World's Best Scientists 2026 revealed!

D-Index & Metrics

Computer Science

D-Index
45
Citations
27790
World Ranking
6971
National Ranking
3050

Overview

Frank McSherry is affiliated with Materialize, Inc. in the United States and works primarily in the field of computer science, with a strong focus on computer networks and communications. Their research covers various advanced topics including database systems, data storage technologies, scientific computing, distributed and parallel computing, cloud computing, and data management.

Their publication record spans several key venues in the field, including:

  • arXiv (Cornell University)
  • Proceedings of the VLDB Endowment
  • ACM SIGMOD Record
  • The VLDB Journal

Frank McSherry's recent research contributions include work on incremental computation and dataflows, with notable papers such as:

  • "DBSP: Automatic Incremental View Maintenance for Rich Query Languages" (2023, Proceedings of the VLDB Endowment)
  • "Shared arrangements" (2020, Proceedings of the VLDB Endowment)
  • "DBSP: Incremental Computation on Streams and Its Applications to Databases" (2024, ACM SIGMOD Record)
  • "DBSP: automatic incremental view maintenance for rich query languages" (2025, The VLDB Journal)
  • "Shared Arrangements: practical inter-query sharing for streaming dataflows" (2020, arXiv (Cornell University))

The scientist frequently collaborates with several coauthors, including:

  • Mihai Budiu
  • Leonid Ryzhyk
  • Val Tannen
  • Tej Chajed
  • Andrea Lattuada

Frank McSherry's research areas can be summarized as:

  • Advanced Database Systems and Queries
  • Advanced Data Storage Technologies
  • Scientific Computing and Data Management
  • Distributed and Parallel Computing Systems
  • Cloud Computing and Resource Management
  • Data Management and Algorithms
  • Distributed Systems and Fault Tolerance

The profile shows a consistent engagement with topics related to streaming data, incremental view maintenance, and practical methods for sharing computational arrangements across queries, which reflects an interest in improving data processing systems and efficiency in distributed environments.

Best Publications

  • Calibrating noise to sensitivity in private data analysis

    Cynthia Dwork;Frank Mcsherry;Kobbi Nissim;Adam Smith

  • Our data, ourselves : Privacy via distributed noise generation

    Cynthia Dwork;Krishnaram Kenthapadi;Frank Mcsherry;Ilya Mironov

  • Mechanism Design via Differential Privacy

    F. McSherry;K. Talwar

  • Privacy integrated queries: an extensible platform for privacy-preserving data analysis

    Frank McSherry

  • Naiad: a timely dataflow system

    Derek G. Murray;Frank McSherry;Rebecca Isaacs;Michael Isard

  • Practical privacy: the SuLQ framework

    Avrim Blum;Cynthia Dwork;Frank McSherry;Kobbi Nissim

  • Spectral Graph Theory and its Applications

    D.A. Spielman

  • Spectral partitioning of random graphs

    F. McSherry

  • Differentially private recommender systems: Building privacy into the Netflix Prize contenders

    Frank McSherry;Ilya Mironov

  • Fast computation of low-rank matrix approximations

    Dimitris Achlioptas;Frank Mcsherry

  • Privacy, accuracy, and consistency too: a holistic solution to contingency table release

    Boaz Barak;Kamalika Chaudhuri;Cynthia Dwork;Satyen Kale

  • A decentralized algorithm for spectral analysis

    David Kempe;Frank McSherry

  • A Simple and Practical Algorithm for Differentially Private Data Release

    Moritz Hardt;Katrina Ligett;Frank Mcsherry

  • On profit-maximizing envy-free pricing

    Venkatesan Guruswami;Jason D. Hartline;Anna R. Karlin;David Kempe

  • Toward privacy in public databases

    Shuchi Chawla;Cynthia Dwork;Frank McSherry;Adam Smith

  • Scalability! but at what cost?

    Frank McSherry;Michael Isard;Derek G. Murray

  • Spectral analysis of data

    Yossi Azar;Amos Fiat;Anna Karlin;Frank McSherry

  • On spectral learning of mixtures of distributions

    Dimitris Achlioptas;Frank McSherry

  • Differential dataflow

    Frank McSherry;Derek Murray;Rebecca Isaacs;Michael Isard

  • Sampling Techniques for Kernel Methods

    Dimitris Achlioptas;Frank Mcsherry;Bernhard Schölkopf

  • Differentially private recommender systems

    Frank McSherry;Ilya Mironov

Frequent Co-Authors

Cynthia Dwork
Cynthia Dwork Harvard University
Kunal Talwar
Kunal Talwar Apple (United States)
Michael Isard
Michael Isard Google (United States)
Dimitris Achlioptas
Dimitris Achlioptas National and Kapodistrian University of Athens
Ilya Mironov
Ilya Mironov Google (United States)
Martín Abadi
Martín Abadi Google (United States)
Aaron Roth
Aaron Roth University of Pennsylvania
Mark S. Manasse
Mark S. Manasse Microsoft (United States)
Kobbi Nissim
Kobbi Nissim Georgetown University
Anna R. Karlin
Anna R. Karlin University of Washington

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