Alexander Kister studied mathematics and did a PhD in Probability Theory. After the PhD he worked five years at Fraunhofer IAIS, where he applied state of the art Machine Learning Algorithms for solving practical problems in various domains. At BAM his research focuses on algorithms that can deal with a common problem in materials science: The training data set is small compared to the complexity of the relationships that must be learned. His current fields of interest are active learning and the exploitation of knowledge, that is encoded in partial differential equations, by Machine Learning.