Pan Deng

Beijing, China

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Summary

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Rockstar

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Top School

Pan Deng is an experienced data engineer and computational biologist with 11 years building cloud-native data pipelines and analytical tools, particularly in health and genomics. He holds a Ph.D. from Weill Cornell and a bachelor's from Tsinghua, and combines deep domain knowledge in cell and cancer biology with hands-on expertise in AWS, Spark, PostgreSQL, and Redshift. An active open-source contributor, Pan has fixed bugs and added features to high-profile projects like pandas and enhanced linear-algebra routines in the Shogun toolbox, demonstrating both data-manipulation finesse and low-level numeric optimization. He mentors through Google Summer of Code and Insight Data Engineering, translating research-grade methods into production-ready systems. Actively seeking roles in health data science, data engineering, or software engineering, he brings a rare blend of wet-lab understanding and scalable software craftsmanship.

11 years of coding experience

Bachelor’s Degree, Bachelor’s Degree at Tsinghua University

Doctor of Philosophy (Ph.D.), Doctor of Philosophy (Ph.D.) at Weill Cornell Graduate School of Medical Sciences

Stackoverflow

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Github Skills (17)

debug10

c-language10

python10

data-science10

pandas10

cprogramming-language10

linear-algebra10

data-analysis10

unit-testing9

machine-learning8

numpy8

artificial-intelligence7

data-structure7

data-structures7

text-align7

Programming languages (7)

C++CScalaTeXObjective-CJupyter NotebookPython

Github contributions (5)

shogun-toolbox/shogun

Mar 2016 - Jul 2017

Shōgun

Role in this project:

Data Scientist

Contributions:111 commits, 148 PRs, 24 pushes in 1 year 4 months

Contributions summary:Pan contributed to the Shōgun machine learning toolbox by implementing a new method for computing the mean of vectors and matrices using the Eigen3 backend. This involved creating new classes and specializations within the linalg implementation to handle different data types, including int32, int64, and float64. The work included writing unit tests to verify the correctness of the new implementation, showcasing a focus on linear algebra and machine learning algorithms. Furthermore, the user has enhanced the library by implementing rowwise mean methods.

cmakedata-sciencegunc-plus-plusmachine-learning

pandas-dev/pandas

Mar 2016 - Sep 2016

Flexible and powerful data analysis / manipulation library for Python, providing labeled data structures similar to R data.frame objects, statistical functions, and much more

Role in this project:

Data Scientist

Contributions:5 commits, 13 PRs, 55 comments in 6 months

Contributions summary:Pan primarily contributed to bug fixes and enhancements within the pandas library, focusing on data analysis and manipulation functionalities. Their work involved addressing issues related to `crosstab` functions, including handling `dropna` and `margins` parameters. They also added a 'names' parameter to `read_excel`, and updated documentation related to `na_values` and `keep_default_na`.

pythondatalabeled-datamanipulationdataframes

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