The use of AI is becoming increasingly widespread, offering new opportunities to address pressing challenges in healthcare, including alleviating physician burnout, supporting patient caregivers, and improving our understanding of complex diagnoses. Our research lab is focused on leveraging these AI advances to tackle real-world problems at the intersection of technology and health, with a particular emphasis on mental health and elder care, areas highlighted by our students as key points of interest.
Our work proceeds along two complementary tracks. The first track applies LLMs and statistical modeling to improve predictive insights, helping us identify key predictors and prevalence patterns of mental health and elder care challenges. The second track focuses on designing user-centered systems that integrate AI-driven insights into clinical workflows. A central part of this effort is capturing physicians’ mental models and standard practices, ensuring that any AI support we develop aligns closely with expert expectations and complements existing clinical decision-making.
GitHub Repository: <to be updated>
Skills and Expertise Involved
- Key areas of work include:
- Applying machine learning and statistical models, including LLMs, for prediction and pattern discovery
- Designing user-centered studies and AI systems that align with clinical workflows
- Working with electronic health records and clinical data
- Investigating factors that influence health outcomes and the prevalence of key clinical and behavioral measures
- Core competencies required are:
- Programming in Python or R
- Data wrangling and cleaning large, noisy datasets
- Statistical modeling and evaluation of analytical methods
We are seeking students who are interested in gaining experience in these areas. Opportunities may be offered on either a volunteer basis or through paid positions, depending on project needs and available resources.