World's Best Scientists 2026 revealed!

D-Index & Metrics

Computer Science

D-Index
32
Citations
5102
World Ranking
13068
National Ranking
5262

Overview

Nihar B. Shah is affiliated with Carnegie Mellon University in the United States. Their primary field of study is Computer Science, with a focus spanning several subfields including Artificial Intelligence, Information Systems, Management Science and Operations Research, Computer Science Applications, and Economics and Econometrics.

Their research topics include:

  • Expert finding and Q&A systems
  • Mobile Crowdsensing and Crowdsourcing
  • Topic Modeling
  • Auction Theory and Applications
  • Privacy-Preserving Technologies in Data
  • Software Engineering Research
  • Game Theory and Voting Systems

Recent papers authored or co-authored by Nihar B. Shah cover a range of subjects in their areas of expertise. Notable publications include:

  • "Some Simple Economics of Stablecoins" (2022) in Annual Review of Financial Economics
  • "Challenges, experiments, and computational solutions in peer review" (2022) in Communications of the ACM
  • "A Permutation-Based Model for Crowd Labeling: Optimal Estimation and Robustness" (2020) in IEEE Transactions on Information Theory
  • "Approval Voting and Incentives in Crowdsourcing" (2020) in ACM Transactions on Economics and Computation
  • "Uncovering Latent Biases in Text: Method and Application to Peer Review" (2021) in Proceedings of the AAAI Conference on Artificial Intelligence

Nihar B. Shah frequently collaborates with several co-authors, including:

  • Ivan Stelmakh
  • Hal Daumé
  • Charvi Rastogi
  • Steven Jecmen
  • Fei Fang

The scholar commonly publishes in several venues, with a significant number of papers appearing in:

  • arXiv (Cornell University)
  • PLoS ONE
  • Proceedings of the AAAI Conference on Artificial Intelligence
  • Proceedings of the AAAI Conference on Human Computation and Crowdsourcing
  • Communications of the ACM

Best Publications

  • Optimal Exact-Regenerating Codes for Distributed Storage at the MSR and MBR Points via a Product-Matrix Construction

    K. V. Rashmi;N. B. Shah;P. V. Kumar

  • Optimal Exact-Regenerating Codes for Distributed Storage at the MSR and MBR Points via a Product-Matrix Construction

    K. V. Rashmi;Nihar B. Shah;P. Vijay Kumar

  • Explicit construction of optimal exact regenerating codes for distributed storage

    K. V. Rashmi;Nihar B. Shah;P. Vijay Kumar;Kannan Ramchandran

  • Distributed Storage Codes With Repair-by-Transfer and Nonachievability of Interior Points on the Storage-Bandwidth Tradeoff

    N. B. Shah;K. V. Rashmi;P. Vijay Kumar;K. Ramchandran

  • 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

  • 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

  • Interference Alignment in Regenerating Codes for Distributed Storage: Necessity and Code Constructions

    N. B. Shah;K. V. Rashmi;P. V. Kumar;K. Ramchandran

  • A "hitchhiker's" guide to fast and efficient data reconstruction in erasure-coded data centers

    Unknown

  • One Extra Bit of Download Ensures Perfectly Private Information Retrieval

    Nihar B. Shah;K. V. Rashmi;Kannan Ramchandran

  • When Do Redundant Requests Reduce Latency

    Nihar B. Shah;Kangwook Lee;Kannan Ramchandran

  • 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;Kannan Ramchandran

  • Interference Alignment in Regenerating Codes for Distributed Storage: Necessity and Code Constructions

    Nihar B. Shah;K. V. Rashmi;P. Vijay Kumar;Kannan Ramchandran

  • Simple, Robust and Optimal Ranking from Pairwise Comparisons

    Nihar B. Shah;Martin J. Wainwright

  • Having your cake and eating it too: jointly optimal erasure codes for I/O, storage and network-bandwidth

    K. V. Rashmi;Preetum Nakkiran;Jingyan Wang;Nihar B. Shah

  • Stochastically Transitive Models for Pairwise Comparisons: Statistical and Computational Issues

    Nihar B. Shah;Sivaraman Balakrishnan;Adityanand Guntuboyina;Martin J. Wainwright

  • Estimation from pairwise comparisons: sharp minimax bounds with topology dependence

    Nihar B. Shah;Sivaraman Balakrishnan;Joseph Bradley;Abhay Parekh

  • Information-Theoretically Secure Regenerating Codes for Distributed Storage

    Nihar B. Shah;K. V. Rashmi;P. Vijay Kumar

  • A Piggybacking Design Framework for Read-and Download-Efficient Distributed Storage Codes

    K. V. Rashmi;Nihar B. Shah;Kannan Ramchandran

  • Explicit codes minimizing repair bandwidth for distributed storage

    Nihar B. Shah;K. V. Rashmi;P. Vijay Kumar;Kannan Ramchandran

  • Double or nothing: multiplicative incentive mechanisms for crowdsourcing

    Nihar B. Shah;Dengyong Zhou

  • Regenerating codes for errors and erasures in distributed storage

    K. V. Rashmi;Nihar B. Shah;Kannan Ramchandran;P. Vijay Kumar

  • Double or nothing: multiplicative incentive mechanisms for Crowdsourcing

    Nihar B. Shah;Dengyong Zhou

  • 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

  • Regularized Minimax Conditional Entropy for Crowdsourcing

    Dengyong Zhou;Qiang Liu;John C. Platt;Christopher Meek

  • When do redundant requests reduce latency

    Nihar B. Shah;Kangwook Lee;Kannan Ramchandran

  • Overcoming Calibration Problems in Pattern Labeling with Pairwise Ratings: Application to Personality Traits

    Baiyu Chen;Sergio Escalera;Isabelle Guyon;Víctor Ponce-López;Víctor Ponce-López

  • Simple, Robust and Optimal Ranking from Pairwise Comparisons

    Nihar B. Shah;Martin J. Wainwright

Frequent Co-Authors

Kannan Ramchandran
Kannan Ramchandran University of California, Berkeley
P. Vijay Kumar
P. Vijay Kumar Indian Institute of Science
Aarti Singh
Aarti Singh Carnegie Mellon University
Dengyong Zhou
Dengyong Zhou Google (United States)
Hal Daumé
Hal Daumé University of Maryland, College Park
R. Ravi
R. Ravi Carnegie Mellon University
P.V. Kumar
P.V. Kumar Indian Institute of Science
Ariel D. Procaccia
Ariel D. Procaccia Harvard University
Vincent Conitzer
Vincent Conitzer Carnegie Mellon University

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