermatology conditions and outcomes. He is working on projects supported by the NIH, Department of Veterans Affairs, and FDA.