Andreas Nienkötter received his PhD degree in Computer Science from the University of Münster, Germany in 2021. His research interests include consensus learning using the generalized median, vector space embedding methods, dimensionality reduction methods and machine learning. He previously worked on topics in medical image analysis and theoretical consensus learning properties. He is currently researching on geohazard risk assessment and prediction.

Representative research results.

1. Books/Book chapters

Nienkötter and Jiang (2018), “Consensus learning for sequence data.”, in Data Mining in Time Series and Streaming Databases published by World Scientific.

2. Journal papers

Nienkötter and Jiang (2023), “Kernel-Based Generalized Median Computation for Consensus Learning.”, IEEE Trans. on Pattern Analysis and Machine Intelligence 45, Nr. 5: 5872–5888.

Deng, Wu, Bian, Zhang, Di, Nienkötter, Deng, Feng (2023), “Scattered Mountainous Area Building Extraction From an Open Satellite Imagery Dataset.”, IEEE Geosci. Remote. Sens. Lett. 20: 1-5.

Nienkötter and Jiang (2020), “A lower bound for generalized median based consensus learning using kernel-induced distance functions.”, Pattern Recognition Letters 140: 339–347.

Welsing, Nienkötter, Jiang (2020), “Exponential Weighted Moving Average of Time Series in Arbitrary Spaces with Application to Strings.”, S+SSPR 2020: 45-54.

Nienkötter and Jiang (2019), “Distance-preserving vector space embedding for consensus learning.”, IEEE Trans. on Systems, Man, and Cybernetics: Systems 51, Nr. 2: 1244–1257.

Nienkötter and Jiang (2016), “Improved prototype embedding based generalized median computation by means of refined reconstruction methods.”, S+SSPR 2016, Merida, Mexico.

Nienkötter and Jiang (2016), “Distance-preserving vector space embedding for the closest string problem.” at 23rd International Conference on Pattern Recognition (ICPR). Cancun, Mexico.