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! The Krypton paper receives an honorable mention as a runner up for the Best Paper Award at SIGMOD 2019! A demo on Krypton is also accepted to VLDB 2019.

• New! Supun and Yuhao's short paper on Cerebro and Vraj's short paper on SortingHat are both accepted to the DEEM Workshop at SIGMOD 2019.

• Gave a talk at Strata Data Conference and interviewed by SE Daily on the topic of ML systems (podcast link).

• Morgan & Claypool publishes a book I co-authored with Matthias Boehm and Jun Yang, Data Management in ML Systems, the first book on the emerging area of ML systems (PDF on M&C webpage; order hard copy).

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.

• CSE 190: Database System Implementation: Spring 2019, Spring 2018, Spring 2017

• CSE 291: Advanced Data Analytics and ML Systems: Winter 2019, Winter 2018, Winter 2017

• CSE 232A: Graduate Database Systems: Fall 2018

• CSE 290: Seminar on Integrative AI Engineering: Fall 2018

• CSE 290: Seminar on Advanced Data Science: Fall 2017, Spring 2017

• CS 564: Database Management Systems: Design and Implementation (Fall 2015 at UW-Madison)

Current:

• Supun Nakandala (PhD, CSE, UCSD)

• Vraj Shah (PhD, CSE, UCSD)

• Yuhao Zhang (MS, CSE, UCSD)

• Advitya Gemawat (BS, HDSI, UCSD)

• Kevin Yang (BS, CSE, UCSD)

Alumni:

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

• Side Li (BS, CSE, UCSD, 2018)

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

• Mingyang Wang (MS, CSE, UCSD, 2017)

Technical Service

Organization:

• 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 2020, 2019, 2018

• SysML 2019

• ACM SIGMOD DEEM Workshop 2019, 2017

• ACM SIGMOD 2017 Demonstrations; 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