Teaching

Materials for courses and pedagogic events.


Graduate Courses


Medical Image Analysis Lab

2024 with Shelley.

More details Changelog: using GitHub classroom for assignments; updated simpler starter code repository and organization. Course evaluation score remains high at 4.89. Representative student feedback: "I absolutely enjoyed this course. I think that Shelley and Amith did a fantastic job with this course. I highly appreciate how interactive the course was during the lecture portion and how available and helpful they were during the lab portion. During the projects, we had quite some freedom to experiment which I think is great for our learning process and the feedback/guidance from the TAs has been really great."

2023, with Shelley and Mike.

More details Changelog: project specific slack channels; video lectures along with in-class presentation; hospital clinic visit. Course evaluation score remains high at 4.92. Representative student feedback: "I got to improve my programming skills and the TA was extremely helpful and nice!"

2022, with Elias and Jayden.

More details The overall course evaluation score increased from 3.25 in 2021 before I was involved to 4.94 this year!

Biomedical Engineering Lab

2022 with Yannick.

More details This is a short practical rotation course for incoming Masters' students in Biomedical Engineering to get a quick overview of what we do in the Medical Image Analysis lab.

Symposia and Community Events


Organizer for Bern AI in Radiotherapy (BART) Symposium in March 2025.

More details Robert and I are co-organizers of BART, a one-day symposium for AI in Radiotherapy. We have three keynote speakers across radiation oncology, medical physics and engineering/AI. More to be updated here soon.

Organizer for Bern Interpretable AI (BIAS) Symposium in March 2023.

More details Yannick and I are co-organizers of BIAS, a one-day symposium for Interpretable AI, hosted at the Cupola room at the Uni Bern main building. We have an attendance of 140 researchers (70 online) and posters from 5 countries.

Educational Content


AI in Radiotherapy

More details This is designed for someone with a computer science background, who would like to familiarize themselves with radiation oncology topics so as to build useful tools in this application area.

BENDER

More details This is a series of fun educational videos to learn nuances of Deep Learning as applied to Medical Imaging. We have also created a [GitHub Repo](https://github.com/ubern-mia/bender) which includes supporting material, including code and references. I play the role of a new graduate student who learns the ropes.