Jane Du
Research Scientist Intern at Meta
Urbana, Illinois, United States
Summary
🤩
Rockstar🎓
Top SchoolJane Du is a PhD candidate and Graduate Research Assistant in computer science with a decade of hands-on experience spanning research, teaching, and industry internships at Microsoft and GEOTAB. She blends strong academic foundations from Cornell and UIUC with practical software engineering—building full-stack features such as WinUI audio-preprocessing tools and melspectrogram visualization integrated with NAudio and ML models in Microsoft’s Windows-Machine-Learning repo. Her background includes designing automation for course infrastructure and build-server provisioning, signaling a knack for tooling that improves developer productivity. Based in Urbana, Illinois, she pairs rigorous theoretical knowledge in ML and AI with pragmatic engineering, often focusing on reproducible pipelines and readable, well-refactored code.
10 years of coding experience
3 years of employment as a software developer
Master of Science - MS Computer Science, Master of Science - MS Computer Science at Cornell UniversityDoctor of Philosophy - PhD Computer Science, Doctor of Philosophy - PhD Computer Science at University of Illinois Urbana-ChampaignOntario Scholar, Ontario Secondary School Diploma, Ontario Scholar, Ontario Secondary School Diploma at The Woodlands School
French, Chinese, Japanese, German
Github Skills (7)
file-handling10
machine-learning10
winui10
audio-processing10
csharp10
dotnet-core10
uwp9
Programming languages (5)
Github contributions (5)
microsoft/Windows-Machine-Learning
Jun 2021 - Aug 2021
Samples and Tools for Windows ML.
Role in this project:
Full-stack Developer
Full-stack DeveloperContributions:45 reviews, 65 commits, 9 PRs in 2 months
Contributions summary:Jane contributed to the development of a WinUI application within the repository, likely for audio preprocessing tasks. Their work involved implementing a file picker for .wav files and displaying a melspectrogram image. The commits showcase integration with the NAudio library and the use of machine learning models for audio processing, which included converting the audio into a specific bitmap format. The user refactored code for better readability and correct color display.
machine-learningwindowsaimldeep-learning
janezdu/sparseIRM-resnet-ver
Sep 2024 - Apr 2025
running resnet18 with mnistcifar based off of SparseIRM, https://github.com/x-zho14/SparseIRM
Contributions:2 PRs, 296 pushes, 3 branches in 7 months
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