My research focusses on two inter-related themes:

1. Distributed human evaluations: Distributed human evaluations are integral to various applications where a set of items is assessed by a group of individuals, each person evaluating only a subset of the items and each item being evaluated by only a handful of individuals. This setup is commonly seen in domains such as scientific peer review, hiring and promotion processes, academic admissions, crowdsourcing initiatives, healthcare assessments, judicial verdicts, and online rating and recommendation systems, among others. The decentralization of evaluations, however, often leads to a host of challenges including instances of fraud, subjectivity, miscalibration, biases, breaches of privacy, and operational inefficiencies.
2. Research on research Our research also focuses on problems in scientific research itself and its evaluations. The challenges within this domain hold substantial consequences. They impact the allocation of billions of dollars in annual grant awards, influence the trajectories of researchers' careers due to the "rich-gets-richer" phenomenon in academia, and can detrimentally affect the public's perception of science.

• Vulnerability of Text-Matching in ML/AI Conference Reviewer Assignments to Collusions
  Jhih-Yi (Janet) Hsieh, Aditi Raghunathan, Nihar Shah
  
  
• Causal Effect of Group Diversity on Redundancy and Coverage in Peer-Reviewing
  Navita Goyal, Ivan Stelmakh, Nihar Shah, Hal Daumé III
  
  
• Enhancing Peer Review in Astronomy: A Machine Learning and Optimization Approach to Reviewer Assignments for ALMA
  John M. Carpenter, Andrea Corvillón, and Nihar Shah
  
  
• Benchmarking Fraud Detectors on Private Graph Data
  Alexander Goldberg, Giulia Fanti, Nihar Shah, Steven Wu
  
  
• Usefulness of LLMs as an Author Checklist Assistant for Scientific Papers: NeurIPS'24 Experiment
  Alexander Goldberg, Ihsan Ullah, Thanh Gia Hieu Khuong, Benedictus Kent Rachmat, Zhen Xu, Isabelle Guyon, Nihar Shah
  
  
• What Can Natural Language Processing Do for Peer Review?
  Ilia Kuznetsov, Osama Mohammed Afzal, Koen Dercksen, Nils Dycke, Alexander Goldberg, Tom Hope, Dirk Hovy, Jonathan K. Kummerfeld, Anne Lauscher, Kevin Leyton-Brown, Sheng Lu, Mausam, Margot Mieskes, Aurélie Névéol, Danish Pruthi, Lizhen Qu, Roy Schwartz, Noah A. Smith, Thamar Solorio, Jingyan Wang, Xiaodan Zhu, Anna Rogers, Nihar Shah, Iryna Gurevych
  
  
• A Randomized Controlled Trial on Anonymizing Reviewers to Each Other in Peer Review Discussions
  Charvi Rastogi, Xiangchen Song, Zhijing Jin, Ivan Stelmakh, Hal Daumé III, Kun Zhang, and Nihar Shah
  
  
• On the Detection of Reviewer-Author Collusion Rings From Paper Bidding
  Steven Jecmen, Nihar Shah, Fei Fang, and Leman Akoglu
  
  
• Peer Reviews of Peer Reviews: A Randomized Controlled Trial and Other Experiments
  
  Alexander Goldberg, Ivan Stelmakh, Kyunghyun Cho, Alice Oh, Alekh Agarwal, Danielle Belgrave, and Nihar Shah
  Blog
  
  
• Testing for Reviewer Anchoring in Peer Review: A Randomized Controlled Trial
  
  Ryan Liu, Steven Jecmen, Vincent Conitzer, Fei Fang, Nihar B. Shah
  
  
• ReviewerGPT? An Exploratory Study on Using Large Language Models for Paper Reviewing
  
  Ryan Liu, Nihar B. Shah
  
  
• A Gold Standard Dataset for the Reviewer Assignment Problem
  
  Ivan Stelmakh, John Wieting, Graham Neubig, Nihar B. Shah
  
  
• Assisting Human Decisions in Document Matching
  
  Joon Sik Kim, Valerie Chen, Danish Pruthi, Nihar B. Shah, Ameet Talwalkar
  Transactions on Machine Learning Research 2023
  
  
• The Role of Author Identities in Peer Review
  
  Nihar B. Shah
  PLOS ONE, 2023
  
  
• Batching of Tasks by Users of Pseudonymous Forums: Anonymity Compromise and Protection
  
  Alexander Goldberg, Giulia Fanti, Nihar B. Shah
  ACM SIGMETRICS 2023
  
  
• How do Authors’ Perceptions of their Papers Compare with Co-authors’ Perceptions and Peer-review Decisions?
  
