I am a healthcare data scientist and AI researcher specializing in machine learning, predictive modeling, and natural language processing for healthcare applications.

Currently, I am a Senior Data Scientist at the Healthcare Transformation Institute at the University of Pennsylvania’s Perelman School of Medicine. My main area of research at Penn is in medical ethics and health policy.

Working with an interdisciplinary team of physicians, health policy researchers, and data scientists, I develop machine learning models that use large-scale healthcare claims data to predict medical expenditures and investigate how alternative payment models could improve Medicare financing and patient equity.

I also work at the intersection of artificial intelligence, natural language processing, and clinical research. My research includes applying large language models to primary care encounters to understand how clinicians and patients discuss preventive healthcare, wellness, and health behaviors. This work combines AI-assisted annotation, clinical expertise, and systematic model evaluation.

Previously, I have worked on creating machine learning models to investigate vocal biomarkers associated with respiratory illness, healthcare operations and value-based care modeling, as well as natural language processing and behavioral analytics in enterprise technology.

Beyond my research, I am the Associate Director of The Erdős Institute, a career development program aimed at preparing graduate students and post-docs for data science positions in industry and entrepreneurship. In this role, I help create the direction of the institute, as well as oversee data science partnerships with corporate entities, and lead educational programs in deep learning and user experience research.

I earned my Ph.D. in Music (Theory, Cognition, and Perception) and an interdisciplinary specialization in Cognitive and Brain Sciences from The Ohio State University. My doctoral research combined statistical modeling, behavioral experiments, and emotion science to investigate how and why people perceive and experience emotions from music listening. My research resulted in an award for the best graduate student paper at an international conference and the graduate achievement award in my department. For my mentorship, I was awarded the Early-Career Mentor Award from the Society of Music Perception and Cognition.