Alex Smola

Interests

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.

Books

• A. Zhang, Z. Lipton, M. Li, A.J. Smola, Dive into Deep Learning, 2019.
• G. Bakir, T. Hofmann, B. Schölkopf, A.J. Smola, B. Taskar, and S.V.N. Vishwanathan, editors. Predicting Structured Data. MIT Press, Cambridge, MA, 2006.
• S. Mendelson and A. J. Smola, editors. Machine Learning, Proceedings of the Summer School 2002, Australian National University, volume 2600 of Lecture Notes in Computer Science. Springer, 2003.
• B. Schölkopf and A. J. Smola. Learning with Kernels. MIT Press, 2002.
• B. Schölkopf, C. J. C. Burges, and A. J. Smola, editors. Advances in Kernel Methods--Support Vector Learning. MIT Press, Cambridge, MA, 1999.
• A. J. Smola, P. L. Bartlett, B. Schölkopf, and D. Schuurmans, editors. Advances in Large Margin Classifiers. MIT Press, Cambridge, MA, 2000.

Biography

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 Geselschaft). 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. 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.