  Charvi Rastogi, Ivan Stelmakh, Alina Beygelzimer, Yann N. Dauphin, Percy Liang, Jennifer Wortman Vaughan, Zhenyu Xue, Hal Daumé III, Emma Pierson, and Nihar B. Shah
  PLOS ONE 2024
  
  
• Near-Optimal Reviewer Splitting in Two-Phase Paper Reviewing and Conference Experiment Design
  
  Steven Jecmen, Hanrui Zhang, Ryan Liu, Fei Fang, Vincent Conitzer, and Nihar B. Shah
  HCOMP 2022
  Best Paper Honorable Mention
  
  
• Strategyproofing Peer Assessment via Partitioning: The Price in Terms of Evaluators' Expertise
  
  Komal Dhull, Steven Jecmen, Pravesh Kothari, Nihar B. Shah
  HCOMP 2022
  Code
  
  
• A Dataset on Malicious Paper Bidding in Peer Review
  
  Steven Jecmen, Minji Yoon, Vincent Conitzer, Nihar B. Shah, Fei Fang
  TheWebConf 2023
  
  
• Counterfactual Evaluation of Peer-Review Assignment Policies
  
  Martin Saveski, Steven Jecmen, Nihar Shah, Johan Ugander
  NeurIPS 2023
  
  
• To ArXiv or not to ArXiv: A Study Quantifying Pros and Cons of Posting Preprints Online
  
  Charvi Rastogi, Ivan Stelmakh, Xinwei Shen, Marina Meila, Federico Echenique, Shuchi Chawla, Nihar B. Shah
  Peer review congress (abstract), 2022
  
  
• Cite-seeing and Reviewing: A Study on Citation Bias in Peer Review
  
  Ivan Stelmakh, Charvi Rastogi, Ryan Liu, Shuchi Chawla, Federico Echenique, Nihar B. Shah
  PLOS ONE, 2023
  
  
• No Rose for MLE: Inadmissibility of MLE for Evaluation Aggregation Under Levels of Expertise
  
  Charvi Rastogi, Ivan Stelmakh, Nihar B. Shah, Sivaraman Balakrishnan
  ISIT 2022
  
  
• Integrating Rankings into Quantized Scores in Peer Review
  
  Yusha Liu, Yichong Xu, Nihar B. Shah, Aarti Singh
  TMLR 2022
  Workshop on ML Evaluation Standards at ICLR 2022
  Outstanding Paper Award and People's Choice Award
  
  
• Tradeoffs in Preventing Manipulation in Paper Bidding for Reviewer Assignment
  
  Steven Jecmen, Nihar B. Shah, Fei Fang, Vincent Conitzer
  Workshop on ML Evaluation Standards at ICLR 2022
  Outstanding Paper Award
  
  
• Calibration with Privacy in Peer Review
  
  Wenxin Ding, Gautam Kamath, Weina Wang, and Nihar B. Shah
  ISIT 2022
  
  
• Uncovering Latent Biases in Text: Method and Application to Peer Review
  
  Emaad Manzoor and Nihar B. Shah
  AAAI 2021.
  Code
  
  
• Mitigating Manipulation in Peer Review via Randomized Reviewer Assignments
  
  Steven Jecmen, Hanrui Zhang, Ryan Liu, Nihar B. Shah, Vincent Conitzer, and Fei Fang
  NeurIPS 2020.
  Code
  
  
• Prior and Prejudice: The Novice Reviewers' Bias against Resubmissions in Conference Peer Review.
  
  Ivan Stelmakh, Nihar B. Shah, Aarti Singh and Hal Daumé III
  CSCW 2021.
  
  
• A Large Scale Randomized Controlled Trial on Herding in Peer-Review Discussions.
  
  Ivan Stelmakh, Charvi Rastogi, Nihar B. Shah, Aarti Singh and Hal Daumé III
  PLOS ONE 2023.
  
  
• A Novice-Reviewer Experiment to Address Scarcity of Qualified Reviewers in Large Conferences
  
  Ivan Stelmakh, Nihar B. Shah, Aarti Singh and Hal Daumé III
  AAAI 2021.
  Reviewer guidelines
  
  
• Catch Me if I Can: Detecting Strategic Behaviour in Peer Assessment
  
  Ivan Stelmakh, Nihar B. Shah and Aarti Singh
  AAAI 2021.
  Data
  
  
• Debiasing Evaluations that are Biased by Evaluations
  
  Jingyan Wang, Ivan Stelmakh, Yuting Wei and Nihar B. Shah
  AAAI 2021.
  
