Welcome to Rishabh Iyer's webpage
Machine Learning Researcher
I am a Research Scientist at Microsoft with hands on experience in several facets of AI & Machine Learning, including:
- Discrete Optimization (specifically submodular optimization) in Machine Learning
- Convex and Non Convex Optimization in Machine Learning
- Deep Learning for Image Classification and Object Detection
- Data Summarization (Video/Image/Text)
- Active Learning, Data Subset Selection, Data partitioning, Model Compression/Pruning etc.
- Video Analytics
- Online Learning, Contextual Bandits and Reinforcement Learning.
- Click Prediction, Web Search and Information Retrieval.
For more on my research, please see my research page or my publications.
I also have a strong theoretical understanding of Discrete and Continuous Optimization, as well as a strong experience in getting real world Machine Learning and Computer Vision problems to work! I have contributed to several Machine Learning and Computer Vision software in C++ and Python, and have worked with massive scale problems with hundreds of Millions of Data Instances.
I completed my Ph.D in 2015 from University of Washington, Seattle where I worked with Jeff Bilmes. I am excited in making machines assist humans in processing massive amounts of data, particularly in understanding videos and images. I am interested in building intelligent systems which organize, analyze and summarize massive amounts of data, and also automatically learn from this.
During my Ph.D, I won best paper awards at two of the top Machine Learning Conferences, Neural Information Processing Systems (NIPS) and International Conference of Machine Learning (ICML). I also won a Microsoft Research Ph.D. Fellowship, a Facebook Ph.D. Fellowship, and the Yang Award for Outstanding Graduate Student from University of Washington, Seattle.
For more information, please see my Google Scholar Profile, my LinkedIn Profile, my DBLP, my Resume or my GitHub page
awards and recognition
- Selected as a finalist in the LDV Computer Vision Conference, New York in 2017
- Yang Outstanding Graduate Student Award, University of Washington, Seattle
- Microsoft Research Fellowship Award, 2014
- Facebook Fellowship Award. 2014 (Declined in favor of Microsoft)
- Best Paper Award at the International Conference of Machine Learning, 2013
- Best Paper Award at the Neural Information Processing Systems Conference, 2013
- Invited for Talks/Tutorials at the AMS Sectional Meeting, the International Symposium for Mathematical Programming (ISMP), 7th IEEE Winter Conference on Applications of Computer Vision (WACV), and Non-Convex Optimization and Machine Learning (NOML at IIT Bombay)
Work Experience and Education
- March 2016 - Present, Research Scientist, Microsoft
- March 2015 - March 2016, Post-Doctoral Researcher, University of Washington
- September 2011 - March 2015, Ph.D Candidate, University of Washington, Seattle
- August 2011 - May 2011, B.Tech, IIT-Bombay
Recent news
- I visited University of Texas at Dallas and University of Pittsburgh in February 2018 and gave a talk on Scalable and Practical Discrete Optimization for Big Data (see this link).
- Two papers accepted into AISTATS 2019!
- Tutorial Speaker at the 7th IEEE Winter Conference on Applications of Computer Vision (WACV) 2019 (see tutorial website. Slides are on the website)
- Three papers accepted to WACV 2019!
- Invited Talk at Allen Institute of AI and Google Seattle, October 2018 (Video Link)
- Released Open Source software Jensen with my collaborators John Halloran and Kai Wei, July 2018
- Presented our work on Online Learning for Click Prediction at the Microsoft Machine Learning, AI and Data Science Conference, Spring 2017
- Finalist at the LDV Vision Conference, New York, May 2017
- Invited Speaker at AMS Sectional Meeting, Special Session on Geometry and Optimization in Computer Vision, Pullman, WA, March 2017
- Our work on Limited Vocabulary Speech Data Subset Selection selected to Appear in Computer Speech & Language, 2017. Corpus Definitions and Baselines for SVitchboard-II and FiSVer-I datasets can be found at this link.
- Work on Minimizing Ratio of Submodular Function accepted at ICML 2016
- Finished my PostDoc (Feb 2016). Will be Joining Microsoft, starting March 2016.
