VP, Distinguished Scientist
Amazon Web Services
Machine Learning

2100 University Avenue
Palo Alto, CA 94303



I joined Amazon Web Services in July 16, 2016 and am now VP / Distinguished Scientist for Machine Learning. We are building exciting tools for data scientists, computer vision, NLP, deep learning and beyond.

For classes in Machine Learning check out my lectures. I am currently not looking for PhD students, since I work in industry. For that, please apply to the Machine Learning Department at Carnegie Mellon University. The also applies to self-funded visitors. That said, I’m always looking for talented interns and team members in general. In particular, if you’re good with deep learning, have written code, are able to build statistical models, design efficient algorithms, or are familiar with high performance computer systems, this is a good place to be.


My primary research interest covers the following areas:

  • Deep Learning (yes, everyone works on this now). What interests me particularly are algorithms for state updates, invariances and statistical testing.
  • Scalability of algorithms. This means pushing algorithms to internet scale, distributing them on many (faulty) machines, showing convergence, and modifying models to fit these requirements. For instance, randomized techniques are quite promising in this context. In other words, I’m interested in big data.
  • Kernels methods are quite an effective means of making linear methods nonlinear and nonparametric. My research interests include support vector Machines, gaussian processes, and conditional random fields. Kernels are very useful also for the representation of distributions, that is two-sample tests, independence tests and many applications to unsupervised learning.
  • Statistical modeling, primarily with Bayesian Nonparametrics is a great way of addressing many modeling problems. Quite often, the techniques overlap with kernel methods and scalability in rather delightful ways.
  • Applications, primarily in terms of user modeling, document analysis, temporal models, and modeling data at scale is a great source of inspiration. That is, how can we find principled techniques to solve the problem, what are the underlying concepts, how can we solve things automatically.



I studied physics in Munich at the University of Technology, Munich, at the Universita degli Studi di Pavia and at AT&T Research in Holmdel. During this time I was at the Maximilianeum München and the Collegio Ghislieri in Pavia. In 1996 I received the Master degree at the University of Technology, Munich and in 1998 the Doctoral Degree in computer science at the University of Technology Berlin. Until 1999 I was a researcher at the IDA Group of the GMD Institute for Software Engineering and Computer Architecture in Berlin (now part of the Fraunhofer Gesellschaft). After that, I worked as a Researcher and Group Leader at the Research School for Information Sciences and Engineering of the Australian National University. From 2004 onwards I worked as a Senior Principal Researcher and Program Leader at the Statistical Machine Learning Program at NICTA, now part of Data61 CSIRO. From 2008 to 2012 I worked at Yahoo! Research. In spring of 2012 I moved to Google Research to spend a wonderful year in Mountain View and I continued working there until the end of 2014. From 2013-2017 I was professor at Carnegie Mellon University. I co-founded Marianas Labs in early 2015. In July 2016 I moved to Amazon Web Services to help build AI and Machine Learning tools for everyone.