Developing AI That Works for Healthcare

I am a Postdoctoral Research Fellow in the lab of Prof Kun-Hsing Yu in the Department of Biomedical Informatics at Harvard Medical School in Boston. Previously, I completed my PhD in Medical Engineering and Medical Physics in the Harvard-MIT Division of Health Sciences and Technology at MIT under the guidance of Prof Jayashree Kalpathy-Cramer. From 2023 to 2024, I was a Research Fellow in the Department of Data Science at Dana-Farber Cancer Institute in Boston. I also hold an MD from Heidelberg University Medical School and a BS in Physics with a minor in Computer Science from Kiel University in Germany.

My research focuses on developing AI methods that are clinically meaningful, robust, and aligned with real-world healthcare workflows. I am particularly interested in combining technical innovation with domain expertise so that models can support clinicians and improve patient outcomes.

Research Areas

I am currently building a research program around three connected themes:

1. Disease modeling from complex clinical and molecular data
   Building machine learning models that can represent biological and clinical heterogeneity in diseases such as cancer.
2. Interpretability and trust in medical AI
   Designing methods that help clinicians understand model behavior and identify when predictions are reliable.
3. Translation to healthcare practice
   Creating AI systems that are tailored to clinical needs, fit existing workflows, and can be evaluated for practical impact.

Detailed research pages are in development and will be added here soon.

For a full list of publications, please see my Google Scholar profile.

CV and Contact

You can download my current CV here: Download my CV.

I am always happy to connect about potential collaborations in medical AI, particularly in the oncology space.