|
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
|
|
|
Publications and Preprints
|
-
Online Data Layout Optimization via Metrical Task Systems
Kexin Rong, Paul Liu, Moses Charikar
Preprint 2023.
-
Scaling a Declarative Cluster Manager Architecture with Query Optimization Techniques (Technical Report)
Kexin Rong, Mihai Budiu, Athinagoras Skiadopoulos, Lalith Suresh, Amy Tai
Preprint 2022.
-
DiffPrep: Differentiable Data Preprocessing Pipeline Search for Learning over Tabular Data
Peng Li, Zhiyi Chen, Xu Chu, Kexin Rong
SIGMOD 2023. (To appear)
-
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]
-
CrossTrainer: Practical Domain Adaptation with Loss Reweighting
Justin Chen, Edward Gan, Kexin Rong, Sahaana Suri, Peter Bailis.
SIGMOD DEEM Workshop 2019.
-
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]
-
MacroBase: Prioritizing Attention in Fast Data
Firas Abuzaid, Peter Bailis, Jialin Ding, Edward Gan, Samuel Madden, Deepak Narayanan, Kexin Rong, Sahaana Suri.
ACM TODS 2018. "Best of SIGMOD 2017" Special Issue.
-
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] [demo paper]
|