Alice聽Zheng
Seattle, Washington, United States
Summary
馃ぉ
Rockstar馃帗
Top SchoolAlice Zheng is a seasoned machine learning leader with 22 years of experience building production ML systems, leading teams, and shipping algorithms for advertising ranking, budgeting, and monitoring at companies including Amazon and Dato. She combines deep research roots鈥攆rom a CMU postdoc and Microsoft research work on system diagnosis and graph analysis鈥攚ith hands-on product delivery, having led ranking science and demand-side optimization for Amazon Display Advertising. Author of two O鈥橰eilly books on feature engineering and model evaluation, she also maintains practical teaching code such as the feature-engineering book repo that demonstrates real-world transformations and clustering examples. Known for bridging technical and organizational challenges, she designs analytics and real-time monitoring systems as well as automated tuning and allocation algorithms. Based in Seattle, she enjoys public speaking and community organization, bringing both academic rigor and pragmatic engineering to complex ML problems. An underappreciated strength is her sustained focus on log-analysis and automatic system diagnosis that enables robust model operations at scale.
22 years of coding experience
14 years of employment as a software developer
B.A., Mathematics and Computer Science, B.A., Mathematics and Computer Science at University of California, Berkeley
Github Skills (14)
scikit-learn10
data-analysis10
pandas10
jupyter-notebook10
machine-learning10
feature-engineering10
python10
scikit10
clustering9
data-visualisation9
data-visualization9
data-visualizations9
regression9
tf-idf8
Programming languages (3)
Github contributions (5)
alicezheng/feature-engineering-book
Aug 2017 - Jun 2018
Code repo for the book "Feature Engineering for Machine Learning," by Alice Zheng and Amanda Casari, O'Reilly 2018
Role in this project:
Data Scientist
Data ScientistContributions:9 commits, 2 PRs, 3 pushes in 10 months
Contributions summary:Alice primarily contributed to the project by implementing and updating machine learning examples within Jupyter notebooks. Their work involved exploring log and Box-Cox transformations for the Yelp Reviews dataset, alongside a regression model using interaction features on the UCI news dataset, indicating data analysis and model building activities. The user also updated K-means and TF-IDF examples and added a swissroll clustering example demonstrating their focus on machine learning algorithms. The updates reveal their efforts to enhance and refine existing machine learning examples.
pythondata-sciencedeep-learningmachine-learningalice
liblit/cbiexp
Nov 2003 - Jun 2007
Contributions:101 commits in 3 years 7 months
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