Michael May is Head of the Technology Field Business Analytics & Monitoring at Siemens Corporate Technology, Munich, and responsible for eleven research groups in Europe, US, and Asia. Michael is driving research at Siemens in data analytics, machine learning and big data architectures. In the last two years he was responsible for creating the Sinalytics platform for Big Data applications across Siemens’ business. Before joining Siemens in 2013, Michael was Head of the Knowledge Discovery Department at the Fraunhofer Institute for Intelligent Analysis and Information Systems in Bonn, Germany. In cooperation with industry he developed Big Data Analytics applications in sectors ranging from telecommunication, automotive, and retail to finance and advertising. Between 2002 and 2009 Michael coordinated two Europe-wide Data Mining Research Networks (KDNet, KDubiq). He was local chair of ICML 2005, ILP 2005 and program chair of the ECML/PKDD Industrial Track 2015. Michael did his PhD on machine discovery of causal relationships at the Graduate Programme for Cognitive Science at the University of Hamburg.
Daniel Keren (PhD 1991, Hebrew University in Jerusalem) is currently the chairman of the computer science department in Haifa University, Haifa, Israel. Prof. Keren's main fields of research are geometry and probability. Since 2003, he has been working closely with Prof. Assaf Schuster’s group in the Technion, in the area of distributed monitoring. His main contribution is in the mathematical aspects of the research such as object modeling, learning, optimization, and probability. A main novelty of the joint research is the incorporation of such mathematical tools into the research paradigm; this allowed to develop entirely new methodologies, based on geometry, to monitor general functions. He applied these methodologies to distributed stream monitoring, distributed online learning, and parallel machine learning. Prof. Keren’s goal is to continue developing the mathematical tools used so far, as well as to develop new ones, to improve and extend the applications of these tools to monitor, mine, and learn from large, distributed data sets.