About me
I’m a Postdoctoral Scientist in Data Science and Digital Health R&D at Johnson and Johnson Innovative Medicine. I completed my PhD (2025) and MS (2020) in the Intelligent Systems Program at the University of Pittsburgh. My research interests include natural language processing, machine learning, knowledge representation, knowledge graphs and applications of these topics in biomedicine.
My PhD research applied knowledge graphs, ontologies, literature-based discovery, embedding approaches, and large language models to generate mechanistic hypotheses for pharmacokinetic natural product-drug interactions in the Boyce lab and at the NaPDI Center. Previously, I worked as a research intern at the National Center for Biotechnology Information (NCBI) at the National Institutes of Health (NIH) in biomedical natural language processing with Dr. Zhiyong Lu (2023) and a research assistant at the Center for Research on Media, Technology and Health, University of Pittsburgh (2018-2020).
Technical Skills: Python, R, PostgreSQL, Neo4j, Scikit-learn, Keras, Pandas, Git, Linux environment, NLTK, Networkx, RDF, OHDSI toolkit, GPT
Strengths and Interests: Machine Learning, Natural Language Processing, OMOP Common Data Model, ETL of Electronic Health Records data, Knowledge Graphs, Knowledge Representation, Biomedical Ontologies, Literature-based discovery, Large Language Models, Bayesian Networks, Semantic Web, Data Mining
Research Projects
Knowledge Graph for Natural Product-Drug Interactions: Heterogeneous knowledge graph for natural product-drug interactions combining biomedical ontologies, drug databases, and scientific literature to generate mechanistic hypotheses.
OMOP vocabulary for natural products: Standardized vocabulary to include botanical natural products, phytoconstituents, synonyms, and spelling variations for pharmacovigilance in adverse event reporting systems
Bayesian Network Models with Decision Tree Analysis for Management of Childhood Malaria: Master’s thesis with Dr. Shyam Visweswaran, Dr. Gerald Douglas and Dr. Gregory Cooper at the Department of Biomedical Informatics (DBMI), University of Pittsburgh (2018-2020).
Discovery of novel associations to prevent the onset of Alzheimer’s disease using electronic health records: University of Pittsburgh Momentum Teaming Grant to discover associations to prevent the onset of Alzheimer’s disease using machine learning, knowledge graphs and large-scale electronic health records (2020-2021).
Machine Learning Classifiers for Twitter Surveillance of Vaping: I worked as a research assistant at the Center for Research on Media, Technology and Health (MTH) at the School of Medicine, University of Pittsburgh, where we focused on public health research on social media related to vaping, nicotine addiction, anti-vaccination, bot detection and more (2018-2020).