Marius Kloft is interested in theory and algorithms of statistical machine learning and its applications, especially in statistical genetics, mechanical engineering, and chemical process engineering. He has been working on, e.g., multiple kernel learning, transfer learning, anomaly detection, extreme classification, and adversarial learning. He co-organized workshops on these topics at NIPS 2010, 2013, 2014, 2017, ICML 2016, and Dagstuhl 2018. His dissertation on Lp-norm multiple kernel learning was nominated by TU Berlin for the Doctoral Dissertation Award of the German Chapter of the ACM (GI). He received the Google Most Influential Papers Award and the DFG Emmy-Noether Career Award.