Research Staff
- Contribute to uncertainty-aware machine learning methodology for decision systems.
- Guide reproducible experimentation in anomaly detection and conformal inference workflows.
Curriculum vitae
Oliver Hennhöfer
Research Assistant / Research Staff in uncertainty-aware machine learning and statistical methodology.
Profile
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Experience
Research Assistant
- Supported applied projects in uncertainty quantification and statistical decision support.
- Developed and maintained reproducible code for early-stage research tooling.
Education
Graduate Education
- Thesis and coursework in machine learning, statistics, and uncertainty-aware systems.
- Focus on trustworthy inference and uncertainty quantification in applied settings.
Undergraduate Education
- Foundational coursework in mathematics, statistics, and software engineering.
Skills
- Python, JavaScript/TypeScript, Git, Docker, GitHub Actions
- Machine learning: uncertainty estimation, anomaly detection, conformal inference
- Statistics & probability: sequential methods, risk control, evaluation protocols
- Scientific tooling: packaging, reproducible experiments, documentation
Projects
nonconform
Python pacakge for Conformal Anomaly Detection & Change-Point Detection.
online-fdr
Python pacakge for false discovery rate control in sequential hypotheses testing.
aberrant
Python package for online anomaly detection in streaming data and related tooling.
talk-tag
Resource and tooling for language and annotation-focused NLP workflows.
Publications
2026