Karlsruhe Research Staff 2026

Reliable machine learning / research software

Oliver Hennhöfer

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

Intelligent Systems Research Group / Karlsruhe, Germany

Conformal inference, anomaly detection, uncertainty.

01

Profile

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. Recent work studies low-data conformal anomaly detection, shift-aware weighted procedures, and practical Python interfaces that turn anomaly scores into calibrated p-values and FDR-controlled decisions.

In parallel to the theory, I build research software for reproducible experiments and applied workflows. The current toolchain includes nonconform for conformal anomaly detection and online-fdr for sequential multiple testing when hypotheses arrive over time.

02

Focus

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

03

Selected work

• 01

  nonconform

  Conformal anomaly detection and change-point detection for turning anomaly scores into calibrated p-values, with split, cross, and weighted conformal workflows plus FDR-controlled decisions.

  Python, conformal inference, anomaly detection, FDR control

  GitHubDocsPyPI

• 02

  online-fdr

  Sequential multiple-testing procedures for online FDR and FWER control, including one-by-one APIs for LORD, SAFFRON, ADDIS, alpha-spending, and batch baselines.

  Python, online FDR, FWER, sequential testing

  GitHubDocs

• 03

  oddball

  A lightweight collection of anomaly detection datasets for reproducible experiments.

  Python, datasets, benchmarks

  GitHub

• 04

  aberrant

  Online anomaly detection algorithms for streaming settings.

  Python, streaming, anomaly detection

  GitHub

04

Publications

• 2026

  Conformal Anomaly Detection in Python: Moving Beyond Heuristic Thresholds with 'nonconform'

  Oliver Hennhöfer, Maximilian Kirsch, Christine Preisach

  Preprint, arXiv:2605.13642

  arXiv

• 2026

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

  Oliver Hennhöfer, Christine Preisach

  Preprint, arXiv:2603.23205

  arXiv

• 2024

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

  Oliver Hennhöfer, Christine Preisach

  IEEE ICKG 2024, pp. 110-119

  DOIarXiv