🔭 Working on probabilistic models to predict how cancer spreads
👯 Interested in collaborating on datasets of lymphatic progression patterns in head & neck cancer
💬 Always happy to hear feedback on our interactive Lymphatic Progression eXplorer (LyProX)

I am a PostDoc in the medical physics research group of Prof. Jan Unkelbach at the University Zurich and the University Hospital Zurich.

In our main project, we try to model the risk for metastases in the lymph system of patients with squamous cell carcinomas in the head & neck region. You can read more on that in an excellent paper by a PostDoc in our group: Pouymayou et al. You can also check out our code for the lymph model, which is a python package containing the code to learn and compute this risk of lymphatic metastases using Bayesian networks (mentioned paper) and also - this is new - hidden Markov models (Ludwig et al).

Another project deals with optimal fractionation schemes. Fractionation is the splitting of a prescribed dose of radiation designed to kill cancer cells in a tumor into multiple sessions to allow the healthy parts of the body to recover better. Innovative technologies like the MR-LinAc at our institution enable us to tackle this problem with reinforcement learning

• probabilistic models
• interpretable machine learning methods
• statistical learning theory

and also (though not necessarily research-related)

• 🌌 (theoretical) astrophysics (I did my master in this group)
• web development
• open source

Writing
Coding
Dev
Software
Learning

📫 In case you want to reach me: [email protected]