- Two Papers accepted in NIPS 2015, Two Papers in AISTATS 2015, One Paper in ACL and INTERSPEECH 2015 and one paper in ICML 2015
- Invited Speaker at the International Symposium on Mathematical Programming (ISMP), Pittsburg - July, 2015 (Session on Submodular Optimization, Link)
- Invited Lecturer at the Non-convex Optimization for Machine Learning (NOML) Summer School, IIT Bombay, India, June 2015
- Successfully defended in March 2015!
Selected Publications
For the complete list of publications and workshop papers, see my publications page or my research page.
- Rishabh Iyer and Jeff Bilmes, Near Optimal Algorithms for Hard Submodular Programs with Discounted Cooperative Costs, To Appear in Artificial Intelligence and Statistics (AISTATS) 2019, Naha, Okinawa, Japan
- Rishabh Iyer and Jeff Bilmes, A Memoization Framework for Scaling Submodular Optimization to Large Scale Problems, To Appear in Artificial Intelligence and Statistics (AISTATS) 2019, Naha, Okinawa, Japan
- Vishal Kaushal, Sandeep Subramanium, Suraj Kothiwade, Rishabh Iyer, and Ganesh Ramakrishnan, A Framework Towards Domain Specific Video Summarization, 7th IEEE Winter Conference on Applications of Computer Vision (WACV) 2019, Hawaii, USA (Long Version, Link to the Video)
- Vishal Kaushal, Rishabh Iyer, Suraj Kothiwade, Rohan Mahadev, Khoshrav Doctor, and Ganesh Ramakrishnan, Learning From Less Data: A Unified Data Subset Selection and Active Learning Framework for Computer Vision, 7th IEEE Winter Conference on Applications of Computer Vision (WACV), 2019 Hawaii, USA (Link to the Video)
- Yuzong Liu, Rishabh Iyer, Katrin Kirchhoff, Jeff Bilmes, SVitchboard-II and FiSVer-I: Crafting high quality and low complexity conversational english speech corpora using submodular function optimization, Computer Speech & Language 42, 122-142, 2017 (Corpus Definitions and Baselines for SVitchboard-II and FiSVer-I datasets can be found at this link)
- Wenruo Bai, Rishabh Iyer, Kai Wei, Jeff Bilmes, Algorithms for optimizing the ratio of submodular functions, In Proc. International Conference on Machine Learning( ICML) 2016 (Link to Video)
- Kai Wei, Rishabh Iyer, Shenjie Wang, Wenruo Bai, Jeff Bilmes, Mixed robust/average submodular partitioning: Fast algorithms, guarantees, and applications, In Advances of Neural Information Processing Systems (NIPS) 2015
- Kai Wei, Rishabh Iyer, Jeff Bilmes, Submodularity in data subset selection and active learning, International Conference on Machine Learning (ICML) 2015
- Sebastian Tschiatschek, Rishabh K Iyer, Haochen Wei, Jeff A Bilmes, Learning mixtures of submodular functions for image collection summarization, In Advances in Neural Information Processing Systems (NIPS) 2014
- Rishabh Iyer and Jeff Bilmes, Submodular optimization with submodular cover and submodular knapsack constraints, In Advances Neural Information Processing Systems 2013 (Winner of the Outstanding Paper Award) Link to Video, from 56th Minute.
- Rishabh Iyer, Stefanie Jegelka, Jeff Bilmes, Fast semidifferential-based submodular function optimization, International Conference on Machine Learning (ICML) 2013 (Winner of the Best Paper Award) Link to Video
- Rishabh Iyer, Jeff A Bilmes, The Lovász-Bregman Divergence and connections to rank aggregation, clustering, and web ranking, Uncertainty In Artificial Intelligence (UAI) 2013
- Rishabh Iyer, Jeff Bilmes, Algorithms for approximate minimization of the difference between submodular functions, with applications, Uncertainty in Artificial Intelligence (UAI) 2012