Research

My research explores how AI can create truly personalized health interventions that adapt to individual needs, communication styles, and motivations. I focus on:

• Behavioral change through conversational AI, particularly for mental health support
• Real-time wellbeing measurement using innovative data collection methods
• Reducing health disparities through accessible, personalized digital interventions
• Understanding how personality and communication preferences affect intervention effectiveness

Academic Positions:

• Visiting Researcher, University of Cambridge Behavioral Health Lab
• Reviewer, The New England Journal of Medicine Catalyst (AI/ML applications)
• MRes Cambridge, PhD candidate (on leave)
• MBA Duke University (Klopman Scholar)

Selected Publications:

• Improving Weight Loss Adherence: A Machine Learning Approach to Personalized Chatbot Interventions
• Using Qualitative Chatbot Messaging Content to Understand Anxiety and Hope Causation During Covid-19
• Using Chatbots to Measure Wellbeing During Covid-19
• SMS Text Agents with Healthy Food Delivery to Improve Healthy Eating

Research Impact

The central finding of my work: personalization must go beyond surface-level customization. When AI systems understand how someone prefers to receive support—their motivational triggers, communication style, and behavioral patterns—engagement transforms. Our studies demonstrated that this deep personalization improved health behavior adherence by up to 85% compared to standard digital interventions.

This research revealed an opportunity to scale personalized health support in ways previously impossible. Current work applies these insights to complex healthcare challenges, particularly supporting aging populations and their families through AI-powered care coordination and wellbeing tools.