Sebastian Pölsterl

Post-Doctoral Researcher

Artificial Intelligence in Medical Imaging

About Me

I’m a researcher at the lab for Artificial Intelligence in Medical Imaging working on machine learning for biomedical applications. My research interests are time-to-event analysis (survival analysis) and using deep learning techniques to learn from non-Euclidean data such as graphs. Previously, I worked at The Institute of Cancer Research, London and was among the winners of the Prostate Cancer DREAM challenge. I’m the author of scikit-survival, a machine learning library for survival analysis built on top of scikit-learn.


  • Time-to-event analysis
  • Non-Euclidean data
  • High-dimensional 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 0.13 Released

Today, I released version 0.13.0 of scikit-survival. Most notably, this release adds sksurv.metrics.brier_score and sksurv.metrics.integrated_brier_score, an updated PEP 517/518 compatible build system, and support for scikit-learn 0.23.

For a full list of changes in scikit-survival 0.13.0, please see the release notes.

Pre-built conda packages are available for Linux, macOS, and Windows via

 conda install -c sebp scikit-survival

Alternatively, scikit-survival can be installed from source following these instructions.

Survival Analysis for Deep Learning Tutorial for TensorFlow 2

A while back, I posted the Survival Analysis for Deep Learning tutorial. This tutorial was written for TensorFlow 1 using the tf.estimators API. The changes between version 1 and the current TensorFlow 2 are quite significant, which is why the code does not run when using a recent TensorFlow version. Therefore, I created a new version of the tutorial that is compatible with TensorFlow 2. The text is basically identical, but the training and evaluation procedure changed.

The complete notebook is available on GitHub, or you can run it directly using Google Colaboratory.

scikit-survival 0.12 Released

Version 0.12 of scikit-survival adds support for scikit-learn 0.22 and Python 3.8 and comes with two noticeable improvements:

  1. sklearn.pipeline.Pipeline will now be automatically patched to add support for predict_cumulative_hazard_function and predict_survival_function if the underlying estimator supports it (see first example ).
  2. The regularization strength of the ridge penalty in sksurv.linear_model.CoxPHSurvivalAnalysis can now be set per feature (see second example ).

For a full list of changes in scikit-survival 0.12, please see the release notes.

scikit-survival 0.11 featuring Random Survival Forests released

Today, I released a new version of scikit-survival which includes an implementation of Random Survival Forests. As it’s popular counterparts for classification and regression, a Random Survival Forest is an ensemble of tree-based learners. A Random Survival Forest ensures that individual trees are de-correlated by 1) building each tree on a different bootstrap sample of the original training data, and 2) at each node, only evaluate the split criterion for a randomly selected subset of features and thresholds. Predictions are formed by aggregating predictions of individual trees in the ensemble.

For a full list of changes in scikit-survival 0.11, please see the release notes.

scikit-survival 0.10 released

This release of scikit-survival adds two features that are standard in most software for survival analysis, but were missing so far:

  1. CoxPHSurvivalAnalysis now has a ties parameter that allows you to choose between Breslow’s and Efron’s likelihood for handling tied event times. Previously, only Breslow’s likelihood was implemented and it remains the default. If you have many tied event times in your data, you can now select Efron’s likelihood with ties="efron" to get better estimates of the model’s coefficients.
  2. A compare_survival function has been added. It can be used to assess whether survival functions across 2 or more groups differ.


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 …