Oliver Hennhöfer

Oliver Hennhöfer

Research staff at Hochschule Karlsruhe working on reliable machine learning systems.

Intelligent Systems Research Group/Karlsruhe, Germany

I work on statistical methods and software for machine learning systems that need to stay reliable under uncertainty. My current focus is conformal inference for anomaly detection, uncertainty quantification, and online false discovery rate control.

I am especially interested in settings where data are scarce, noisy, non-stationary, or arrive sequentially. In parallel to the theory, I build Python tools that make these methods easier to test, reuse, and compare.

Interests

Anomaly Detection/Conformal Inference/Uncertainty Quantification/False Discovery Rate Control/Sequential Testing/Research Software

Projects

  • nonconform

    Conformal anomaly detection and change-point detection for turning anomaly scores into p-values and FDR-controlled decisions.

    Python, conformal inference, anomaly detection

    GitHubDocsPyPI

  • online-fdr

    Sequential multiple-testing procedures for online false discovery rate and family-wise error rate control.

    Python, online FDR, sequential testing

    GitHubDocs

  • oddball

    A lightweight collection of anomaly detection datasets for reproducible experiments.

    Python, datasets, benchmarks

    GitHub

  • aberrant

    Online anomaly detection algorithms for streaming settings.

    Python, streaming, anomaly detection

    GitHub

Selected Publications

  • Between Resolution Collapse and Variance Inflation: Weighted Conformal Anomaly Detection in Low-Data Regimes

    Oliver Hennhöfer, Christine Preisach

    Preprint, arXiv:2603.23205

    2026

    arXiv

  • Interdisciplinary Harmonies: A Story-Driven Course on AI and Music to Increase Interest in Computer Science

    Kai Marquardt, Qiongdan Shang, Oliver Hennhöfer, Lucia Happe

    ECSEE 2025, pp. 145-153

    2025

    DOI

  • Leave-One-Out-, Bootstrap- and Cross-Conformal Anomaly Detectors

    Oliver Hennhöfer, Christine Preisach

    IEEE ICKG 2024, pp. 110-119

    2024

    DOIarXiv

Writing