  
• On the Privacy-Utility Tradeoff in Peer-Review Data Analysis
  
  Wenxin Ding, Nihar B. Shah, and Weina Wang
  AAAI Privacy-Preserving Artificial Intelligence (PPAI-21) workshop 2021.
  
  
• On Testing for Biases in Peer Review
  
  Ivan Stelmakh, Nihar B. Shah and Aarti Singh
  NeurIPS 2019.
  
  
• A SUPER* Algorithm to Optimize Paper Bidding in Peer Review
  
  Tanner Fiez, Nihar B. Shah and Lillian Ratliff
  UAI 2020
  Code
  
  
• PeerReview4All: Fair and Accurate Reviewer Assignment in Peer Review
  
  Ivan Stelmakh, Nihar B. Shah and Aarti Singh
  JMLR 2021 (shorter version at ALT 2019)
  Code for the PeerReview4All paper-reviewer assignment algorithm
  Dataset
  
  
• Your 2 is My 1, Your 3 is My 9: Handling Arbitrary Miscalibrations in Ratings
  
  Jingyan Wang and Nihar B. Shah
  AAMAS 2019
  My PhD student Jingyan Wang won the Best Student Paper Award at AAMAS 2019
  Nominated for the Best Paper Award
  
  
• Loss Functions, Axioms, and Peer Review
  
  Ritesh Noothigattu, Nihar B. Shah and Ariel Procaccia
  Journal of Artificial Intelligence Research (JAIR), 2021.
  Code
  COMSOC 2021 Best Poster runner up
  
  
• On Strategyproof Conference Review
  
  Yichong Xu, Han Zhao, Xiaofei Shi and Nihar B. Shah
  IJCAI 2019
  Code and data
  
  
• Design and Analysis of the NIPS 2016 Review Process
  
  Nihar B. Shah, Behzad Tabibian, Krikamol Muandet, Isabelle Guyon and Ulrike von Luxburg
  Journal of Machine Learning Research 2018
  
  
  PAST RESEARCH ON CROWDSOURCING
  
  
• Allocation Schemes in Analytic Evaluation: Applicant-Centric Holistic or Attribute-Centric Segmented?
  
  Jingyan Wang, Carmel Baharav, Nihar B. Shah, Anita Williams Woolley, and R Ravi
  HCOMP 2022
  
  
• Approval Voting and Incentives for Crowdsourcing
  
  Nihar B. Shah and Dengyong Zhou
  ACM Transactions on Economics and Computation, June 2020.
  Video of ICML talk  Slides Dataset
  
  
• The Square Root Agreement Rule for Incentivizing Objective Feedback in Online Platforms
  
  Vijay Kamble, Nihar Shah, David Marn, Abhay Parekh, Kannan Ramachandran
  Management Science, July 2022
  
  
  
• A Heuristic for Statistical Seriation
  
  Komal Dhull, Jingyan Wang, Nihar B. Shah, Yuanzhi Li, and R. Ravi
  UAI 2021.
  
  
• Stretching the Effectiveness of MLE from Accuracy to Bias for Pairwise Comparisons
  
  Jingyan Wang, Nihar B. Shah and R. Ravi
  AISTATS 2020.
  
  
• Two-Sample Testing with Ranked Preference Data and the Role of Modeling Assumptions
  
  Charvi Rastogi, Sivaraman Balakrishnan, Nihar B. Shah and Aarti Singh
  ISIT 2020.
  Video        Slides
  
  
• A Permutation-based Model for Crowd Labeling: Optimal Estimation and Robustness
  
  Nihar B. Shah, Sivaraman Balakrishnan and Martin J. Wainwright
  IEEE Transactions on Information Theory 2020
  Code for the WAN and the OBI-WAN estimators
  Dataset
  
  
• Active Ranking from Pairwise Comparisons and when Parametric Assumptions Do Not Help
  
  Reinhard Heckel, Nihar B. Shah, Kannan Ramchandran and Martin J. Wainwright
  Annals of Statistics 2019
  Code
  
  
• Low Permutation-rank Matrices: Structural Properties and Noisy Completion
  
  Nihar B. Shah, Sivaraman Balakrishnan and Martin J. Wainwright
  JMLR 2019
  
  
• Feeling the Bern: Adaptive Estimators for Bernoulli Probabilities of Pairwise Comparisons
  
