Preprints

• DynaQuant: Compressing Deep Learning Training Checkpoints via Dynamic Quantization
  Amey Agrawal, Sameer Reddy, Satwik Bhattamishra, Venkata Prabhakara Sarath Nookala, Vidushi Vashishth, Kexin Rong, Alexey Tumanov
  Preprint.
• Rethinking Similarity Search: Embracing Smarter Mechanisms over Smarter Data
  Renzhi Wu, Jingfan Meng, Jie Jeff Xu, Huayi Wang, Kexin Rong
  Preprint.

Publications

2025

• SketchQL: Video Moment Querying with a Visual Query Interface
  Renzhi Wu, Pramod Chunduri, Ali Payani, Xu Chu, Joy Arulraj, Kexin Rong
  To appear at SIGMOD 2025.

2024

• Lotus: Characterization of Machine Learning Preprocessing Pipelines via Framework and Hardware Profiling
  Rajveer Bachkaniwala, Harshith Lanka, Kexin Rong, Ada Gavrilovska
  To appear at IISWC 2024.
• SketchQL Demonstration: Zero-shot Video Moment Querying with Sketches
  Renzhi Wu, Pramod Chunduri, Dristi Shah, Ashmitha Julius Aravind, Ali Payani, Xu Chu, Joy Arulraj, Kexin Rong
  VLDB 2024 Demo.
• Demonstration of VCR: A Tabular Data Slicing Approach to Understanding Object Detection Model Performance
  Jie Jeff Xu, Saahir Dhanani, Jorge Piazentin Ono, Wenbin He, Liu Ren, Kexin Rong
  VLDB 2024 Demo.
• Dynamic Data Layout Optimization with Worst-case Guarantees
  Kexin Rong, Paul Liu, Sarah Ashok Sonje, Moses Charikar
  ICDE 2024.
• FALCON: Fair Active Learning using Multi-armed Bandits
  Ki Hyun Tae, Hantian Zhang, Jaeyoung Park, Kexin Rong, Steven Euijong Whang
  VLDB 2024.

2023

• Scaling a Declarative Cluster Manager Architecture with Query Optimization Techniques
  Kexin Rong, Mihai Budiu, Athinagoras Skiadopoulos, Lalith Suresh, Amy Tai
  VLDB 2023.
• Interactive Demonstration of EVA
  Gaurav Tarlok Kakkar, Aryan Rajoria, Myna Prasanna Kalluraya, Ashmita Raju, Jiashen Cao, Kexin Rong, Joy Arulraj
  VLDB 2023 Demo.
• DiffPrep: Differentiable Data Preprocessing Pipeline Search for Learning over Tabular Data
  Peng Li, Zhiyi Chen, Xu Chu, Kexin Rong
  SIGMOD 2023.

2021

• Improving Computational and Human Efficiency in Large-Scale Data Analytics
  Kexin Rong. Stanford University CS Ph.D. Thesis, 2021.
  (SIGMOD Jim Gray Doctoral Dissertation Award honorable mention)

2020

• Approximate Partition Selection for Big-Data Workloads using Summary Statistics
  Kexin Rong, Yao Lu, Peter Bailis, Srikanth Kandula, Philip Levis
  VLDB 2020.

2019

• Rehashing Kernel Evaluation in High Dimensions
  Paris Siminelakis, Kexin Rong, Peter Bailis, Moses Charikar, Philip Levis.
  ICML 2019. (Long talk)
• CrossTrainer: Practical Domain Adaptation with Loss Reweighting
  Justin Chen, Edward Gan, Kexin Rong, Sahaana Suri, Peter Bailis.
  SIGMOD DEEM Workshop 2019.

2018

• 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.
• 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.)

2017

• ASAP: Prioritizing Attention via Time Series Smoothing
  Kexin Rong, Peter Bailis.
  VLDB 2017
• Demonstration: MacroBase, A Fast Data Analysis Engine
  Peter Bailis, Edward Gan, Kexin Rong, Sahaana Suri.
  SIGMOD 2017 Demo.
• 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.)