Kexin Rong

I am an Assistant Professor in the School of Computer Science at Georgia Tech. I lead the Data-to-Insights (D2I) Lab, where we build systems and algorithms for large-scale data analytics. We are part of the Georgia Tech database group. I also serve as an affiliated researcher with the VMware Research Group.

Our goal is to shorten the journey from raw data to actionable insights by improving computational and human efficiency at every stage of the data lifecycle. Current areas of focus include: 1) analytics over dirty and unstructured data; and 2) data infrastructure for GenAI-powered applications.

Previously, I completed my Ph.D. in CS from Stanford (advised by Peter Bailis and Philip Levis) and my B.S. in CS from Caltech.

Email / Google Scholar / Bio / CV / Lab Website

Updates

[June 2025] Congrats to Peng Li for winning the SIGMOD 2025 Jim Gray Doctoral Dissertation Award!
[June 2024] Received a SIGMOD 2024 Distinguished PC Award.
[Apr 2024] Received an Amazon Research Award for optimizing layout designs in data analytics systems.
[Apr 2024] Received an NSF award to reimagine video retrieval with hand-drawn sketches.
[Oct 2023] Received an NSF award to build a person-focused open knowledge graph.

here

[Oct 2023] Congrats to Hantian Zhang for winning the Chih Foundation Graduate Student Research Publication Awards!
[Aug 2023] Congrats to Peng Li for winning the best research paper award at VLDB'23!!
[Aug 2023] Recognized as a distinguished reviewer for PVLDB Vol16.
[June 2023] EECS Rising Stars 2023 Workshop will be hosted at Georgia Tech. Apply by July 10!
[Oct 2022] Thanks Bosch Research for supporting our work!
[Aug 2022] I am honored to have received the Catherine M. and James E. Allchin Early Career Professorship in the College of Computing.
[Jun 2022] After Datadog, TimescaleDB has also published a blog post about using our work ASAP for smoothing their time series visualizations.
[Jun 2022] I honored to have received an Honorable Mention for the 2022 SIGMOD Jim Gray Doctoral Dissertation Award.

PhD Students

Hongbin Zhong
Jixian Su
Weili Shi (w/ Joy Arulraj)

Hantian Zhang (PhD 2024, Google; w/ Xu Chu)
Renzhi Wu (PhD 2024, ByteDance; w/ Xu Chu)
Peng Li (PhD 2023, ByteDance; w/ Xu Chu)

Selected Publications and Preprints

ActionEngine: From Reactive to Programmatic GUI Agents via State-machine Memory
Hongbin Zhong, Fazle Faisal, Luis França, Tanakorn Leesatapornwongsa, Adriana Szekeres, Kexin Rong, Suman Nath
Preprint 2026.
Stream2LLM: Overlap Context Streaming and Prefill for Reduced Time-to-First-Token
Rajveer Bachkaniwala, Chengqi Luo, Richard So, Divya Mahajan, Kexin Rong
To appear at MLSys 2026.
Honeybee: Efficient Role-based Access Control for Vector Databases via Dynamic Partitioning
Hongbin Zhong, Matthew Lentz, Nina Narodytska, Adriana Szekeres, Kexin Rong
To appear at SIGMOD 2026.
VCR: Interpretable and Interactive Debugging of Object Detection Models with Visual Concepts
Jie Jeff Xu, Saahir Dhanani, Jorge Piazentin Ono, Wenbin He, Liu Ren, Kexin Rong
Information Systems 2025.
[demo]
SketchQL: Video Moment Querying with a Visual Query Interface
Renzhi Wu*, Pramod Chunduri*, Ali Payani, Xu Chu, Joy Arulraj, Kexin Rong
SIGMOD 2025.
[demo][code]
Inshrinkerator: Compressing Deep Learning Training Checkpoints via Dynamic Quantization
Amey Agrawal, Sameer Reddy, Satwik Bhattamishra, Venkata Prabhakara Sarath Nookala, Vidushi Vashishth, Kexin Rong, Alexey Tumanov
SoCC 2024.
Lotus: Characterization of Machine Learning Preprocessing Pipelines via Framework and Hardware Profiling
Rajveer Bachkaniwala, Harshith Lanka, Kexin Rong, Ada Gavrilovska
IISWC 2024. (Best Paper Finalist)
[code]
FALCON: Fair Active Learning using Multi-armed Bandits
Ki Hyun Tae, Hantian Zhang, Jaeyoung Park, Kexin Rong, Steven Euijong Whang
VLDB 2024.
[code]
Dynamic Data Layout Optimization with Worst-case Guarantees
Kexin Rong, Paul Liu, Sarah Ashok Sonje, Moses Charikar
ICDE 2024.
[slides][code]
Scaling a Declarative Cluster Manager Architecture with Query Optimization Techniques
Kexin Rong, Mihai Budiu, Athinagoras Skiadopoulos, Lalith Suresh, Amy Tai
VLDB 2023.
[slides] [code]
DiffPrep: Differentiable Data Preprocessing Pipeline Search for Learning over Tabular Data
Peng Li, Zhiyi Chen, Xu Chu, Kexin Rong
SIGMOD 2023.
[slides] [code]
Improving Computational and Human Efficiency in Large-Scale Data Analytics
Kexin Rong
PhD Thesis 2021. (SIGMOD Doctoral Dissertation Award Honorable Mention)
Approximate Partition Selection for Big-Data Workloads using Summary Statistics
Kexin Rong, Yao Lu, Peter Bailis, Srikanth Kandula, Philip Levis
VLDB 2020.
[talk]
Rehashing Kernel Evaluation in High Dimensions
Paris Siminelakis*, Kexin Rong*, Peter Bailis, Moses Charikar, Philip Levis.
ICML 2019. (Long talk)
[blog] [code] [supplementary]
Locality-Sensitive Hashing for Earthquake Detection: A Case Study of Scaling Data-Driven Science
Kexin Rong, Clara Yoon, Karianne Bergen, Hashem Elezabi, Peter Bailis, Philip Levis, Gregory Beroza.
VLDB 2018.
[blog] [video] [code] [seismology paper]
ASAP: Prioritizing Attention via Time Series Smoothing
Kexin Rong, Peter Bailis.
VLDB 2017.
[Datadog blog] [Timescale blog] [blog] [demo] [talk] [slides] [code]
Prioritizing Attention in Fast Data: Principles and Promise
Peter Bailis, Edward Gan, Kexin Rong, Sahaana Suri.
CIDR 2017.
MacroBase: Prioritizing Attention in Fast Data
Peter Bailis, Edward Gan, Samuel Madden, Deepak Narayanan, Kexin Rong, Sahaana Suri.
SIGMOD 2017 (Invited to ACM TODS "Best of SIGMOD 2017" Special Issue.)
[website] [code]

Teaching

Spring 2026: Emerging Database Technologies (CS4440 A)
Fall 2025: Database Systems Concepts and Design (CS6400 A)

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