Konstantin Kirchheim

When should a machine-learning system no longer be trusted?

I study the evaluation and monitoring of machine-learning systems under distribution shift. My research combines out-of-distribution detection, structured reasoning, and reproducible evaluation to identify situations in which learned models may behave unreliably.

Portrait of Konstantin Kirchheim

I am a PhD researcher in computer science at Otto von Guericke University Magdeburg and was previously a visiting graduate student at the University of Waterloo. My broader objective is to connect model-level evidence to defensible decisions about deploying machine learning in safety-critical systems.

Research program

Lines of work

Research overview

Distribution shift and monitoring

Methods for recognizing inputs and operating conditions beyond those under which a model can be expected to perform reliably.

Reasoning with structured knowledge

Neuro-symbolic methods that use logical and probabilistic knowledge to identify semantically inconsistent situations.

Evaluation and evidence

Software, benchmarks, and reproducibility tools for clarifying what empirical results establish about model reliability.

Research in practice

All projects

Open-source software

pytorch-ood

Evaluation infrastructure for out-of-distribution detection, with unified score semantics, tested implementations, datasets, pretrained models, and benchmark interfaces.

Project page · Documentation · GitHub

Teaching

Introduction to Machine Learning Safety

Course information and teaching materials are being prepared.

Teaching

Representative outputs

Selected research

Complete publication list
Improving Out-of-Distribution Detection with Markov Logic Networks (06 Jun. 2025)
Our paper Improving Out-of-Distribution Detection with Markov Logic Networks has been accepted at ICML. In it, we propose a probabilistic extension of Out-of-Distribution Detection with Logical Reasoning, as well as a simple algorithm to mine logical constraints for OOD detection …
Categories: Neuro-Symbolic
Tagged with: ICML · Neuro-Symbolic · Anomaly Detection
Thumbnail for Improving Out-of-Distribution Detection with Markov Logic Networks
Out-of-Distribution Detection with Logical Reasoning (04 Jan. 2024)
Our paper Out-of-Distribution Detection with Logical Reasoning has been accepted at WACV 2024. Abstract § Machine Learning models often only generalize reliably to samples from the training distribution. Consequentially, detecting when input data is out-of-distribution (OOD) is …
Categories: Anomaly Detection Neuro-Symbolic
Tagged with: WACV · Anomaly Detection · Neuro-Symbolic
Thumbnail for Out-of-Distribution Detection with Logical Reasoning
PyTorch-OOD: A library for Out-of-Distribution Detection based on PyTorch (13 Jul. 2022)
Our paper, PyTorch-OOD: A library for Out-of-Distribution Detection based on PyTorch, has been presented at the CVPR 2022 Workshops. You can find the most recent version of the Python source code on GitHub. The library has developed substantially since the original 2022 paper. …
Categories: Anomaly Detection
Tagged with: CVPR · Anomaly Detection
Thumbnail for PyTorch-OOD: A library for Out-of-Distribution Detection based on PyTorch

Application and exchange

Talks and public engagement

Media · Deutschlandfunk

Safe use of AI in railway operations

A conversation about distribution shift, reliable uncertainty estimates, and how learned components interact with established railway safety processes.

Listen to the interview