Kexin Rong

I am a Ph.D. student in Computer Science at Stanford University, co-advised by Prof. Peter Bailis and Prof. Philip Levis. I am affiliated with the Stanford DAWN project. I am broadly interested in designing and building systems to enable data analytics at scale, supporting applications such as scientific analysis, infrastructure monitoring, and analytical queries on big-data clusters.

Previously, I received my bachelor's degree in Computer Science from Caltech (2015). I've also spend time at the DMX group at Microsoft Research in Redmond (2019).

Email / Google Scholar / CV / Github

Publications

	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] [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

Stanford CS197: Computer Science Research, Fall 2019 - Teaching Assistant
Stanford CS 161: Design and Analysis of Algorithms, Summer 2018 - Teaching Assistant
Caltech CS 122: Relational Database Implementation, Winter 2015 - Teaching Assistant
Caltech CS 24: Introduction to Computing Systems, Spring 2014 - Teaching Assistant
Caltech CS 1: Introduction to Computer Programming, Fall 2013 - Teaching Assistant
Caltech CS 1: Introduction to Computer Programming, Fall 2012 - Teaching Assistant

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