Kexin Rong

I am an Assistant Professor in the School of Computer Science at Georgia Tech. My lab studies systems and algorithms to improve the computational and human efficiency of large-scale data analytics and is part of the Georgia Tech database group. I also spend time at VMware Research Group as an affiliated researcher.

I am broadly interested in building systems and tools to help democratize data science, i.e., making it easy for non-experts to make sense and leverage the increasing large volumes of data by making the process more efficient and more accessible.

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.

I am actively looking for master and PhD students. If you are a GT student who is interested in working with me, please check out this page.

Email / Google Scholar / CV / Github / Lab Website

Updates

I will be offering a special topics class on human-in-the-loop data analytics this fall. Check in out!
After Datadog, TimescaleDB has also published a blog post about using our work ASAP for smoothing their time series visualizations.
I received an Honorable Mention for the 2022 SIGMOD Jim Gray Doctoral Dissertation Award.

Publications and Preprints

	Scaling a Declarative Cluster Manager Architecture with Query Optimization Techniques (Technical Report) Kexin Rong, Mihai Budiu, Athinagoras Skiadopoulos, Lalith Suresh, Amy Tai Preprint, 2022
	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] A system that leverages summary statistics to select weighted, partition-level samples to approximate analytical queries on big-data clusters.
	Rehashing Kernel Evaluation in High Dimensions Paris Siminelakis, Kexin Rong, Peter Bailis, Moses Charikar, Philip Levis. ICML, 2019 (Long talk) [blog] [code] [supplementary] LSH-based sketching and importance sampling algorithms to accelerate kernel evaluation in high dimensions.
	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] An unsupervised, end-to-end earthquake detection pipeline based on pairwise similarity search on seismic waveforms.
	ASAP: Prioritizing Attention via Time Series Smoothing Kexin Rong, Peter Bailis. VLDB, 2017 [Datadog blog] [Timescale blog] [blog] [demo] [talk] [slides] [code] An automatic smoothing algorithm for time series visualization that removes short-term fluctuations while preserving large-scale deviations.
	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] [journal paper] [vision paper] [demo paper] A data analytics engine that highlights and aggregates important and unusual behavior in high-volume fast data streams.

Teaching

Fall 2022: Human-in-the-loop Data Analytics (CS 8803-MDS)

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