Research

My research program uses clinical bioinformatics and technology-driven methods to improve care for people with Parkinson's disease.

Core Areas

• Fall risk prediction in Parkinson's disease using longitudinal cohorts and clinically grounded modeling.
• Clinical bioinformatics and natural language processing to extract meaningful outcomes from electronic health records.
• Technology-enabled and patient-centered outcome measurement in movement disorders.
• Clinical evaluation of AI tools under real-world neurological decision constraints.

Current Projects

• Longitudinal analysis of fall frequency, risk factors, and outcomes in Parkinson's disease
• Natural language processing for automated extraction of falls and progression markers from electronic health records
• Translation of technology-enabled outcomes into practical clinical and trial workflows

Training and Support

This work is supported by NIH T32 training in Neuroengineering and Medicine, alongside graduate training in Translational Research with a concentration in bioinformatics.