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Iyer, Rishabh

Dr. Rishabh Iyer

Assistant Professor

Degrees:

  • Post-Doctoral Researcher, University of Washington, 2016
  • Ph.D. in Computer Science, University of Washington, Seattle, 2015
  • B.Tech, IIT-Bombay, 2011

Research Interests:

  • Artificial Intelligence
  • Machine Learning
  • 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.

Major Honors and Awards:

  • Business Contribution Award, NEC Japan, 2018
  • CEATEC Award, Innovation for Better Society, CEATEC Japan CPS/IoT Exhibition, 2016
  • 2nd Place Cyber Security for the Next Generation: Americas Round, Kaspersky Lab, 2014

Representative Publications:

  • 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)

Notable Service:

  • Reviewer for Journal of Machine Learning Research (JMLR 2016, 2017, 2018)
  • Reviewer for Journal of Discrete Applied Mathematics (DAM 2016)
  • Reviewer for Pattern Analysis and Machine Intelligence (PAMI 2015, 2016, 2017)
  • Reviewer for Symposium of Discrete Algorithms, SODA 2019
  • Reviewer for Conference on Learning Theory (COLT) 2019
  • Reviewer for International Conference of Machine Learning (ICML) 2013 – 2019
  • Reviewer for Neural Information Processing Systems (NIPS) 2013 – 2019
  • Program Committee Member for Uncertainty in Artificial Intelligence (UAI) 2013 – 2016
  • Program Committee Member for American Association of Artificial Intelligence (AAAI) 2016 – 2018
  • Program Committee Member for Artificial Intelligence and Statistics (AISTATS) 2016 – 2019
  • Reviewer for International Conference of Learning Representations (ICLR) 2018, 2019

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