I’m a PhD candidate in the Intelligent Systems Program at the University of Pittsburgh, where I previously got my Master’s degree. My research interests include natural language processing, machine learning, knowledge representation, knowledge graphs and applications of these topics for biomedical discoveries and improving healthcare. My PhD research applies knowledge graphs, ontologies, machine reading and embedding approaches to generate mechanistic hypotheses for pharmacokinetic natural product-drug interactions in the Boyce lab (primary advisor - Dr. Richard Boyce) and at the NaPDI Center.
In Summer 2023, 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. I am a member of the JAMIA Student Editorial Board (2022-2023). I am also a member of the American Medical Informatics Association (AMIA) and the AMIA student working group (STWG), where I contributed to the STWG newsletter as the co-editor (2021-2022).
Technical Skills: Python, R, PostgreSQL, Scikit-learn, Keras, Pandas, Git, Linux environment, NLTK, Networkx, RDF, Neo4j, OHDSI toolkit
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
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).