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Tim Kraska

Tim Kraska

Massachusetts Institute of Technology · Electrical Engineering & Computer Science

Active 1999–2026

h-index61
Citations12.3k
Papers312102 last 5y
Funding$3.3M

Academic metrics are sourced from OpenAlex and public funding records; values may differ from Google Scholar.

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About

Tim Kraska is an Associate Professor at MIT in the Department of Electrical Engineering and Computer Science, specializing in Artificial Intelligence and Machine Learning. His research focuses on developing systems and techniques for AI and decision-making, combining intellectual traditions from computer science and electrical engineering to analyze and synthesize systems that interact with the external world through perception, communication, and action. His work involves learning, decision-making, and adaptation within changing environments, contributing to advancements in AI systems and their applications.

Research topics

  • Computer Science
  • Artificial Intelligence
  • Information Retrieval
  • Data Mining
  • Database
  • Machine Learning
  • Programming language
  • Parallel computing
  • Data science
  • World Wide Web

Selected publications

  • Bao: Making Learned Query Optimization Practical

    Proceedings of the 2022 International Conference on Management of Data · 2021 · 178 citations

    Senior authorCorresponding

    Recent efforts applying machine learning techniques to query optimization have shown few practical gains due to substantive training overhead, inability to adapt to changes, and poor tail performance. Motivated by these difficulties, we introduce Bao (the \underlineBa ndit \underlineo ptimizer). Bao takes advantage of the wisdom built into existing query optimizers by providing per-query optimization hints. Bao combines modern tree convolutional neural networks with Thompson sampling, a well-stu…

    DOI

  • Tsunami

    Proceedings of the VLDB Endowment · 2020 · 115 citations

    Senior authorCorresponding

    Filtering data based on predicates is one of the most fundamental operations for any modern data warehouse. Techniques to accelerate the execution of filter expressions include clustered indexes, specialized sort orders (e.g., Z-order), multi-dimensional indexes, and, for high selectivity queries, secondary indexes. However, these schemes are hard to tune and their performance is inconsistent. Recent work on learned multi-dimensional indexes has introduced the idea of automatically optimizing an…

    DOI

  • The Seattle Report on Database Research

    ACM SIGMOD Record · 2020 · 68 citations

    Approximately every five years, a group of database researchers meet to do a self-assessment of our community, including reflections on our impact on the industry as well as challenges facing our research community. This report summarizes the discussion and conclusions of the 9th such meeting, held during October 9-10, 2018 in Seattle.

    PublisherDOI

  • MIRIS: Fast Object Track Queries in Video

    2020 · 68 citations

    Video databases that enable queries with object-track predicates are useful in many applications. Such queries include selecting objects that move from one region of the camera frame to another (e.g., finding cars that turn right through a junction) and selecting objects with certain speeds (e.g., finding animals that stop to drink water from a lake). Processing such predicates efficiently is challenging because they involve the movement of an object over several video frames. We propose a novel…

    DOI

  • The Case for a Learned Sorting Algorithm

    2020 · 44 citations

    Senior authorCorresponding

    Sorting is one of the most fundamental algorithms in Computer Science and a common operation in databases not just for sorting query results but also as part of joins (i.e., sort-merge-join) or indexing. In this work, we introduce a new type of distribution sort that leverages a learned model of the empirical CDF of the data. Our algorithm uses a model to efficiently get an approximation of the scaled empirical CDF for each record key and map it to the corresponding position in the output array.…

    PublisherOA PDFDOI

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