Andreas Theissler
Research on Machine Learning
and Human-centered AI
This website addresses research questions from the fields of Machine Learning in applications, Machine Learning fundamentals, and human-centered AI:
Some examples of conducted research for machine learning in applications are fault detection in automotive systems using different settings of anomaly detection, predictive maintenance, Deep Learning-based multi-output regression of solar signals or the classification of Twitch messages. This line of research addresses the recurring question when bringing Machine Learning to applications, i.e.:
How can we utilize, adapt, combine or enhance state-of-the-art machine learning for real-world applications ?
Examples of research in the field of human-centered AI are explainable artificial intelligence (XAI), interpretable machine learning and learning from partly or fully unlabelled data – addressing the overriding question:
How can we enable, improve, evaluate, or understand machine learning by incorporating expert knowledge ?
On this website you will find information, publications and in some cases videos on reserarch I have conducted in the aforementioned fields. Research is a never-ending process… so you might want to check the website regularly, read through the provided news ticker, or follow me on researchgate.
Andreas Theissler
### News: ###Keynote (invited talk) at Explainable AI for time series (XAI-TS) Workshop at ECML/PKDD 2023: Explainable AI for time series classification and anomaly detection: Current state and open issues.Keynote (invited talk) at XKDD Workshop at ECML/PKDD 2023: Explainable AI: how far we have come and what’s left for us to doSept 2022: Journal paper in IEEE Access accepted: „Explainable AI for Time Series Classification: A review, taxonomy and research directions“Sept 2022: Journal paper in Springer Neural Computing accepted: „XAI4EEG: Spectral and spatio-temporal explanation of Deep Learning-based Seizure Detection in EEG time series“Sept 2022: Best paper award for PhD student at IEEE conference (IES Young Professionals & Students Paper Award): „Visual Detection of Tiny Objects for Autonomous Robotic Pick-and-Place Operations“ published at IEEE ETFA 2022.Aug 2022: Journal paper in Springer The Visual Computer accepted: „VisGIL: Machine Learning based Visual Guidance for Interactive Labeling“March 2022: Two papers presented and published in Proceedings at IEEE EduconOct 2021: Paper accepted at BMVC 2021 „SPARROW: Semantically Coherent Prototypes for Image Classification“Sept 2021: Best paper award for PhD student at IEEE conference (IES Young Professionals & Students Paper Award): „Fingertip 6-Axis Force/Torque Sensing for Texture Recognition in Robotic Manipulation“ published at IEEE ETFA 2021.Aug 2021: 4th place (> 100 participants, 35 with submissions) in the Machine Learning Data Challenge of the Ariel Space mission by the European Space Agency. The task: „identify and correct for the effects of spots on the star from the faint signals of the exoplanets‘ atmospheres in the presence of signal distortions by the instrument“.June 2021: Journal paper accepted in Elsevier Reliability Engineering & System Safety „Predictive Maintenance enabled by Machine Learning: Use cases and challenges in the automotive industry“June 2021: Paper accepted at IEEE Conference on Emerging Technologies and Factory Automation (ETFA 2021) „Interpretable Machine Learning: A brief survey from the predictive maintenance perspective“June 2021: Paper accepted at IEEE Conference on Emerging Technologies and Factory Automation (ETFA 2021) „Challenges of machine learning-based RUL prognosis: A review on NASA’s C-MAPSS data set“
Interpretable Machine Learning / Explainable AI
Learning from unlabeled data
Machine Learning in Applications
Anomaly Detection