About Masoud Rouhizadeh
Masoud Rouhizadeh M.A., Prof.MS., M.S., Ph.D. is an Assistant Professor in the University of Florida College of Pharmacy, Department of Pharmaceutical Outcomes, under the AI in the Health Sciences Initiative.
The primary focus of Dr. Rouhizadeh’s research involves applying machine learning and natural language processing methods for identifying clinical concepts from unstructured text and converting them into structured data. Another major part of his research has been developing clinical ontologies and lexical resources, as well as computational models for identifying social and behavioral determinants of health.
Before joining the UF, Dr. Rouhizadeh was a Faculty Instructor at Biomedical Informatics and Data Science and the natural language processing lead at the Institute for Clinical and Translational Research at the Johns Hopkins University School of Medicine. Prior to JHU, he was a postdoctoral fellow at the University of Pennsylvania’s World Well-Being Project and then at the Penn Institute for Biomedical Informatics. He obtained his Master’s and Ph.D. in Computer Science and Engineering from Oregon Health and Science University and his Master’s in Human Language Technology from the University of Trento, Italy.
With a strong foundation in machine learning and natural language processing (NLP), Dr. Rouhizadeh has focused his research on developing large and scalable language analyses with applications to physical, mental, and public health. While much of NLP involves modeling language itself, he builds on such techniques to develop novel approaches to extract clinical concepts from unstructured free-text notes and better understand social and behavioral determinants of health by leveraging the unprecedented amount of data afforded from electronic health records. Another major part of his research has been developing clinical ontologies and lexical resources in various domains, as well as computational models for identifying signs of neurological disorders affecting children and the elderly.