  Nihar B. Shah, Sivaraman Balakrishnan and Martin J. Wainwright
  IEEE Transactions on Information Theory (Shorter version at ISIT 2016).
  Code for the CRL estimator
  
  
• Simple, Robust and Optimal Ranking from Pairwise Comparisons
  Nihar B. Shah and Martin J. Wainwright
  Journal of Machine Learning Research, 2018.
  Dataset
  
  
• Stochastically Transitive Models for Pairwise Comparisons: Statistical and Computational Issues
  Nihar B. Shah, Sivaraman Balakrishnan, Adityanand Guntuboyina and Martin J. Wainwright
  IEEE Transactions on Information Theory 2017 (Shorter version at ICML 2016).
  
  
• No Oops, You Won't Do It Again: Mechanisms for Self-correction in Crowdsourcing
  
  Nihar B. Shah and Dengyong Zhou
  ICML 2016.
  
  
• Estimation from Pairwise Comparisons: Sharp Minimax Bounds with Topology Dependence
  
  Nihar B. Shah, Sivaraman Balakrishnan, Joseph Bradley, Abhay Parekh, Kannan Ramchandran, and Martin J. Wainwright
  The Journal of Machine Learning Research, 2016.
  Dataset for cardinal vs. ordinal  Dataset for pairwise comparison topologies
  
  
• Double or Nothing: Multiplicative Incentive Mechanisms for Crowdsourcing
  
  Nihar B. Shah and Dengyong Zhou
  Journal of Machine Learning Research 2016 (shorter version at NeurIPS 2015).
  Dataset
  
  
• Parametric Prection from Parametric Agents
  
  Yuan Luo, Nihar B. Shah, Jianwei Huang, Jean Walrand
  Operations Research, 2017.
  
  
  
• Truth Serums for Massively Crowdsourced Evaluation Tasks
  
  Vijay Kamble, Nihar Shah, David Marn, Abhay Parekh, Kannan Ramachandran
  SCUGC 2015: The 5th Workshop on Social Computing and User-Generated Content.
  
  
  
• On the Impossibility of Convex Inference in Human Computation
  
  Nihar B. Shah and Dengyong Zhou
  AAAI, Austin, Jan. 2015.
  
  
• A Case for Ordinal Peer-evaluation in MOOCs
  
  Nihar B. Shah, Joseph Bradley, Abhay Parekh, Martin J. Wainwright, Kannan Ramchandran
  Neural Information Processing Systems (NeurIPS): Workshop on Data Driven Education, Lake Tahoe, Dec. 2013.
  
  
• Regularized Minimax Conditional Entropy for Crowdsourcing
  
  Dengyong Zhou, Qiang Liu, John Platt, Christopher Meek, and Nihar B. Shah
  Dec. 2014.
  
  
  
  PAST RESEARCH ON DISTRIBUTED STORAGE
  
  (* indicates equal contribution)
  
  
• A Piggybacking Design Framework for Read-and Download-efficient Distributed Storage Codes
  
  K. V. Rashmi, Nihar B. Shah and Kannan Ramchandran
  IEEE Transactions on Information Theory, 2017.
  Slides from conference (ISIT) presentation
  
  
• When Do Redundant Requests Reduce Latency ?
  
  Nihar B. Shah, Kangwook Lee and Kannan Ramchandran
  IEEE Transactions on Communication, Feb. 2016.
  Slides
  
  
• The MDS Queue: Analysing Latency Performance of Codes
  
  Kangwook Lee, Nihar B. Shah, Longbo Huang and Kannan Ramchandran
  IEEE Transactions on Information Theory, 2017.
  Addendum and Erratum
  
  
• Distributed Storage Codes with Repair-by-Transfer and Non-achievability of Interior Points on the Storage-Bandwidth Tradeoff
  
  Nihar B. Shah*, K. V. Rashmi*, P. Vijay Kumar and Kannan Ramchandran
  IEEE Transactions on Information Theory, March 2012.
  
  
• Optimal Exact-Regenerating Codes for Distributed Storage at the MSR and MBR Points via a Product-Matrix Construction
  
  K. V. Rashmi*, Nihar B. Shah* and P. Vijay Kumar
  IEEE Transactions on Information Theory, August 2011.
  IEEE Data Storage Best Paper and Best Student Paper Awards for the years 2011 & 2012.
  
