Applying NLP and machine learning to health data — including sentiment analysis of vaccine discourse, predictive modeling, and detecting health misinformation.
Health informatics sits at the intersection of computing and public health, leveraging data-driven methods to improve outcomes, inform policy, and understand population-level trends. My research in this area applies modern NLP techniques to real-world health datasets.
One focus area is understanding public sentiment around vaccines — particularly COVID-19 vaccines — by analyzing social media data across geographic regions. This work helps public health communicators understand hesitancy patterns and tailor their messaging accordingly.
We are also exploring predictive models for health forum data, working to identify early indicators of disease exacerbation in patient communities discussing conditions like asthma, and developing privacy-preserving techniques for sensitive medical data.