Andreas Nienkötter 于2021年在德国明斯特大学取得计算机科学博士学位。主要研究兴趣包括基于泛化中值和向量空间嵌入的共识学习方法，降维方法和机器学习算法。前期工作主要包括医学图像分析和共识学习理论方法研究。目前正在从事山地灾害风险评估与预测方法研究工作。

联系方式

nienkoetter@scu.edu.cn

代表性研究成果

一、著作 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.

二、论文 Journal papers

1. 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.

2. 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.

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

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

5. 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.

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

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