  
• Interference Alignment in Regenerating Codes for Distributed Storage: Necessity and Code Constructions
  
  Nihar B. Shah*, K. V. Rashmi*, P. Vijay Kumar and Kannan Ramchandran
  IEEE Transactions on Information Theory, April 2012.
  
  
• On Minimizing Data-read and Download for Storage-Node Recovery
  
  Nihar B. Shah
  IEEE Communications Letters, 2013.
  Second place in the first ACM University Student Research Competition, 2013.
  
  
• Having Your Cake and Eating It Too: Jointly Optimal Codes for I/O, Storage and Network-bandwidth In Distributed Storage Systems
  
  KV Rashmi, Preetum Nakkiran, Jingyan Wang, Nihar B. Shah, and Kannan Ramchandran
  USENIX FAST, Santa Clara, Feb. 2015.
  Picked as the best paper of USENIX FAST 2015 by StorageMojo.
  
  
  
• Fundamental Limits on Communication for Oblivious Updates in Storage Networks
  
  Preetum Nakkiran, Nihar B. Shah, K. V. Rashmi
  IEEE GLOBECOM 2014, Dec. 2014.
  
  
• A "Hitchhiker's" Guide to Fast and Efficient Data Reconstruction in Erasure-coded Data Centers
  
  K. V. Rashmi, Nihar B. Shah, Dikang Gu, Hairong Kuang, Dhruba Borthakur, and Kannan Ramchandran
  ACM SIGCOMM, Aug 2014.
  
  
• One Extra Bit of Download Ensures Perfectly Private Information Retrieval
  
  Nihar B. Shah, K. V. Rashmi and Kannan Ramchandran
  ISIT 2014.
  
  
  
• A Solution to the Network Challenges of Data Recovery in Erasure-coded Distributed Storage Systems: A Study on the Facebook Warehouse Cluster
  
  K. V. Rashmi, Nihar B. Shah, Dikang Gu, Hairong Kuang, Dhruba Borthakur, and Kannan Ramchandran
  USENIX HotStorage, San Jose, Jun. 2013.
  
  
• Secret Sharing Across a Network with Low Communication Cost: Distributed Algorithm and Bounds
  
  Nihar B. Shah, K. V. Rashmi and Kannan Ramchandran
  IEEE International Symposium on Information Theory (ISIT), Istanbul, Jul. 2013.
  Slides  Poster
  
  
• Information-theoretically Secure Regenerating Codes for Distributed Storage
  
  Nihar B. Shah*, K. V. Rashmi*, K. Ramchandran, and P. Vijay Kumar
  IEEE Transactions on Information Theory 2017.
  
  
• Regenerating Codes for Errors and Erasures in Distributed Storage
  
  K. V. Rashmi*, Nihar B. Shah*, Kannan Ramchandran, and P. Vijay Kumar
  IEEE International Symposium on Information Theory (ISIT), Cambridge, Jul. 2012.
  Slides
  
  
• Enabling Node Repair in Any Erasure Code for Distributed Storage
  
  K. V. Rashmi*, Nihar B. Shah* and P. Vijay Kumar
  IEEE International Symposium on Information Theory (ISIT), St. Petersburg, Jul. 2011.
  
  
• A Flexible Class of Regenerating Codes for Distributed Storage
  
  Nihar B. Shah*, K. V. Rashmi*, and P. Vijay Kumar
  IEEE International Symposium on Information Theory (ISIT), Austin, Jun. 2010.
  
  
• Explicit and Optimal Exact-Regenerating Codes for the Minimum-Bandwidth Point in Distributed Storage
  
  K. V. Rashmi*, Nihar B. Shah*, P. Vijay Kumar, and Kannan Ramchandran
  IEEE International Symposium on Information Theory (ISIT), Austin, Jun. 2010.
  
  
• Explicit Codes Minimizing Repair Bandwidth for Distributed Storage    (the complete version on Arxiv)
  
  Nihar B. Shah*, K. V. Rashmi*, P. Vijay Kumar and Kannan Ramchandran
  IEEE Information Theory Workshop (ITW), Cairo, Jan. 2010.
  
  
• Explicit Construction of Optimal Exact Regenerating Codes for Distributed Storage
  
  K. V. Rashmi*, Nihar B. Shah*, P. Vijay Kumar and Kannan Ramchandran
  Allerton Conference on Control, Computing and Communication, Urbana-Champaign, Sep. 2009.
  
