Jialin Ding

I am a fourth year CS PhD in the MIT Data Systems Group, where I am advised by Prof. Tim Kraska. My research focuses on applying machine learning to database systems.

I also collaborate with Umar Farooq Minhas and the Database Group at Microsoft Research on learned data structures. Prior to MIT, I was an undergraduate at Stanford University, where I worked on data-intensive systems with Prof. Peter Bailis as part of Stanford DAWN. My research is partly supported by a Facebook Fellowship.

[CV] [Google Scholar]

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Publications

• APEX: A High-Performance Learned Index on Persistent Memory.
  Baotong Lu, Jialin Ding, Eric Lo, Umar Farooq Minhas and Tianzheng Wang.
  VLDB 2022.

• Self-Organizing Data Containers.
  Samuel Madden Jialin Ding, Tim Kraska, Sivaprasad Sudhir, David Cohen, Timothy Mattson and Nesime Tatbul.
  CIDR 2022.

• Tsunami: A Learned Multi-dimensional Index for Correlated Data and Skewed Workloads. [news]
  Jialin Ding, Vikram Nathan, Mohammad Alizadeh and Tim Kraska.
  VLDB 2021.

• Instance-Optimized Data Layouts for Cloud Analytics Workloads.
  Jialin Ding, Umar Farooq Minhas, Badrish Chandramouli, Chi Wang, Yinan Li, Ying Li, Donald Kossmann, Johannes Gehrke and Tim Kraska.
  SIGMOD 2021.

• Cortex: Harnessing Correlations to Boost Query Performance.
  Vikram Nathan, Jialin Ding, Tim Kraska and Mohammad Alizadeh.
  CoRR 2020.

• The Case for Learned Spatial Indexes.
  Varun Pandey, Alexander van Renen, Andreas Kipf, Ibrahim Sabek, Jialin Ding and Alfons Kemper.
  AIDB Workshop @ VLDB 2020.

• ALEX: An Updatable Adaptive Learned Index. [talk] [seminar talk] [code]
  Jialin Ding, Umar Farooq Minhas, Jia Yu, Chi Wang, Jaeyoung Do, Hantian Zhang, Yinan Li, Badrish Chandramouli, Johannes Gehrke, Donald Kossmann, David Lomet and Tim Kraska.
  SIGMOD 2020.

• Learning Multi-dimensional Indexes. [talk] [seminar talk]
  Vikram Nathan^*, Jialin Ding^*, Mohammad Alizadeh and Tim Kraska.
  SIGMOD 2020.

• LISA: Towards Learned DNA Sequence Search.
  Darryl Ho, Jialin Ding, Sanchit Misra, Nesime Tatbul, Vikram Nathan, Vasimuddin Md and Tim Kraska.
  Systems for ML Workshop @ NeurIPS 2019. Oral Presentation.

• Learning Multi-dimensional Indexes. [talk]
  Vikram Nathan^*, Jialin Ding^*, Mohammad Alizadeh and Tim Kraska.
  ML for Systems Workshop @ NeurIPS 2019. Oral Presentation.

• SageDB: A Learned Database System. [the morning paper]
  Tim Kraska, Mohammad Alizadeh, Alex Beutel, Ed Chi, Jialin Ding, Ani Kristo, Guillaume Leclerc, Samuel Madden, Hongzi Mao and Vikram Nathan.
  CIDR 2019.

• A Machine-compiled Database of Genome-wide Association Studies.
  Volodymyr Kuleshov, Jialin Ding, Christopher Vo, Braden Hancock, Alexander Ratner, Yang Li, Christopher Ré, Serafim Batzoglou and Michael Snyder
  Nature Communications 2019.

• Moment-Based Quantile Sketches for Efficient High Cardinality Aggregation Queries. [the morning paper] [blog]
  Edward Gan, Jialin Ding, Kai Sheng Tai, Vatsal Sharan and Peter Bailis.
  VLDB 2018.

• Efficient Mergeable Quantile Sketches using Moments.
  Edward Gan, Jialin Ding, Peter Bailis.
  SysML 2018. Extended Abstract.

• MacroBase: Prioritizing Attention in Fast Data.
  Firas Abuzaid, Peter Bailis, Jialin Ding, Edward Gan, Samuel Madden, Deepak Narayanan, Kexin Rong and Sahaana Suri.
  TODS 2018.

• A Machine-Compiled Database of Genome-Wide Association Studies.
  Volodymyr Kuleshov, Jialin Ding, Braden Hancock, Alexander Ratner, Christopher Re, Serafim Batzoglou and Michael Snyder.
  ISMB 2017. Short Paper.

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Teaching and Service

• VLDB 2022 Demo Track Reviewer
• 6.887: Machine Learning for Systems, Fall 2021 Teaching Assistant
• VLDB 2021 Student Volunteer
• TKDE 2020 External Reviewer

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Experience

• Research Intern @ MSR Redmond, Summer 2020
• Research Intern @ MSR Redmond, Summer 2018
• Software Engineering Intern @ Google, Summer 2016
• Software Engineering Intern @ Thumbtack, Summer 2015

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Contact

jialind@mit.edu