My research interests lie in the areas of statistics, machine learning, information theory and game theory, with a focus on "learning from people": How to elicit high-quality data from people? How to draw inferences from such data? I am particularly interested in dealing with issues of fairness, accuracy, and robustness. This is an exciting and challenging area of research that has many important applications including hiring, admissions, crowdsourcing, A/B testing, online ratings and recommendations, peer grading, and peer review. My research aims to address these important challenges at scale, in a principled and pragmatic manner. I am presently particularly excited about developing principled approaches towards improving the backbone of all scientific research: Peer Review! Here are some vignettes from my recent research:

• Bias: Many research communities ae debating single vs. double blind reviewing, with a FAQ "Where is the evidence of bias in reviewing in my community?'' We design statistical tests for biases in peer review, that accommodates the various idiosyncrasies of the peer review process. (link). We also design algorithms to test for biases in review text rather than just review scores as done previously (link).


• Miscalibration: Reviewers are often miscalibrated: some reviewer may be lenient and always provide scores greater than 5/10, while some other may be strict and never provide more than 5/10. If these biases are a priori unknown, how would can calibrate the reviewers (from say, just one review obtained per reviewer)? We design a novel randomized estimator that can handle arbitrary and even adversarial miscalibrations. This also leads to a surprising insight into the eternal debate between ratings and rankings (link).


• Reviewer assignment: Commonly employed algorithms for assigning reviewers to papers can be unfair to certain papers (e.g., to interdisciplinary or novel papers). We design an algorithm to assign reviewers to papers which guarantees fairness of assignment to all papers. Simultaneously, the algorithm guarantees statistical accuracy of the review procedure. This algorithm was used in ICML 2020 and performed very well on common metrics of evaluation (link).


• Subjectivity: It is common to see a handful of reviewers reject a highly novel paper, because they view extensive experiments more important than novelty, whereas the community as a whole would have embraced the novelty. We develop a novel method to mitigate such subjectivity by ensuring that every paper is judged by the same yardstick. We prove that surprisingly, this is the only method which meets three natural requirements. We also provide an empirical analysis on IJCAI 2017 (link).


• Lone-wolf strategic review: Conference peer review incentivizes reviewers to influence the final outcome for their own papers by manipulating the reviews they provide. We consider a framework to ensure peer review systems that are insulated from strategic manipulations. We present positive results in terms of an algorithm and an analysis on ICLR 2017 data, as well as negative results which demonstrate the challenges in this problem (link). Additionally, since program chairs may want to first test if such strategic behavior exists at a large scale (before deploying any mitigation techniques), we also design a statistical test to check for large-scale strategic behavior (link).


• Malicious coalitions: It has recently been discovered that in paper and grant peer review, some people form malicious coalitions. They try to get assigned, and then accept each other's papers. We design a randomized assignment algorithm such that the program chairs can cap the probability of any reviewer being assigned to any paper. It provably guarantees that the assignment is optimal (in expectation) subject to these randomization constraints (link). This algorithm is now available for use in the OpenReview.net conference management platform.


• Bidding: Many conferences ask reviewers to bid on papers they are interested to review. Such bidding is known to be highly skewed with a large number of papers getting zero or insufficient bids. We address this issue by exploiting primacy effects to design an algorithm that balances (i) amount of skew in the bids, and (ii) reviewer satisfaction. (link)


• Private data release: A major impediment towards research on peer review is the dearth of data. There is indeed a challenge in releasing any data publicly due to requirements on the anonymity of reviewers for each paper. We establish a framework for releasing certain kinds of peer-review data while preserving privacy of reviewer assignments. (link)


• Feedback: To evaluate the quality of peer reviews, one may be tempted to ask authors for their feedback. However, author feedback is often biased: positive if their paper was accepted and negative if not. We design algorithms to de-bias such feedback (link).


• Empirical analysis: In NeurIPS 2016 (link), ICML 2020 (link1, link2, link3); we also perform other empirical analyses in the aforementioned works.

• 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
  arxiv 2020.
  
  
• 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 (spotlight).
  
  
• 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
  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), accepted.
  Code
  
  
• 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
  
  
• 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
  
  
• 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 (to appear)
  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.
  
  
• 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
  
  
• 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
  
  
• Information-theoretically Secure Regenerating Codes for Distributed Storage
  
  Nihar B. Shah*, K. V. Rashmi*, and P. Vijay Kumar
  Globecom 2011.
  
  
• 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
  

Please visit the publications page.

• 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.

• 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.