  
• Regenerating Codes for Distributed Storage Networks (invited)
  
  Nihar B. Shah*, K. V. Rashmi*, P. Vijay Kumar, and Kannan Ramchandran
  International Workshop on the Arithmetic of Finite Fields (WAIFI), Istanbul, Jun. 2010.
  
  
• Network Coding
  
  K. V. Rashmi*, Nihar B. Shah* and P. Vijay Kumar
  Resonance, vol. 15, no. 7, pp. 604-621., Jul. 2010.
  (Resonance is a journal of science education published by the Indian Academy of Sciences)
  .
• Distributed Storage System for Optimal Storage Space and Network Bandwidth Utilization and A Method Thereof
  
  K. V. Rashmi*, Nihar B. Shah* and P. Vijay Kumar
  US Patent, Nov 2011.
  

• UC Berkeley
  PhD in Electrical Engineering and Computer Sciences
  Advisors: Prof. Martin J. Wainwright and Prof. Kannan Ramchandran
  Other members of thesis committee: Prof. Christos Papadimitriou and Prof. Tom Griffiths
  
  
• Indian Institute of Science (IISc), Bangalore
  M.E. in Telecommunication
  Thesis: Minimizing Repair Bandwidth in Distributed Storage Systems
  Advisor: Prof. P. Vijay Kumar
  
  
• National Institute of Technology Karnataka, Surathkal
  B. Tech. in Electronics and Communication
  

• Young Alumnus Medal, Indian Institute of Science 2025
• Outstanding paper award and people's choice award, Workshop on ML Evaluation Standards at ICLR 2022
• HCOMP Best Paper Award Honorable Mention 2022
• JP Morgan Faculty Research Award 2022
• Google Research Scholar award 2021
• NSF CAREER award 2020-2025
• Best paper nomination at AAMAS 2019
• Mentored and co-authored Best Student Paper at AAMAS 2019 to my PhD student Jingyan Wang
• PhD thesis received David J. Sakrison Memorial Prize for a "truly outstanding piece of research" at EECS, UC Berkeley, May 2017
• Outstanding Graduate Student Instructor award at UC Berkeley, 2015-16
• Microsoft Research PhD Fellowship, 2014-2016.
• IEEE Data Storage Best Paper and Best Student Paper awards for years 2011 & 2012
• Second place in the first ACM University Student Research Competition, 2013.
• Berkeley Fellowship, 2011-13 (the most prestigious fellowship for incoming graduate students at UC Berkeley).
• Excellence Award for the academic year 2011-2012 at UC Berkeley.
• Prof. SVC Aiya Medal for the best master-of-engineering student in the ECE department at IISc, 2010.

• Scientific Integrity Chair, International Conference on Machine Learning 2025
• co-Editor-in-Chief, Transactions on Machine Learning Research, 2025 onwards
• Action Editor, Transactions on Machine Learning Research, 2022-2025
• Associate Program Chair, AAAI 2022
• Diversity and inclusion committee, IEEE Information Theory Society, 2020 onwards
  • Click here for the Report on 2021 Information Theory Society survey
• Program committee member/Senior PC member/Area Chair, AAAI, NeurIPS , ICML, IJCAI, AIStats, HCOMP.
• Co-chair, the NeurIPS 2014 workshop on Crowdsourcing and Machine-learning and the ICML 2014 workshop on Crowdsourcing and Human-computation.
• Reviewer, Proceedings of the National Academy of Sciences, Journal of Machine Learning Research, Journal of Artificial Intelligence Research, Annals of Statistics, Operations Research, Management Science, PLOS One, Proceedings of the IEEE, ACM Transactions on Economics and Computation, IEEE Transactions on Information Theory, IEEE Transactions on Signal Processing, IEEE Transactions on Communications, IEEE Transactions on Parallel and Distributed Systems, IEEE Communications Letters, IEEE Journal on Selected Areas in Communications, ACM Transactions on Intelligent Systems, NeurIPS, ICLR, ISIT, ACM EC, DISC, Netcod, Infocom, Globecom, ITW, MIT Press.

• Intern, Microsoft Research Redmond, May 2013 to August 2013 and May 2014 to August 2014
  Crowdsourcing algorithms.
  
  
• Project Associate, IISc-Infosys collaborative project, Bangalore, July 2010 to June 2011
  Algorithms for robust and efficient media content distribution networks.
  
  
• Member of Technical Staff, Adobe Systems, Bangalore, July 2007 to July 2008.
  Worked on Adobe Captivate, an automated e-learning authoring tool.