
Felix Biessmann obtained his B.Sc. in Cognitive Science in 2005 (University of Osnabrück and ETH Zurich), a M.Sc. in Neural and Behavioural Sciences in 2007 (International Max-Planck Research School, Tübingen), and a Ph.D. in Machine Learning (ML) in 2011 (Max Planck Institute for Cybernetics, Tübingen and Technical University of Berlin). In 2013, Felix was appointed Assistant Professor for Machine Learning at Korea University, Seoul. From 2014 to September 2018, he has been working as a machine learning scientist at Amazon Research, Berlin, on scalable ML methods for Data Quality and Search. Since 2018, he has been a tenured professor for Machine Learning at the Einstein Center Digital Future and the Berlin University of Applied Sciences and Technology (BHT). His research focuses on methods and applications for responsible usage of ML. He has published over 90 peer reviewed articles in internationally renowned journals and conference proceedings, and he has been serving the scientific community by reviewing for top-tier ML conferences and journals. Felix has led over 10 research projects since 2020.
Publications
- Robust Conformal Prediction for Infrequent Classes
- SoilNet: A multimodal multitask model for hierarchical classification of soil horizons
- Uncertainty Propagation in XAI: A Comparison of Analytical and Empirical Estimators
- End-to-end Pseudonymization of German Texts with Deep Learning – An Empirical Comparison of Classical and Modern Approaches
- MechDetect: Detecting Data-Dependent Errors
- Towards Realistic Error Models for Tabular Data
- TinyChirp: Bird Song Recognition Using TinyML Models on Low-power Wireless Acoustic Sensors
- Advancing Blood Sample Analysis: Incorporating Expert Opinions and Explainable AI in Multi-label Disease Prediction
- Evaluating the quality of tabular synthetic data in health care
- Anonymized but Useful Synthetic Tabular Health Data for AI based Fall Risk Assessment
- Readiness Assessment for AI in Nursing Care Projects: Multimethods Study
- Towards Transnational Fairness in Machine Learning: A Case Study in Disaster Response Systems
- The Influence of Faulty Labels in Data Sets on Human Pose Estimation
- RamanBench: A Large-Scale Benchmark for Machine Learning on Raman Spectroscopy