Arun Kumar

Assistant Professor
Computer Science and Engineering
and Halicioglu Data Science Institute
University of California, San Diego
Email: arunkk [at] eng [dot] ucsd [dot] edu
Office: 3218 EBU3B (CSE building)


Arun Kumar is an Assistant Professor in the Department of Computer Science and Engineering and the Halicioglu Data Science Institute at the University of California, San Diego. He is a member of the Database Lab and CNS and an affiliate member of the AI Group. His primary research interests are in data management and systems for machine learning/artificial intelligence-based data analytics. Systems and ideas based on his research have been released as part of the Apache MADlib open-source library, shipped as part of products from Cloudera, IBM, Oracle, and Pivotal, and used internally by Facebook, Google, LogicBlox, Microsoft, and other companies. He is a recipient of two SIGMOD research paper awards, three distinguished reviewer awards from SIGMOD/VLDB, the PhD dissertation award from UW-Madison CS, a Hellman Fellowship, two Google Faculty Research Awards, and an Oracle Labs Research Award.

Curriculum Vitae | Research Blog | On Twitter

Recent News

  • New! 04/20: The invited extended version of the Krypton paper has been accepted to ACM TODS 2020.

  • New! 04/20: A big thank you to Google for supporting our work on the ML Data Prep Zoo with a faculty research award!

  • 3/20: Both the Vista and SpeakQL papers are accepted to ACM SIGMOD 2020! Let our data systems open their eyes and ears to the era of "database perception."

  • 2/20: The GDPRBench paper is accepted to VLDB 2020!


My current research focuses on the foundations of advanced data analytics systems that help make the process of building and deploying ML/AI-powered data analytics applications easier (improving the productivity of data scientists and ML/software engineers) and faster (improving runtime performance and introducing accuracy trade-offs). Thus, the key themes of my research are usability, developability, performance, and scalability. I enjoy working on problems that are motivated by real applications and are formally grounded. I also enjoy insightful conversations with practitioners on the frontlines of data analytics.

More details about my research are available on my research group webpage, including current projects, and all of our publications.




  • Side Li (PhD, CSE, UCSD)

  • Tara Mirmira (PhD, CSE, USCD)

  • Supun Nakandala (PhD, CSE, UCSD)

  • Vraj Shah (PhD, CSE, UCSD)

  • Yuhao Zhang (PhD, CSE, UCSD)

  • Advitya Gemawat (BS, HDSI, UCSD)

  • Kabir Nagrecha (BS, CSE, UCSD)

  • Kevin Yang (BS, CSE, UCSD)


  • David Justo (MS, CSE UCSD, 2019); Co-advisor: Nadia Polikarpova

  • Side Li (BS, CSE, UCSD, 2018)

  • Anthony Thomas (MS, CSE, UCSD, 2018)

  • Lingjiao Chen (MS, CS, UW-Madison, 2018)

  • Mingyang Wang (MS, CSE, UCSD, 2017)



  • Associate Editor, VLDB 2021

  • Lead Organizer, SoCal DB Day 2018

  • Co-Chair, ACM SIGMOD Workshop on Data Management for End-to-End Machine Learning (DEEM) 2018

  • Organizing Committee, ACM SIGKDD Workshop on Common Model Infrastructure (CMI) 2018

  • Organizing Committee, Extremely Large Databases (XLDB) 2018

Program Committee:

  • ACM SIGMOD: 2020, 2019, 2018, 2017

  • VLDB: 2021, 2020, 2019, 2018

  • ACM SIGMOD DEEM Workshop: 2020, 2019, 2017

  • MLSys / SysML: 2020, 2019

  • ACM SIGMOD 2017 Demonstrations; Student Research Competition

  • IEEE ICDE 2017

  • USENIX HotCloud 2016

  • ACM SIGMOD 2016 Undergraduate Research Poster Competition


  • ACM Transactions on Database Systems (TODS) 2017, 2015

  • IEEE Transactions on Knowledge and Data Engineering (TKDE) 2014