Kexin Rong

Kexin Rong is an Assistant Professor in the School of Computer Science at Georgia Institute of Technology. She leads the D2I Lab, which is part of the Georgia Tech database group, and maintains an affiliation with VMware Research Group as an affiliated researcher.

Dr. Rong received her Ph.D. in Computer Science from Stanford University in 2021 (advised by Peter Bailis and Philip Levis) and her B.S. in Computer Science from California Institute of Technology in 2015. Her academic journey includes recognition as an EECS Rising Star by UC Berkeley and an Honorable Mention for the SIGMOD Jim Gray Doctoral Dissertation Award.

Her research focuses on democratizing data science through systems that simplify large-scale data analytics for non-experts. She synthesizes techniques from data management, machine learning, and human-computer interaction to improve both computational and human efficiency. Current research directions include analytics over unstructured data (Video Labeling with SketchQL and Image Understanding with VCR) and data infrastructure for AI (Data preprocessing with LOTUS and DiffPrep, Checkpointing for DL training with Inshrinkerator).

Her recent publications show a strong trend toward building user-friendly systems that bridge complex data analytics and non-expert users, with significant impact demonstrated by adoption of systems like ASAP by industry players including Datadog and TimescaleDB for time series visualizations.

• Amazon Research Award recipient (2024)
• SIGMOD 2024 Distinguished PC Award
• Multiple NSF awards (2023-2024)
• Best Paper Finalist at IISWC 2024
• Catherine M. and James E. Allchin Early Career Professorship

Dr. Rong actively mentors students, with recent PhD graduates including Peng Li (VLDB'23 Best Paper Award winner), Hantian Zhang, and Renzhi Wu now at leading tech companies. She teaches core database courses including CS6400 Database Systems Concepts and Design and CS4440 Emerging Database Technologies.

The D2I Lab under her direction is committed to providing an inclusive environment that welcomes applicants from diverse backgrounds, especially those underrepresented in computer science.