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)

Bio

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 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 two SIGMOD research paper awards, a SIGMOD Research Highlight Award, three distinguished reviewer awards from SIGMOD/VLDB, the PhD dissertation award from UW-Madison CS, an NSF CAREER Award, a Hellman Fellowship, a UCSD oSTEM Faculty of the Year Award, and research award gifts from Google, Oracle, and VMware.

Curriculum Vitae | Research Blog | On Twitter

Recent News

• New! 07/20: A big thank you to VMware for supporting our work on the Cerebro with a faculty research award!

• New! 06/20: A big thank you to NSF for supporting our work on Cerebro with a CAREER Award grant.

• New! 06/20: The Krypton paper receives an ACM SIGMOD Research Highlights Award! A shorter version of the paper will be featured in SIGMOD Record later in 2020.

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

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

• CSE 291/234: Data Systems for Machine Learning (new version of my former CSE 291): Fall 2020

• CSE 132C (formerly CSE 190): Database System Implementation: Spring 2020, Spring 2019, Spring 2018, Spring 2017

• DSC 102: Systems for Scalable Analytics: Winter 2020

• CSE 232A: Graduate Database Systems: Fall 2019, Fall 2018

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

• 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:

• 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)

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

• Advitya Gemawat (BS, HDSI, UCSD)

• Kabir Nagrecha (BS, CSE, UCSD)

Alumni:

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

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

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

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

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

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

Organization:

• Associate Editor, Scalable Data Science Category, VLDB 2021

• Co-Chair, Diversity and Inclusion, ACM SIGMOD 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:

• VLDB: 2021, 2020, 2019, 2018

• ACM SIGMOD: 2020, 2019, 2018, 2017

• 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

Reviewer:

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

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

Outreach Materials

• Interviews by UCSD's Data Science Student Society on scalable analytics and career options and on ethical risks/benefits of AI and other emerging technologies.

• Blog post on inclusive CS examples to raise awareness of the importance of inclusion and avoiding exclusionary examples in CS.

• Talk on data science careers to high school students at SDSC's REHS summer workshop and QI's Big Data summer camp at UCSD.

• Blog post on my coming out experience from my sociocultural and individual vantage point.