Jundong Li
Associate Professor
Charlottesville, Virginia, United States
Summary
🤩
Rockstar🎓
Top SchoolJundong Li is an academic software engineer and Associate Professor at the University of Virginia with 11 years of research and teaching experience specializing in machine learning and feature selection. He holds a PhD from Arizona State University and has a track record of contributing practical, open-source tools—most notably improvements to the scikit-feature repository where he refined ReliefF, Fisher Score and graph-based affinity constructions using NumPy and scikit-learn. His work bridges rigorous research and reproducible tooling, mentoring students while collaborating with industry as a LinkedIn Scholar. Based in Charlottesville, he blends deep theoretical knowledge with hands-on data-science development, often focusing on interpretable feature engineering methods that translate directly into applied workflows.
11 years of coding experience
12 years of employment as a software developer
Master of Science (M.Sc.) Computer Science, Master of Science (M.Sc.) Computer Science at University of AlbertaDoctor of Philosophy (PhD) Computer Science, Doctor of Philosophy (PhD) Computer Science at Arizona State UniversityBachelor of Engineering (B.Eng.) Software Engineering, Bachelor of Engineering (B.Eng.) Software Engineering at Zhejiang University
English, Chinese
Github Skills (11)
scikit-learn10
algorithms10
machine-learning10
python10
feature-selection10
numpy10
scikit10
data-analysis9
statistical-models8
graph-theory8
linear-algebra8
Programming languages (1)
Github contributions (5)
jundongl/scikit-feature
Nov 2015 - Sep 2020
open-source feature selection repository in python
Role in this project:
Data Scientist
Data ScientistContributions:75 commits, 8 PRs, 71 pushes in 4 years 10 months
Contributions summary:Jundong primarily contributed to the feature selection methods implemented within the `scikit-feature` repository. Their commits focus on updating and refining existing feature selection algorithms, including ReliefF, Fisher Score, and others, as well as creating and modifying example files to test them. The user also made modifications to functions related to constructing affinity matrices, suggesting a focus on graph-based feature selection techniques. Their work involved the use of Python and related libraries such as `NumPy` and `scikit-learn`, which are core to the repository's functionality.
dtwpythonscikit-learnselectionfeature-selection
suhangwang/FS_Package_DMML
Dec 2014 - Nov 2015
Contributions:286 commits, 174 pushes in 11 months
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