S. Sathiya Keerthi

I am a principal scientist in Microsoft. I have been with Microsoft since June 2012. Currently I lead a machine learning group in Microsoft Office. Previously in Microsoft I was with the Cloud and Information Services Lab (CISL, pronounced as sizzle), an applied science group in Microsoft with Raghu Ramakrishnan as the head. I am located in Mountain View, CA. From Jan 2004-Apr 2012 I was with the Machine Learning Group of Yahoo! Research, in Santa Clara, CA. My recent research has mainly focused on the design of distributed training algorithms for developing various types of linear and nonlinear models on Big Data, and the application of machine learning to textual problems.

Prior to joining Yahoo! Research, I worked for 11 years at the Indian Institute of Science, Bangalore, and for 5 years at the National University of Singapore. During those sixteen years my research focused on the development of practical algorithms for a variety of areas, such as machine learning, robotics, computer graphics and optimal control. (Many of the publications during that period are not mentioned in this page.) My works on support vector machines (e.g., improved SMO algorithm), polytope distance computation (e.g., GJK algorithm) and model predictive control (e.g., stability theory) are highly cited. Overall, I have published more than 100 papers in leading journals and conferences. I am an Action Editor of JMLR (Journal of Machine Learning Research) since 2008. Previously I was an Associate Editor for the IEEE Transactions on Automation Science and Engineering.

Contact: keerthi at microsoft dot com

Slide deck of my talk on Interplay between Optimization and Generalization in Deep Neural Networks given at the 3rd annual Machine Learning in the Real World Workshop organized by Criteo Research, Paris, on 8th November, 2017: Optimization_and_Generalization_Keerthi_Criteo_November_08_2017.pptx. This is a review and critique of recent works in this topic. The actual talk was for 45 minutes and I covered the main ideas quickly. The ppt has more detailed material. I intend to update the slide deck as new works are published on this and related topics.

Slide deck of my talks on Optimization for machine learning given at UC Santa Cruz in February, 2017: Keerthi_Optimization_For_ML_UCSC_2017.pdf

In 2010 I attended and gave a talk at GilbertFest, a symposium in honor of my Ph.D thesis advisor,Elmer G. Gilbert. Check out the symposium page, which also has pdfs of his classic papers in Control and Optimization. I am honored to have some of my joint papers with him in that list.

Check out:

Citations of my papers in Google Scholar


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1999 and Earlier (To be added)

Last updated: May, 2017