Arun Kumar

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


Arun Kumar is an Associate Professor in the Department of Computer Science and Engineering and the Halicioglu Data Science Institute and an HDSI Faculty Fellow at the University of California, San Diego. He is a member of the Database Lab and Center for Networked Systems 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 three SIGMOD research paper awards, four distinguished reviewer/metareviewer awards from SIGMOD/VLDB, the IEEE TCDE Rising Star Award, an NSF CAREER Award, a UCSD oSTEM Faculty of the Year Award, and research award gifts from Amazon, Google, Oracle, and VMware.

Curriculum Vitae | Research Blog | On Twitter | On Tumblr

Recent News

  • New! 9/21: Awarded early tenure and promoted to Associate Professor by UC San Diego. Excited to keep powering ahead at this truly amazing academic home!

  • 8/21: Honored to be named a Distinguished Associate Editor by VLDB 2021! It was a pleasure to help co-launch VLDB's new SDS Research category and give it my best to make the peer review process better for both authors and reviewers.

  • 8/21: A new blog post summarizing the papers, talks, demo, and panel discussions at VLDB and KDD by ADALab members. Links to the public videos are included.

  • 7/21: The Kingpin paper on optimized execution of mass ML model building over data sub-groups, part of the Cerebro project, is accepted to VLDB 2021. Round up all groups’ models and execute them en masse!

  • 6/21: The IEEE Data Engineering Bulletin publishes this invited letter on my research following the TCDE Rising Star Award.


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.




  • Kabir Nagrecha (PhD, CSE, UCSD)

  • Kyle Luoma (PhD, CSE, UCSD)

  • Supun Nakandala (PhD, CSE, UCSD)

  • Tara Mirmira (PhD, CSE, USCD)

  • Vraj Shah (PhD, CSE, UCSD)

  • Xiuwen Zheng (PhD, CSE, USCD); Co-advisor: Amarnath Gupta

  • Yuhao Zhang (PhD, CSE, UCSD)

  • Yutong Shao (PhD, CSE, UCSD); Primary advisor: Ndapa Nakashole

  • Liangde Li (MS, CSE, UCSD)


  • Advitya Gemawat (BS, HDSI, UCSD, 2021); First employment: Microsoft NERD AI.

  • Kabir Nagrecha (BS, CSE, UCSD, 2021); First employment: PhD at UCSD.

  • Shaoqing Yi (BS, HDSI and Math, UCSD, 2021); First employment: PhD at UC Berkeley.

  • Side Li (MS, CSE, UCSD, 2021); First employment: Google.

  • Kevin Yang (BS, CSE, UCSD, 2020); First employment: MS at UPenn

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

  • Anthony Thomas (MS, CSE, UCSD, 2018); First employment: PhD at UCSD

  • Lingjiao Chen (MS, CS, UW-Madison, 2018); First employment: PhD at Stanford

  • Side Li (BS, CSE, UCSD, 2018); First employment: Amazon

  • Mingyang Wang (MS, CSE, UCSD, 2017); First employment: Amazon



  • Associate Editor, Scalable Data Science Category, VLDB 2022, 2021 (Inaugural)

  • Co-Chair, Diversity and Inclusion, ACM SIGMOD 2021 (Inaugural)

  • Core Committee member, Diversity & Inclusion in DB Initiative, 2021 (Inaugural)

  • (Inaugural) Lead Organizer, SoCal DB Day 2018

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

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

  • Organizing Committee, Extremely Large Databases (XLDB) 2018

Program Committee:

  • VLDB: 2022, 2021, 2020, 2019, 2018

  • CIDR: 2022, 2021

  • ACM SIGMOD: 2020, 2019, 2018, 2017

  • ACM SIGMOD DEEM Workshop: 2021, 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

Reviewer / External:

  • ACM SIGMOD 2022

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

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

Outreach Materials

Blog Posts and Talks:

Interviews and Panels:

News and Other Resources: