Stefanie Jegelka will join the faculty in January 2015. She is currently a postdoctoral researcher in the Department of Electrical Engineering and Computer Science at UC Berkeley. She received a PhD in Computer Science from ETH Zurich (in collaboration with the Max Planck Institute for Intelligent Systems in Tuebingen, Germany), and a Diplom in Bioinformatics with distinction from the University of Tuebingen in Germany. During her studies, she was also a research assistant at the Max Planck Institute for Biological Cybernetics, and has spent a year at the University of Texas at Austin. She has spent research visits at Georgetown University, the University of Washington, the University of Tokyo, INRIA and Microsoft Research. She has been a fellow of the German National Academic Foundation and its College for Life Sciences, and has received a Google Anita Borg Fellowship, a Fellowship of the Klee Foundation, and a Best Paper Award at the International Conference on Machine Learning. Stefanie has organized several workshops on Discrete Optimization in Machine Learning, and has held three tutorials on Submodularity in Machine Learning at international conferences. Her research interests lie in algorithmic machine learning. In particular, she is interested in modeling and efficiently solving machine learning problems that involve discrete structure. She has also worked on distributed machine learning, kernel methods, clustering and applications in computer vision.