Sharon Li - Associate Professor

Sharon Li

Associate Professor

Madison, Wisconsin, United States

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Summary

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Senior

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Top School

Sharon Li is an Assistant Professor of Computer Science at the University of Wisconsin–Madison and a research scientist with 11 years of experience bridging deep learning research and real-world deployment. Trained under Turing laureate John E. Hopcroft with a PhD from Cornell and a postdoc at Stanford, she brings a rigorous theoretical foundation paired with industry experience from internships at Google AI and a research scientist stint at Facebook AI. Her work focuses on reliable open-world machine learning—developing algorithms that remain safe and adaptive amid evolving, unpredictable data streams. She combines hands-on model engineering (including implementing DenseNet in TensorFlow for CIFAR-10) with systems-level thinking about failure modes in deployed ML, and was recognized on Forbes 30 Under 30 in Science. Colleagues describe her research approach as both practically minded and deeply analytical, shaped by sustained movement between academia and industry.

11 years of coding experience

7 years of employment as a software developer

Bachelor of Engineering (BEng), Bachelor of Engineering (BEng) at Shanghai Jiao Tong University

Doctor of Philosophy (PhD), Doctor of Philosophy (PhD) at Cornell University

Postdoc, Computer Science, Postdoc, Computer Science at Stanford University

Chinese, English

Github Skills (6)

machine-learning10

tensorflow10

python10

mask-rcnn9

faster-rcnn9

data-augmentation8

Programming languages (3)

HTMLJupyter NotebookPython

Github contributions (5)

YixuanLi/densenet-tensorflow

Sep 2016 - Mar 2018

DenseNet Implementation in Tensorflow

Role in this project:

ML Engineer

Contributions:28 commits, 4 PRs, 11 pushes in 1 year 6 months

Contributions summary:Sharon focused on implementing a DenseNet model for CIFAR10 within the TensorFlow framework. Their contributions involve modifying the original ResNet implementation to incorporate DenseNet architecture, including defining the model structure, and adjusting training parameters. Furthermore, they added and modified arguments and parameters related to model depth and training schedules to optimize performance. The commits demonstrate iterative development and refinement of the DenseNet model.

deep-learningdensenettensorflow

YixuanLi/LEMON

Jan 2015 - Feb 2016

Contributions:22 commits, 1 PR, 21 pushes in 1 year 1 month

lemonoverlapping-community-detectionprecisionhigh-precisionoverlapping

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