University of Washington, Seattle Ph. No: (248) melodi.ee.washington.edu/~rkiyer/ Seattle, WA

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1 Rishabh Iyer Contact Information Research Interests Areas of Expertise Education Work Experience Achievements and Awards University of Washington, Seattle Ph. No: (248) Electrical Engineering Box melodi.ee.washington.edu/~rkiyer/ Seattle, WA I am interested in investigating both theoretical and practical aspects of discrete optimization, particularly submodularity with links to Machine Learning applications including Computer Vision, Natural Language Processing, Information Retrieval, and Speech Processing. Optimization: Discrete Optimization, Submodular Optimization, Approximation Algorithms, Convex optimization. Machine Learning: Supervised and Unsupervised Classification, Active Learning, Feature subset selection, Training Data Subset Selection, Image Summarization, News article and Document Summarization, Image segmentation and Image correspondence, Structured Prediction, Computer Vision and Speech. University of Washington, Seattle(Sep, June, 2015 (Expected)) Ph.D, Electrical Engineering, GPA: 3.95/4.00 Advisor: Prof. Jeff Bilmes Committee: Prof. Jeff Bilmes, Prof. Andreas Krause, Prof. Carlos Guestrin, Prof. Rekha Thomas, Prof. Maryam Fazel, Prof. Anna Karlin Area: Submodular functions with applications to Machine Learning. University of Washington, Seattle(Sep, Dec, 2013 ) MS, Electrical Engineering, GPA: 3.95/4.00 Advisor: Prof. Jeff Bilmes Indian Institute of Technology, Bombay(July, May, 2011 ) B.Tech., Electrical Engineering, GPA 8.95/10 Advisor: Prof. Subhasis Chaudhuri Area: Computer Vision. Graduate Research Assistant, University of Washington, Seattle (Sep, present) Working with Prof. Jeff Bilmes on Submodular Optimization and Machine Learning. Research Internship at Microsoft Research, Redmond (June - Sep, 2014) Worked at the Machine Learning Group (Mentors: Dr. Chris Meek, Dr. Max Chickering and Dr. Patrice Simard), on active learning and active featuring. Research Internship at Microsoft Research, Redmond (June - Sep, 2012) Worked at the Mobility and Networking Research Group (Mentor: Dr. Matthai Phillipose) on power efficient sensor selection for continuous vision mobile systems. Research Internship at Simon Fraser University, Canada (May - July, 2010) Worked at Computer Science Dept. (Mentor: Prof. Torsten Möller) on sampling and Yang Outstanding Doctoral Student Award, 2014 from the Department of Electrical Engineering, University of Washington. Won the Microsoft Research Fellowship award for Won the Facebook Fellowship award for 2014 (declined in favour of the Microsoft award). Best paper award at NIPS-2013 for the paper Submodular Optimization with Submodular Cover and Submodular Knapsack Constraints. Best paper award at ICML-2013 for the paper Fast Semi-differential based submodular function optimization. Undergraduate Research Award (URA) for truly significant research both in quality and in extent done during the B.Tech programme at IIT Bombay. Secured an All India Rank 131 in IITJEE 2007 among 2,50,000 students.

