I am a second year Ph.D student at the University of Washington, Seattle and am working with Prof. Jeff Bilmes. In the summer of 2012, I did an internship at MSR, Redmond and worked with Dr. Matthai Phillipose. My internship was mainly in designing a continuous audio and vision wearable system designed to understand interactions amongst people. Prior to coming to UW, I spent four awesome years, at the Indian institute of Technology, Bombay where I completed my B.Tech in Electrical Engineering. At IIT Bombay, I worked mainly with Prof. Subhasis Chaudhuri. I also did my internship an 2010 at the Computer Science Department at Simon Fraser University, where I worked with Prof. Torsten Möller.
My main research interests are at the intersection of Discrete optimization, specifically submodularity and Machine Learning. The main theme of my research is investigating certain theoretical characterizations of submodular functions and exploiting these in machine learning applications. One significant thread of my research evolves around providing faster and more practical algorithms for various forms of submodular function optimization, which occur naturally in real world applications. I am also interested in Bregman divergences with applications to Machine Learning. For more information please see some of my recent publications on these, or contact me at rkiyer at u.washington.edu.
As an undergraduate, I worked mainly in computer vision and image processinge. In particular I worked on object mining from videos and creating story-board video from a novel script. I also worked on a project involving information theoretic bounds in the embedding capacity of reversible watermarking schemes.
(C5) Rishabh Iyer and Jeff A. Bilmes, The Lovasz-Bregman Divergence and connections to rank aggregation, clustering and web ranking , In Uncertainity in Artificial Intelligence (UAI), Bellevue, Washington, July - 2013 (selected for oral presentation).
(C4) Rishabh Iyer, Stefanie Jegelka, and Jeff A. Bilmes, Fast Semidifferential-based Submodular Function Optimization, In Proc. International Conference on Machine Learning (ICML), Atlanta, Georgia, June - 2013 PDF, Extended Version (selected for oral presentation).
(C3) Rishabh Iyer and Jeff A. Bilmes, Submodular-Bregman and the Lovasz-Bregman Divergences with Applications, In Advances of Neural Information Processing Systems (NIPS), Lake Tahoe, Nevada, December - 2012 PDF, Extended Version, Poster.
(C2) Rishabh Iyer and Jeff A. Bilmes, Algorithms for Approximate Minimization of the Difference between Submodular Functions, In Uncertainity in Artificial Intelligence (UAI), Catalina Islands, CA, August - 2012 PDF, Extended Version, Talk (selected for oral presentation, 9% Acceptance rate).
(C1) Shah Ronak, Rishabh Iyer and Subhasis Chaudhuri, Object Mining for Large Video Data, In Proc. British Machine Vision Conference (BMVC), Dundee, Scotland, United Kingdom, August - 2011, PDF (selected for oral presentation - 8% Acceptance rate).
(W1) Rishabh Iyer, Stefanie Jegelka and Jeff A. Bilmes, Mirror Descent-Like Algorithms for Submodular Optimization, In Advances of Neural Information Processing Systems (NIPS) Workshop on Discrete Optimization in Machine Learning (DISCML): Structure and Scalability, Lake Tahoe, Nevada, December - 2012 PDF, Poster.
(P1) Rishabh Iyer, Rushikesh Borse, Shah Ronak and Subhasis Chaudhuri, Embedding Capacity estimation for pixel pair based watermarking schemes, Arxiv Preprint.
(1) Submodular Bregman divergences and Lovasz-Bregman divergences with Applications to Machine Learning, Seminar, IIT Bombay, March 2013.
(2) Algorithms for Approximate Minimization of the Difference between Submodular Functions, UAI-2012, Catalina Island, August 2012.
In my copious free time, I try to bike (of course when the weather in seattle is good), hike and play squash.
I also enjoy meditation and introspection.
You can also find me on facebook, LinkedIn and Google.
Finally this is the person I owe my life to!