Yingyu Liang
Assistant Professor at University of Wisconsin-Madison
Atlanta, Georgia, United States
Summary
🤩
Rockstar🎓
Top SchoolYingyu Liang is an assistant professor and machine learning researcher with 11 years of experience bridging academic research and practical ML engineering. Based in Atlanta, she progressed from a Ph.D. at Georgia Tech and postdoctoral work at Princeton to a faculty role at the University of Wisconsin–Madison, demonstrating a sustained focus on foundational and applied ML. She contributes to open-source projects—such as improving the popular Smooth Inverse Frequency (SIF) sentence embedding implementation—highlighting attention to reproducibility and usable research code. Her work blends theoretical rigor with hands-on improvements to training and similarity-calculation pipelines, making algorithms more accessible to practitioners. Known for iterative refinement of demos and scripts, she brings a user-centric mindset to research tools that often remain academic-only.
11 years of coding experience
Ph.D, Computer Science, Ph.D, Computer Science at Georgia Institute of TechnologyM.S, Computer Science, M.S, Computer Science at Tsinghua University
Github Skills (7)
machine-learning10
nlp10
python10
natural-language-processing10
scikit-learn9
scikit9
tensorflow3
Programming languages (2)
Github contributions (5)
PrincetonML/SIF
Nov 2016 - Oct 2017
sentence embedding by Smooth Inverse Frequency weighting scheme
Role in this project:
ML Engineer
ML EngineerContributions:20 commits, 1 PR, 18 pushes in 11 months
Contributions summary:Yingyu's contributions primarily involve modifications and enhancements to the project's SIF (Smooth Inverse Frequency) embedding implementation. They corrected demo examples, updated training scripts, and refined the SIF embedding and related similarity calculation components. These changes indicate a focus on improving the functionality and usability of the SIF embedding techniques within the context of the project.
smooth-inverse-frequency
PrincetonML/SemanticVector
Nov 2016 - Aug 2017
Word embedding approach based on a dynamic log-linear model
Contributions:15 commits, 14 pushes, 1 branch in 9 months
word-embeddings
Find and Hire Top DevelopersWe’ve analyzed the programming source code of over 60 million software developers on GitHub and scored them by 50,000 skills. Sign-up on Prog,AI to search for software developers.