Ralf Herbrich

SVP Builder Platform and AI, Zalando

Ralf Herbrich, Amazon

Since January 2020, I work at Zalando SE in Berlin. I lead a diverse range of departments and initiatives that have at their core research in the area of artificial intelligence (AI) spanning data science, machine learning (ML) and economics in order to make Zalando the starting point for fashion. In particular, this includes working on fashion size and fit, causality, forecasting, the development and deployment of easy-to-use AI tools as well as exploring new research ideas in the area of Fashion AI through our Zalando Research team. I am also responsible for both the internal science community as well as our interactions with academia. Our teams apply and advance the science in many established scientific fields including computer vision, natural language processing, data science and economics.

From 2013 to 2020, I worked at Amazon as Director of Machine Learning leading Core Machine Learning worldwide with teams in Cambridge, Barcelona, Tübingen, Berlin, New York and Seattle. Until August 2013, I worked in Seattle and then in Berlin, Germany. My work invloved cross-disciplinary applications of Forecasting, Content Linkage (including Machine Translation), Scalable Machine Learning Services, Vision-Assisted Technologies, Human-Aware AI, Robotics and Causality.

From October 2011 to November 2012, I worked at Facebook in Palo Alto & Menlo Park leading the Unified Ranking and Allocation team. This team is focused on building horizontal large-scale machine learning infrastructure for learning user-action-rate predictors that enabled unified value experiences across the products.

From 2009 to 2011, I was Director of Microsoft’s Future Social Experiences (FUSE) Lab UK demonstrating and enabling new social experiences through development of computational intelligence technologies on large online data collections. From 2006 – 2009, together with Thore Graepel, I was leading the Applied Games and the Online Services and Advertising (OSA) research group which engaged in research at the intersection of machine learning and computer games as well as research in search and online advertising combining insights from machine learning, information retrieval, game theory, artificial intelligence and social network analysis. I joined Microsoft Research in 2000 as a Postdoctoral researcher and Research Fellow of the Darwin College Cambridge.

Prior to joining Microsoft, I worked at the Technical University Berlin as a teaching assistant where I obtained both a diploma degree in Computer Science and a Ph.D. degree in Statistics.

My research interests include Bayesian inference and decision making, computer games, kernel methods and statistical learning theory.