Nedim is a research scientist at the Systems Technology Lab of Adobe Research. His main research interests are in statistical machine learning from volatile web data, especially, from user-generated content and user behavior.
Most recently, has has been working on scalable reinforcement learning algorithms for sequential decision making in digital marketing. Employing Spark, Hadoop, and related technologies belongs to the day-to-day engineering processes in order to handle the huge amount of data describing customers and advertising campaigns. In his own team, he combines these state-of-the art technologies with insights from research in order to automatically infer advertisement strategies in a timely manner that go beyond current targeting models.
Prior to joining Adobe in November 2012, he worked as a research associate at the Web Technology and Information Systems group at the Bauhaus-Universität Weimar, Germany, where he developed expertise in data mining, machine learning, and text classification. In 2011, he was hosted by the machine learning consulting group “The Church and Duncan Group, Inc.” in San Francisco and conducted research on new algorithms for look-alike modeling in the digital advertisement context. He has been published at several international conferences (e.g., ICDM, WWW, CIKM, SIGIR, ECIR, CLEF) and serves frequently in the program committees of TIR, PAN, and I-KNOW. Most of the publications can be found here.