I work on the foundations of robust and reliable machine-learning systems under distribution shift, with publications in NeurIPS, ICML, ICLR, and JMLR.

My research develops methods to evaluate and correct shifts between training and deployment conditions, enabling confidence in real-world deployment and supporting certification and risk-aware decision-making.

Since 2024, I head the Robust AI group at the LIT AI Lab, Johannes Kepler University Linz [link]

Research Areas

• Quantification of Distribution Shifts
  (divergence measures, density ratio estimation, out-of-distribution detection)

• Correction of Distribution Shifts
  (re-calibration, domain adaptation, parameter choice)

• Robustness Evaluation and Certification Readiness
  (benchmarks, synthetic test data, safety and regulatory alignment)

Short BIO

Since 2024, I lead applied research on robustness, evaluation, and certification readiness of machine-learning systems at the Institute for Machine Learning, Johannes Kepler University Linz; including supervision of PhD projects and coordination of industry-linked research. Previously, I was a Postdoctoral Researcher at RICAM, Austrian Academy of Sciences (2022–2024), Research Team Lead at SCCH GmbH (2020–2022), and Industrial Researcher at SCCH GmbH (2012–2016).