World's Best Scientists 2026 revealed!
Tim Kraska

Tim Kraska

D-Index & Metrics

Computer Science

D-Index
57
Citations
13420
World Ranking
3825
National Ranking
1815

Research.com Recognitions

  • 2017 - Fellow of Alfred P. Sloan Foundation

Overview

Tim Kraska is affiliated with MIT in the United States and specializes in computer science, particularly in areas related to data management and algorithms. Their research spans numerous subfields, including computer networks and communications, artificial intelligence, signal processing, information systems, and computer vision and pattern recognition.

The scientist has made significant contributions in several main topics such as data management and algorithms, advanced database systems and queries, cloud computing and resource management, data stream mining techniques, advanced data storage technologies, distributed systems and fault tolerance, and algorithms and data compression.

Tim Kraska's publication record includes papers published in well-known venues. Recent papers include:

  • "Tsunami," 2020, Proceedings of the VLDB Endowment
  • "The Seattle Report on Database Research," 2020, ACM SIGMOD Record
  • "ARDA," 2020, Proceedings of the VLDB Endowment
  • "Flow-loss," 2021, Proceedings of the VLDB Endowment
  • "Robust Query Driven Cardinality Estimation under Changing Workloads," 2023, Proceedings of the VLDB Endowment

Frequent coauthors that have worked with Tim Kraska include:

  • Ryan Marcus (18 joint publications)
  • Andreas Kipf (17 joint publications)
  • Samuel Madden (12 joint publications)
  • Mohammad Alizadeh (9 joint publications)
  • Kapil Vaidya (9 joint publications)

The scientist publishes regularly in several prominent venues, with the highest number of publications in the following:

  • arXiv (Cornell University) - 30 publications
  • Proceedings of the VLDB Endowment - 22 publications
  • ACM SIGMOD Record - 5 publications
  • Communications of the ACM - 2 publications
  • 2022 IEEE 38th International Conference on Data Engineering (ICDE) - 2 publications

Tim Kraska has also contributed to book publications, including one published by Springer Science+Business Media titled "Heterogeneous Data Management, Polystores, and Analytics for Healthcare" in 2021.

In recognition of their contributions, Tim Kraska was awarded the Alfred P. Sloan Foundation Fellowship in 2017.

Best Publications

  • The Case for Learned Index Structures

    Tim Kraska;Alex Beutel;Ed H. Chi;Jeffrey Dean

  • CrowdDB: answering queries with crowdsourcing

    Michael J. Franklin;Donald Kossmann;Tim Kraska;Sukriti Ramesh

  • CrowdER: crowdsourcing entity resolution

    Jiannan Wang;Tim Kraska;Michael J. Franklin;Jianhua Feng

  • Building a database on S3

    Matthias Brantner;Daniela Florescu;David Graf;Donald Kossmann

  • MLbase: A Distributed Machine-learning System

    Tim Kraska;Ameet Talwalkar;John C. Duchi;Rean Griffith

  • ALEX: An Updatable Adaptive Learned Index

    Jialin Ding;Umar Farooq Minhas;Jia Yu;Chi Wang

  • An evaluation of alternative architectures for transaction processing in the cloud

    Donald Kossmann;Tim Kraska;Simon Loesing

  • Consistency rationing in the cloud: pay only when it matters

    Tim Kraska;Martin Hentschel;Gustavo Alonso;Donald Kossmann

  • Neo: a learned query optimizer

    Ryan Marcus;Parimarjan Negi;Hongzi Mao;Chi Zhang

  • MDCC: multi-data center consistency

    Tim Kraska;Gene Pang;Michael J. Franklin;Samuel Madden

  • Learning Multi-Dimensional Indexes

    Vikram Nathan;Jialin Ding;Mohammad Alizadeh;Tim Kraska

  • How is the weather tomorrow?: towards a benchmark for the cloud

    Carsten Binnig;Donald Kossmann;Tim Kraska;Simon Loesing

  • Leveraging transitive relations for crowdsourced joins

    Jiannan Wang;Guoliang Li;Tim Kraska;Michael J. Franklin

  • Superneurons: dynamic GPU memory management for training deep neural networks

    Linnan Wang;Jinmian Ye;Yiyang Zhao;Wei Wu

  • MLI: An API for Distributed Machine Learning

    Evan R. Sparks;Ameet Talwalkar;Virginia Smith;Jey Kottalam

  • VizML: A Machine Learning Approach to Visualization Recommendation

    Kevin Hu;Michiel A. Bakker;Stephen Li;Tim Kraska

  • FITing-Tree: A Data-aware Index Structure

    Alex Galakatos;Michael Markovitch;Carsten Binnig;Rodrigo Fonseca

  • The end of slow networks: it's time for a redesign

    Carsten Binnig;Andrew Crotty;Alex Galakatos;Tim Kraska

  • Sherlock: A Deep Learning Approach to Semantic Data Type Detection

    Madelon Hulsebos;Kevin Hu;Michiel Bakker;Emanuel Zgraggen

  • Automating model search for large scale machine learning

    Evan R. Sparks;Ameet Talwalkar;Daniel Haas;Michael J. Franklin

  • SageDB: A Learned Database System

    Tim Kraska;Mohammad Alizadeh;Alex Beutel;Ed H. Chi

  • Bao: Learning to Steer Query Optimizers.

    Ryan Marcus;Parimarjan Negi;Hongzi Mao;Nesime Tatbul

Frequent Co-Authors

Michael J. Franklin
Michael J. Franklin University of Chicago
Donald Kossmann
Donald Kossmann Microsoft (United States)
Eli Upfal
Eli Upfal Brown University
Ugur Cetintemel
Ugur Cetintemel Brown University
Michael I. Jordan
Michael I. Jordan University of California, Berkeley

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