Dr. Sinn's research interests lie at the intersection of Computer Science (Machine Learning) and Statistics (Time Series Analysis, Large-Sample Theory). Most recently, his focus is on understanding the robustness of Deep Learning classifiers with respect to adversarial perturbations. In his past work, he worked on order statistics for time series (with applications to the analysis of electroencephalography recordings), asymptotic properties of models for labeling and segmenting sequence data, and applications of semi-parametric statistical models to forecast electricity demand and distributed renewables in smart grids.
Dr. Sinn received his Master's in Computer Science in 2006, and his PhD in Mathematics in 2009, both from the University of Luebeck, Germany. From 2009-2011 he worked as a Postdoctoral Research Fellow at the University of Waterloo, Canada. In 2011, he joined IBM as a Research Staff Member where, since 2012, he has been leading the AI, Machine Learning and Data Science work in the Dublin Research laboratory.
Dr. Sinn is the co-author of more than 40 peer-reviewed publications and co-inventor of more than 10 US patents. His research has been founded by the German Research Foundation (DFG), the Canadian Bureau of International Education, Google Inc and MITACS. He has served as a reviewer and program committee member for numerous journals and top conferences in data mining, machine learning and artificial intelligence, including IEEE Transactions on Pattern Analysis and Machine Intelligence, IEEE Transactions on Smart Grids, Data Mining and Knowledge Discovery, AISTATS, ICLR, ICML , IJCAI, NIPS and UAI. He has also served as external reviewer on PhD committees at EPFL, TU Dresden and Aalborg University. At IBM, he has been the recipient of a Research Division Award and two Outstanding Technical Accomplishment Awards.