What you will be working on?
I will be working on evaluating and extending foundation models for clinical prediction tasks using electronic health record (EHR) data from Stanford Medicine. Specifically, I will help develop data preprocessing pipelines, generate structured and embedding representations of patient trajectories, and benchmark how foundation models perform across few-shot and out-of-distribution settings. This project will also involve analyzing the generalizability of multimodal EHR models and identifying which modeling approaches best capture longitudinal clinical patterns.
What are you looking forward to the most in your experience as a WA Scholar?
I am most looking forward to learning from and collaborating with researchers who are shaping the future of biomedical data science. The Warren Alpert community provides a unique opportunity to engage with others who share a vision for translating complex data into meaningful healthcare insights. I am excited to contribute my experience working with clinical and molecular data while also gaining new perspectives that will help me refine how I approach using AI to accelerate therapeutic discovery and improve patient outcomes.
How do you see this shaping your research and ultimately, career?
This project will strengthen my understanding of how foundation models can learn from complex clinical data and be applied to broader areas of biomedical research. In my career, I hope to use these approaches to advance drug discovery by integrating patient-level insights with molecular and experimental data to better predict therapeutic outcomes and accelerate the development of effective treatments.
From your perspective, what does it mean to you to be a WA Scholar?
To me, being a WA Scholar means being part of a community that is committed to advancing human health through both innovation and empathy. It represents the responsibility to ensure that the computational tools we build serve diverse populations and address real clinical needs. It also reflects a belief that data science can be a bridge between understanding disease mechanisms and developing new therapies, empowering researchers to turn technical progress into tangible impact for patients.