Sebastian Pölsterl

AI Researcher


About Me

I’m an AI researcher in the computational pathology, Oncology R&D team at AstraZeneca and an open-source enthusiast working on machine learning for biomedical applications. My research interests are time-to-event analysis (survival analysis) and causal inference. Previously, I worked at the lab for Artificial Intelligence in Medical Imaging at the Technical University of Munich and The Institute of Cancer Research, London. I’m the author of scikit-survival, a machine learning library for survival analysis built on top of scikit-learn.


  • Time-to-event analysis
  • Causal inference
  • Heterogenous data
  • Biomedical applications
  • Deep learning


  • PhD in Computer Science, 2016

    Technische Universität München

  • MSc in Bioinformatics, 2011

    Ludwig-Maximilians-Universität & Technische Universität München

  • BSc in Bioinformatics, 2008

    Ludwig-Maximilians-Universität & Technische Universität München

Recent Posts

scikit-survival 0.22.0 released

I am pleased to announce the release of scikit-survival 0.22.0. The highlights for this release include

scikit-survival 0.21.0 released

Today marks the release of scikit-survival 0.21.0. This release features some exciting new features and significant performance improvements:

  • Pointwise confidence intervals for the Kaplan-Meier estimator.
  • Early stopping in GradientBoostingSurvivalAnalysis.
  • Improved performance of fitting SurvivalTree and RandomSurvivalForest.
  • Reduced memory footprint of concordance_index_censored.

scikit-survival 0.18.0 released

I’m pleased to announce the release of scikit-survival 0.18.0, which adds support for scikit-learn 1.1.

In addition, this release adds the return_array argument to all models providing predict_survival_function and predict_cumulative_hazard_function. That means you can now choose, whether you want to have the survival (cumulative hazard function) automatically evaluated at the unique event times. This is particular useful for plotting. Previously, you would have to evaluate each survival function before plotting:

Using VS Code and Podman to Develop SYCL Applications With DPC++'s CUDA Backend

I recently wanted to create a development container for VS Code to develop applications using SYCL based on the CUDA backend of the oneAPI DPC++ (Data Parallel C++) compiler. As I’m running Fedora, it seemed natural to use Podman’s rootless containers instead of Docker for this. This turned out to be more challenging than expected, so I’m going to summarize my setup in this post. I’m using Fedora Linux 36 with Podman version 4.1.0.

scikit-survival 0.17.2 released

I’m pleased to announce the release of scikit-survival 0.17.2. This release fixes several small issues with packaging scikit-survival and the documentation. For a full list of changes in scikit-survival 0.17.2, please see the release notes.

Most notably, binary wheels are now available for Linux, Windows, and macOS (Intel). This has been possible thanks to the cibuildwheel build tool, which makes it incredible easy to use GitHub Actions for building those wheels for multiple versions of Python. Therefore, you can now use pip without building everything from source by simply running

pip install scikit-survival

As before, pre-built conda packages are available too, by running

 conda install -c sebp scikit-survival


scikit-survival: machine learning for time-to-event analysis

scikit-survival is a Python module for survival analysis built on top of scikit-learn. It allows doing survival analysis while …