Biomedical Informatics is an interdisciplinary field, where the central theme is to explore the effective uses of data, information, and knowledge for scientific inquiry, problem solving, and decision making, motived by efforts to import human health. I have a diverse yet strong multi-disciplinary background in data integration and harmonization, artificial intelligence and machine learning (AI/ML), natural language processing (NLP), ontology and semantic web technology, and software engineering for developing health informatics tools and systems. Nevertheless, my expertise and background serve an overarching theme: data science with heterogeneous data, information and knowledge resources. My research areas can be divided into two logical sections under this overarching theme: (1) data-driven medicine—applications of informatics techniques, including machine learning methods in medicine on solving big data problems; and (2) development of novel informatics methods, tools, and systems to support clinical and clinical research activities such as tools for generating real-world evidence, data integration, cohort discovery, and clinical trial design. I have a track record of not only building clinical data infrastructure (e.g., the OneFlorida+ Data Trust), but also obtaining extramural funding as PI/MPIs with over 15 R01-level grants and leading research projects using real-world data (RWD) from diverse data sources. Further, I have a record of collaboration, as a team scientist, with a diverse range of researchers across departments and colleges as well as nationally, evident from my funding profile as co-investigators.
Ph.D. - University of Arkansas for Medical Sciences, Little Rock, AR 2010
