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Arun KumarAssistant Professor |
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 two SIGMOD research paper awards in 2019 and 2014, three distinguished reviewer awards from SIGMOD/VLDB in 2019 and 2017, the 2016 PhD dissertation award from UW-Madison CS, a 2018 Hellman Fellowship, a 2016 Google Faculty Research Award, and a 2019 Oracle Labs Research Award.
Curriculum Vitae | Research Blog | On Twitter
Recent News
07/19: Gave a talk on data science careers to two groups of high school students, REHS summer workshop by SDSC and Big Data Summer Camp by QI at UCSD. PDF of slides
06/19: Blog posts galore related to SIGMOD 2019! Overview post on the ADALab presence at SIGMOD by me, Post on Krypton by Supun, Post on Cerebro by Supun and Yuhao, Post on MLDataPrepZoo by Vraj, and Post on SpeakQL by Vraj.
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.
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).
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
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 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)
Advising
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