Jinglin Peng - PhD Candidate

Jinglin Peng

PhD Candidate

Greater Vancouver Metropolitan Area Canada

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Summary

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Rockstar

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Top School

Jinglin Peng is a PhD candidate and researcher with nine years of hands-on experience building interactive data analytics systems that make large-scale insight extraction practical and fast. His work focuses on Approximate Query Processing—where he has developed a hybrid samples-and-cubes AQP system, a sampler-combination framework, and an online aggregation engine for partitioned data—and on simplifying exploratory data analysis via DataPrep.EDA, a Python task-centric EDA library that has gained significant traction (~1k stars and ~200k downloads). He has industrial research experience from an Alibaba internship and contributes to open-source data tooling, fixing visualization and EDA bugs in the dataprep project to improve usability. Based in the Greater Vancouver area, he blends rigorous academic research with pragmatic engineering, often improving estimation quality and interactivity by orders of magnitude.

9 years of coding experience

Bachelor's degree, Bachelor's degree at Harbin Institute of Technology

Doctor of Philosophy (Ph.D.), Doctor of Philosophy (Ph.D.) at Simon Fraser University

Chinese, English

Github Skills (13)

data-visualizations10

data-visualisation10

data-visualization10

exploratory-data-analysis10

bokeh10

python10

eda10

data-science9

data-cleaning9

dataprep9

data-preprocessing9

pandas8

numpy8

Programming languages (2)

HTMLPython

Github contributions (5)

sfu-db/dataprep

Jul 2019 - Aug 2022

Open-source low code data preparation library in python. Collect, clean and visualization your data in python with a few lines of code.

Role in this project:

Data Scientist

Contributions:75 reviews, 238 commits, 180 PRs in 3 years 1 month

Contributions summary:Jinglin primarily contributed to the data preparation library by fixing issues related to exploratory data analysis plots. They addressed bugs in the visualization of missing data and correlation matrices, making adjustments to the plot layouts and import orders. Additionally, the user corrected issues related to the number of bars and bins in plots, fixing various visual aspects in the eda module to improve usability and functionality. The user also added documentation.

pythondatadataprepcleaningdatacleaning

sfu-db/aqppp

Jun 2018 - May 2019

Contributions:16 commits, 13 pushes in 11 months

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