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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)

Bio

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 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 ACM SIGMOD 2014 Best Paper Award, the 2016 Graduate Student Research Award for the best dissertation research in UW-Madison CS, a 2016 Google Faculty Research Award, and a 2018 Hellman Fellowship.

Curriculum Vitae | Research Blog | On Twitter

Recent News

  • New! Side's paper on a non-linear variant of Morpheus gets a rare direct accept to SIGMOD 2019. Kudos to Side, the first undergraduate researcher I know to have accomplished this feat!

  • New! The Nimbus and Tuple-Oriented Compression papers are both accepted to SIGMOD 2019! I amsterdam and all that.

  • Represented CSE/UCSD at the oSTEM National Conference. Excited to see the high interest in computer science and data science! Also happy to spread the word on UCSD's spectacular resources and efforts on inclusivity of LGBTQ+ people.

  • The inaugural edition of SoCal DB Day was a big success! Thank you to all the participating schools and companies.

  • The SLAB paper is accepted to VLDB 2018 (or 2019?). Hit your ML system with SLAB to prove it is worthy!


Research

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.


Teaching

Advising

Current:

  • Supun Nakandala (PhD, UCSD)

  • Vraj Shah (PhD, UCSD)

  • Yuhao Zhang (MS, UCSD)

  • Kevin Yang (BS, UCSD)

Alumni:

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

  • Side Li (BS, UCSD, 2018)

  • Anthony Thomas (MS, UCSD, 2018)

  • Mingyang Wang (MS, UCSD, 2017)

Technical Service

Organization:

  • Lead Organizer, SoCal DB Day 2018

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

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

  • Organizing Committee, Extremely Large Databases (XLDB) 2018

Program Committee:

  • ACM SIGMOD 2019, 2018

  • VLDB 2019, 2018

  • SysML 2019

  • ACM SIGMOD DEEM Workshop 2019, 2017

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

  • IEEE ICDE 2017

  • USENIX HotCloud 2016

  • ACM SIGMOD 2016 Undergraduate Research Poster Competition

Reviewer:

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

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