Arun Kumar

Assistant Professor
Computer Science and Engineering
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 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 data systems for machine learning/artificial intelligence-based data analytics. Systems and ideas based on his research have been released as part of the MADlib open-source library, shipped as part of products from EMC, Oracle, Cloudera, and IBM, and used internally by Facebook, LogicBlox, Microsoft, and other companies. He is a recipient of the Best Paper Award at ACM SIGMOD 2014 and the 2016 Graduate Student Research Award for the best dissertation research in UW-Madison CS.

Curriculum Vitae | Research Blog | On Twitter

Recent News

  • New! Preprint of the Nimbus paper is out! If ML training data is the new oil, we need to design new markets for this commodity!

  • New! Delighted that my student, Lingjiao Chen, is a recipient of a 2018 Google PhD Fellowship, the only student in the database area! Congratulations to Lingjiao and thank you to Google!

  • Preprints of the Vista and SLAB papers are out! Also out is a preprint of the DAnA paper, a collaboration with Hadi Esmaeilzadeh.

  • We have added a multi-community panel to discuss and chart the future course of research on data management and systems for ML/AI-based analytics at the DEEM workshop at SIGMOD 2018!

  • Hamlet++ paper is accepted to VLDB 2018 (project webpage with paper/code/data)! Shakespeare vai para o Brasil!


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.




  • Lingjiao Chen (PhD, UW-Madison; co-advised by Paris Koutris)

  • Supun Nakandala (PhD, UCSD)

  • Vraj Shah (MS, UCSD)

  • Anthony Thomas (MS, UCSD)

  • Side Li (BS, UCSD)


  • Mingyang Wang (MS, UCSD, 2017)

Technical Service


Program Committee:

  • ACM SIGMOD 2019, 2018

  • VLDB 2019, 2018

  • ACM SIGMOD 2017 (Research Track, Demonstrations, and Student Research Competition)

  • ACM SIGMOD 2017 Workshop on Data Management for End-to-End Machine Learning (DEEM)

  • 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