MSR India Research Overview


August 17, 2015


Sriram Rajamani




Distributed machine learning is an important area that has been receiving considerable attention from academic and industrial communities, as data is growing in unprecedented rate. In the first part of the talk, we review several popular approaches that are proposed/used to learn classifier models in the big data scenario. With commodity clusters priced on system configurations becoming popular, machine learning algorithms have to be aware of the computation and communication costs involved in order to be cost effective and efficient. In the second part of the talk, we focus on methods that address this problem; in particular, considering different data distribution settings (e.g., example and feature partitions), we present efficient distributed learning algorithms that trade-off computation and communication costs.


Sriram Rajamani

I am Assistant Managing Director of Microsoft Research India, and “area champion” for two research areas in Microsoft Research India: (1) Programming Languages and Tools, (2) Security and Privacy

I am broadly interested in programming languages and tools to improve software productivity. Specific current research interests include: building new programming tools by combining verification, testing, and statistics, designing new programming models for concurrent and distributed systems, and designing programming languages and analysis techniques to enable widespread use of machine learning by non-experts.

I moved to MSR India towards the end of 2005. Prior to moving to MSR India, I was manager for the Software Productivity Tools (SPT) group at MSR Redmond. SPT was a truly remarkable set of people.