I am a Machine Learning Scientist working on Big Data (add other buzz words here) making history at the one and only Amazon. I am a part of the Core Machine Learning team, and work under the direction of Amazon Fellow Charles Elkan, out of Palo Alto.
My research interests lie at the intersection of AI, optimization, learning and inference particularly using them to understand, model and combat noise and uncertainty in real world applications. Broadly, I work on using ideas from online algorithms, optimization under uncertainty, control theory, game theory, artificial intelligence, graphical models and estimation theory to develop novel, theoretically well motivated optimization algorithms that bring intelligent decision support to resource allocation problems. For more details on stuff I managed to sneak past peer review, see my publications page.
Organizing MPE 2013+ Workshop on Data-aware Energy Use
TPC of HotPower 2014
Publicity Chair for BuildSys 2014
Reviewer for the Journal of Climate
Reviewer for the Transactions on Cloud Computing
From 2014-2015 I was a post-doc at UC San Diego, in the Machine Learning Group with Prof. Charles Elkan and MESL, headed by Prof. Rajesh Gupta.
From 2011-2013 I was a Research Staff Member at IBM Research, India Research Lab in Bangalore where I worked on Next Generation Systems. I was a part of the High Performance Computing (HPC) group and also worked with the Smarter Energy group.
At the same time, I had the pleasure of working with the amazing game theory lab run by Prof. Narahari at the Indian Institute of Science. My collaborators included Shweta Jain, Swaprava Nath and Pankaj Dayama.
Before IBM (note the obfuscation of a start date), I was a Doctoral student in the Electrical and Computer Engineering department at Carnegie Mellon University, from where I graduated in 2011. I am fortunate to be advised (then and now) by Profs. Raj Reddy, Rohit Negi and Pradeep Khosla. My PhD research interests were in establishing theoretical and algorithmic limits and guarantees for large scale detection applications (think sensor networks) using ideas from information and coding theory.
In the past I have worked in robust speech recognition, text-independent speaker identification, speech science, machine learning and auditory scene analysis with Prof. Richard Stern. I also did some work on target tracking with Ryan Kerekes and Iris recognition (the body part not the flower) with Ryan Kerekes and Jason Thronton (who even in this day and age does not have a webpage).
I am a proud recipient of the National Talent Search (NTSE) and the Jawaharlal Nehru (JNCASR) scholarships from the the government of India during my undergraduate studies.
Disclaimer : The postings on this site are my own and don't necessarily represent Amazon's positions, strategies or opinions.
PhD - Electrical and Computer Engineering - Carnegie Mellon University, Pittsburgh(11)