Michael Franklin

About

Michael Franklin is the Morton D. Hull Distinguished Service Professor of Computer Science at the University of Chicago. He also serves as Senior Advisor to the Provost for Computing and Data Science and is the founding Faculty Co-Director of the Data Science Institute. Franklin was the inaugural holder of the Liew Family Chair of Computer Science at UChicago, where he led the rapid growth of the department in scale, scope, and stature. His research focuses on large-scale data intelligence systems, including early efforts on massively parallel databases, federated data systems, and scalable data-centric AI systems. Prior to his tenure at UChicago, Franklin was the Thomas M. Siebel Professor of Computer Science at the University of California, Berkeley, where he was on the faculty for 17 years and served as Chair of the Computer Science Division of the EECS Department. He was also the Director of the Algorithms, Machines and People Laboratory (AMPLab) and Principal Investigator of the lab’s NSF CISE Expeditions in Computing award. Franklin is one of the original creators of Apache Spark, a leading open-source platform for advanced data analytics and machine learning developed at AMPLab. He has held visiting positions at MIT CSAIL and at research labs in Hong Kong, Shanghai, and Paris. He is a Founding Advisor at Databricks and a technical advisor to data-driven technology companies and organizations, including Chicago-based startups Invocate, Ocient, and Zengines. Franklin…

Research topics

• Computer Science
• Data Mining
• Artificial Intelligence
• Machine Learning
• Mathematics
• Data science
• Database
• Social Science
• Statistics
• Information Retrieval

Selected publications

• Data market platforms

  Proceedings of the VLDB Endowment · 2020 · 139 citations

  Senior authorCorresponding

  Data only generates value for a few organizations with expertise and resources to make data shareable, discoverable, and easy to integrate. Sharing data that is easy to discover and integrate is hard because data owners lack information (who needs what data) and they do not have incentives to prepare the data in a way that is easy to consume by others. In this paper, we propose data market platforms to address the lack of information and incentives and tackle the problems of data sharing, discov…

  DOI

• Volume under the surface

  Proceedings of the VLDB Endowment · 2022 · 106 citations

  Senior authorCorresponding

  Anomaly detection (AD) is a fundamental task for time-series analytics with important implications for the downstream performance of many applications. In contrast to other domains where AD mainly focuses on point-based anomalies (i.e., outliers in standalone observations), AD for time series is also concerned with range-based anomalies (i.e., outliers spanning multiple observations). Nevertheless, it is common to use traditional point-based information retrieval measures, such as Precision, Rec…

  PublisherDOI

• SAND

  Proceedings of the VLDB Endowment · 2021 · 102 citations

  Senior authorCorresponding

  With the increasing demand for real-time analytics and decision making, anomaly detection methods need to operate over streams of values and handle drifts in data distribution. Unfortunately, existing approaches have severe limitations: they either require prior domain knowledge or become cumbersome and expensive to use in situations with recurrent anomalies of the same type. In addition, subsequence anomaly detection methods usually require access to the entire dataset and are not able to learn…

  DOI

• How Large Language Models Will Disrupt Data Management

  Proceedings of the VLDB Endowment · 2023 · 97 citations

  Large language models (LLMs), such as GPT-4, are revolutionizing software's ability to understand, process, and synthesize language. The authors of this paper believe that this advance in technology is significant enough to prompt introspection in the data management community, similar to previous technological disruptions such as the advents of the world wide web, cloud computing, and statistical machine learning. We argue that the disruptive influence that LLMs will have on data management wil…

  PublisherDOI

• 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

Frequent coauthors

• Tim Kraska

  Amazon (United States)

  73 shared

• Sanjay Krishnan

  University of Chicago

  61 shared

• Ion Stoica

  47 shared

• Joseph M. Hellerstein

  University of California, Berkeley

  46 shared

• Jiannan Wang

  Purdue University System

  46 shared

• Aaron J. Elmore

  University of Chicago

  37 shared

• Samuel Madden

  35 shared

• Reynold Xin

  Databricks (United States)

  28 shared

Labs

• Data & Systems LabPI

Education

• Ph.D.

  University of California, Berkeley

• B.S.

  University of California, Berkeley

Awards & honors

• 2025 CIDR Test of Time Award
• 2023 Arthur Kelly Faculty Prize
• 2022 ACM SIGMOD Systems Award
• 2021 AAAS Fellow
• 2013 SIGMOD Test of Time Award

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