### 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 do
Sept 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 Educon
Oct 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“