2 Selected for Indian National physics Olympiad and the Indian National Mathematical Olympiad in Travel scholarship for UAI-2012, 2013 and NIPS-2012, Awarded the T.R.S Anand and Bhanumati Anand scholarship in IIT Bombay in 2008 for consistent academic performance. Selected in the MITACS Globalink research-internship program in Canada in Peer Reviewed Publications Google Scholar: DBLP: [1] R. Iyer and J. Bilmes, Submodular Point Processes with Applications in Machine Learning, In Proc. International Conference on Artificial Intelligence and Statistics (AISTATS) (a preliminary version also appeared in Discrete Optimization in Machine Learning Workshop at NIPS-2014). [2] Y. Kawahara, R. Iyer and J. Bilmes, On Approximate Non-submodular Minimization via Tree-Structured Supermodularity, In Proc. International Conference on Artificial Intelligence and Statistics (AISTATS) (a preliminary version also appeared in Discrete Optimization in Machine Learning Workshop at NIPS-2014) [3] R. Iyer, R. Borse and S. Chaudhuri, Embedding capacity estimation of reversible watermarking schemes, In Sadhana, Springer, 39 (Part 6), [4] S. Tschiatschek, R. Iyer, H. Wei and J. Bilmes, Learning Mixtures of Submodular Functions for Image Collection Summarization, In Advances of Neural Information Processing Systems (NIPS) [5] R. Iyer, S. Jegelka and J. Bilmes, Monotone Closure of Relaxed Constraints in Submodular Optimization: Connections Between Minimization and Maximization, In Uncertainity in Artificial Intelligence (UAI) [6] K. Wei, R. Iyer, and J. Bilmes, Fast Multi-stage Submodular Maximization, In Proceedings of the International Conference on Machine Learning (ICML) (Selected for JMLR Fast track, 2% selection rate). [7] R. Iyer and J. Bilmes, Submodular Optimization with Submodular Cover and Submodular Knapsack Constraints, In Advances of Neural Information Processing Systems (NIPS) (Winner of the Best Paper Award). [8] R. Iyer, S. Jegelka, and J. Bilmes, Curvature and Optimal Algorithms for Learning and Minimizing Submodular Functions, In Advances of Neural Information Processing Systems (NIPS) [9] R. Iyer and J. Bilmes, The Lovász-Bregman Divergence and connections to rank aggregation, clustering and web ranking, In Uncertainity in Artificial Intelligence (UAI) (selected for oral presentation, 11% Acceptance rate). [10] R. Iyer, S. Jegelka, and J. Bilmes, Fast Semi-differential-based Submodular Function Optimization, In Proceedings of the International Conference on Machine Learning (ICML) (Winner of the Best paper award). [11] R. Iyer and J. Bilmes, Submodular-Bregman and the Lovász-Bregman Divergences with Applications, In Advances of Neural Information Processing Systems (NIPS) [12] R. Iyer and J. Bilmes, Algorithms for Approximate Minimization of the Difference between Submodular Functions, In Uncertainity in Artificial Intelligence (UAI) (oral presentation, 9% Acceptance rate). [13] R. Shah, R. Iyer and S. Chaudhuri, Object Mining for Large Video Data, In British Machine Vision Conference (BMVC) (selected for oral presentation - 8% Acceptance rate).

3 Workshops Under Submission Professional Activities Programming Skills Relevant Courses [14] R. Iyer and J. Bilmes, Near Optimal algorithms for constrained submodular programs with discounted cooperative costs, In NIPS Workshop on Discrete Optimization in Machine Learning (DISCML) [15] R. Iyer, S. Jegelka and J. Bilmes, Mirror Descent-Like Algorithms for Submodular Optimization, In NIPS Workshop on Discrete Optimization in Machine Learning (DISCML), [16] R. Iyer and T. Möller, A spatial domain optimization of sampling point-set, MITACS Globalink Research Symposium [17] R. Iyer and J. Bilmes, Difference of Submodular Optimization: Unifying Algorithms and Applications. [18] R. Iyer, J. Bilmes, Submodular Optimization subject to Submodular Constraints: Unifying Algorithms, Theoretical Results and Applications. [19] R. Iyer, S. Jegelka and J. Bilmes, Submodular Function Optimization: Unifying Combinatorial Algorithms and Parameters of Complexity. [20] R. Iyer and J. Bilmes, Polyhedral Connections Between Submodularity and Concavity. [21] K. Wei, R. Iyer and J. Bilmes, Submodularity in Data Subset Selection and Active Learning. [22] R. Bairi, R. Iyer, J. Bilmes and G. Ramakrishnan, Learning Submodular Functions for Summarizing DAG-Structured Topic Hierarchies. Reviewer for Journal of Machine Learning Research (JMLR) Reviewer for Journal of Discrete Applied Mathematics (DAM). Reviewer for International Conference of Machine Learning (ICML) , 2014, and Reviewer for Uncertainty in Artificial Intelligence (UAI) Reviewer for Neural Information Processing Systems (NIPS) and C/C++, C#, MATLAB, Java, Python, Visual Basic, Linux bash scripting, Windows shell scripting, Latex, Microsoft Office, Windows, Linux and Mac OSX. Submodular Optimization, Network Optimization, Convex Optimization (I and II), Information Theory (I and II), Probabilistic Graphical Models, Stochastic Processes, Probability and Random Variables, Machine Learning, Computer Vision, Image Processing, Speech Recognition. Talks Invited Talks International Symposium on Mathematical Programming (ISMP), Pittsburg - July, 2015 (Session on Submodular Optimization). UW-MSR Joint Machine Learning Symposium, Redmond (invited spotlight) - Feb General Electric (GE), August, University of Washington Yahoo! Machine Learning Lunch - May, 2014 University of Washington, Trends in Optimization (TOPS) seminar - May, Microsoft Research, Bangalore, January, Indian Institute of Science (IISc), January, Indian Institute of Technology, Bombay (IIT-B), March, 2013 and Feb Indian Institute of Technology, Gandhinagar (IIT-GN), Feb Conference and Workshop Talks Neural Information Processing Systems (NIPS) International Conference on Machine Learning (ICML) Uncertainty in Artificial Intelligence (UAI) Uncertainty in Artificial Intelligence (UAI) Workshop on Discrete Optimization in Machine Learning (DISCML) MITACS Globalink Research Symposium

