Kexin Rong
Assistant Professor, School of Computer Science, Georgia Tech
I am an Assistant Professor in the School of Computer Science at Georgia Tech. I lead the Data-to-Insights (D2I) Lab, where we develop data systems for emerging data and AI workloads. We are part of the Georgia Tech database group.
Our research explores the evolving role of data systems in the age of AI, with a focus on managing data with, for, and from AI. Current research directions include: (1) managing complex and unstructured data, such as videos, documents, prompts and agent traces, to make them easier to query, analyze, and reuse; and (2) building efficient data infrastructure for emerging AI workloads.
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.
News
- Oct 2026Honored to receive the 2nd Annual Google ML and Systems Junior Faculty Award, in recognition of our work on ML systems and data applications.
- Aug 2026Recognized as a distinguished reviewer for SIGMOD (2026) and VLDBJ (2025–2026).
- Jun 2025Congrats to Peng Li for winning the SIGMOD 2025 Jim Gray Doctoral Dissertation Award!
- Jun 2024Received a SIGMOD 2024 Distinguished PC Award.
- Apr 2024Received an Amazon Research Award for optimizing layout designs in data analytics systems.
Older news
- Apr 2024Received an NSF award to reimagine video retrieval with hand-drawn sketches.
- Oct 2023Received an NSF award to build a person-focused open knowledge graph. Learn more about our story here.
- Oct 2023Congrats to Hantian Zhang for winning the Chih Foundation Graduate Student Research Publication Awards!
- Aug 2023Congrats to Peng Li for winning the best research paper award at VLDB’23!
- Aug 2023Recognized as a distinguished reviewer for PVLDB Vol16.
- Jun 2023EECS Rising Stars 2023 Workshop will be hosted at Georgia Tech.
- Oct 2022Thanks Bosch Research for supporting our work!
- Aug 2022Honored to have received the Catherine M. and James E. Allchin Early Career Professorship in the College of Computing.
- Jun 2022After Datadog, TimescaleDB has also published a blog post about using our work ASAP for smoothing their time series visualizations.
- Jun 2022Honored to have received an Honorable Mention for the 2022 SIGMOD Jim Gray Doctoral Dissertation Award.
PhD Students
Current
- Jixian Su
- Weili Shi (w/ Joy Arulraj)
- Shahmeer Ahmad (w/ Joy Arulraj)
- Yujun He
Selected Publications & Preprints
- Prompt Analytics
- ActionEngine: From Reactive to Programmatic Web Agents via State-machine Memory
- Action Chunk Scheduling for Batched Robot Policy Serving
- Stream2LLM: Overlap Context Streaming and Prefill for Reduced Time-to-First-Token
- Honeybee: Efficient Role-based Access Control for Vector Databases via Dynamic Partitioning
- VCR: Interpretable and Interactive Debugging of Object Detection Models with Visual Concepts
- SketchQL: Video Moment Querying with a Visual Query Interface
- Inshrinkerator: Compressing Deep Learning Training Checkpoints via Dynamic Quantization
- Lotus: Characterization of Machine Learning Preprocessing Pipelines via Framework and Hardware Profiling
- FALCON: Fair Active Learning using Multi-armed Bandits
- Dynamic Data Layout Optimization with Worst-case Guarantees
- Scaling a Declarative Cluster Manager Architecture with Query Optimization Techniques
- DiffPrep: Differentiable Data Preprocessing Pipeline Search for Learning over Tabular Data
- Improving Computational and Human Efficiency in Large-Scale Data Analytics
- Approximate Partition Selection for Big-Data Workloads using Summary Statistics
- Rehashing Kernel Evaluation in High Dimensions
- Locality-Sensitive Hashing for Earthquake Detection: A Case Study of Scaling Data-Driven Science
- ASAP: Prioritizing Attention via Time Series Smoothing
- MacroBase: Prioritizing Attention in Fast Data
Teaching
- Database Systems Concepts and Design (CS 6400 A)Fa26 · Fa25 · Fa24
- Emerging Database Technologies (CS 4440 A)Sp26 · Sp25 · Sp24
- Human-in-the-loop Data Analytics (CS 8803 MDS)Fa23 · Fa22
Support
Our research has been supported by the NSF (2335881, 2333737), Bosch Research, Dolby Advanced Technology Group (ATG), Amazon, Google, and the Helen Gurley Brown Foundation.