4 Teaching Experience Selected Projects Teaching Assistant for EE-596, Submodular Functions and Optimization, April - June, 2014 at University of Washington, Seattle. Teaching Assistant for EE-215, Introduction to Electronics, Sep - Dec 2011 at University of Washington, Seattle. Undergraduate Teaching Assistant for IC-102, Statistics and Probability, July - Dec 2010 at IIT-Bombay. SubTK: A scalable C++ toolkit for large and massive scale submodular optimization Joint work with Kai Wei, Yuzong Liu, Hui Lin and Sebastian Tschiatchek Provided the first general purpose C++ toolkit for large scale submodular function optimization, which includes a large class of algorithms and commonly used submodular functions. Includes several memoization tricks to speed up the algorithms. Algorithms scale to massive datasets involving ground set sizes of several million instances. Includes implementation for several summarization (document/image) and data selection applications. Unifying and scalable framework for submodular optimization Joint work with Stefanie Jegelka and Jeff Bilmes. Designed a unifying framework of scalable algorithms for a large class of submodular optimization problems, using semigradients. Won the best paper award at ICML Scaling Submodular maximization to massive datasets Proposed a multistage algorithmic framework, for scaling several real world problems to massive datasets. Demonstrated theoretical and empirical results on several real world problems. Presented this work at ICML Document Summarization Joint work with Hui Lin, Kai Wei and Jeff Bilmes. Studied the role of several scalable algorithms (using submodular functions) for document summarization on DUC , acheiving near state of the art on these corpora. Image Collection Summarization Joint work with Sebastian Tschiatchek and Jeff Bilmes. Worked on the problem of finding the most representative images (canonical views) from a collection of images, by learning mixtures of submodular functions. Presented this work at NIPS Training Data Subset Selection Addressed the problem of finding a subset (coreset) of a large training data corpus, such that training on the smaller corpus acheives comparable accuracies to the entire data. Formulated this as a submodular optimization problem, and empirically tested this on large speech corpora, and machine learning datasets. Submodular Data Selection and Active Learning Investigated algorithms for active learning, using submodular functions to simultaneously model diversity (or coverage) and information (or uncertainty). Showed how these algorithms beat state of the art uncertainty sampling algorithms for text classification. Submitted this work to ICML Limited vocabulary corpus selection Joint work with Yuzong Liu and Jeff Bilmes. Worked on the problem of finding a limited vocabulary corpus for speech recognition, with rich accoustic characteristics. Posed this as a submodular optimization problem, and tested it on Switchboard and Fisher.

5 Feature subset selection Joint work with Jeff Bilmes. Posed the problem of feature subset selection as a submodular optimization problem, with good empirical performance. Active Learning and Active Featuring Joint work with Chris Meek, Max Chickering and Patrice Simard. Investigated algorithms for joint active learning and featuring, in text classification problems. Object Mining in videos Joint work with Ronak Shah and Subhasis Chaudhuri. Worked on object mining on large scale videos (video-google) where in we can efficiently search for all instances of an object appearing in a large video, given a single instance of that object Proposed a novel graph based method to store and represent an object dictionary for large videos. Extra Curricular Activities References Volunteering at the Vedic Cultural Center (VCC) at Sammamish, AnandaMela-2012/13/14 at Redmond, Flavors of India-2012/13/14 at Bellevue. Cofounder and Core group member of the P2P Stude Club, IIT Bombay, aimed at improving the Academics by promoting healthy lifestyle, values and Skills for Action. Organized regular seminars by distinguished speakers and two workshops on programming/computationsl skills. Certified Pranik Healer. Biking, Hiking, Squash and Meditation. Prof. Jeff Bilmes, Professor, Department of EE, University of Washington, Seattle (bilmes@uw.edu) Prof. Andreas Krause, Assistant Professor, CSE Department, ETH Zurich (krausea@eth.ch) Dr. Matthai Phillipose, Researcher, Microsoft Research, Redmond (matthaip@microsoft.com) Dr. Max Chickering, Researcher, Microsoft Research, Redmond (dmax@microsoft.com) Prof. Ganesh Ramakrishnan, Associate Professor, Department of CSE, IIT Bombay (ganesh@cse.iitb.ac.in) Prof. Subhasis Chaudhuri, Professor, Department of EE, IIT Bombay (sc@ee.iitb.ac.in) Prof. Maryam Fazel, Assistant Professor, Department of EE, University of Washington, Seattle (mfazel@uw.edu)