David Shaub
Principal at Shaub Business Group
Los Angeles, California, United States
Summary
👤
Senior🎓
Top SchoolDavid Shaub is a seasoned principal and trial-ready intellectual property and business litigator with over 50 years in private practice and a record of litigating more than 1,000 cases and nearly 100 jury trials. He specializes in transnational IP disputes across biotech, semiconductor, green tech and software, and counsels on complex international transactions and arbitration under AAA, UNCITRAL and other regimes. A former adjunct professor at the University of Michigan, he blends courtroom tenacity with strategic deal-making and has led firms through high-stakes patent, trade secret, antitrust, insurance and commercial disputes. Less obvious: he pairs this legal depth with analytical rigor rooted in a BS in mathematics and physics, and has contributed to open-source testing and ML model-selection projects, reflecting a rare combination of technical literacy and global litigation experience.
11 years of coding experience
Bachelor of Science, Mathematics and Physics, Bachelor of Science, Mathematics and Physics at University of MichiganJ.D., J.D. at University of Michigan Law School
Github Skills (12)
test-automation10
forecasting10
machine-learning10
forecast10
caret10
classification10
r10
testing10
data-analysis9
time-series9
deep-learning8
cran8
Programming languages (9)
Github contributions (5)
robjhyndman/forecast
Aug 2015 - Aug 2020
Forecasting Functions for Time Series and Linear Models
Role in this project:
QA Engineer / Test Automation Engineer
QA Engineer / Test Automation EngineerContributions:68 commits, 32 PRs, 18 comments in 5 years 1 month
Contributions summary:David primarily focused on improving the testing framework for the `forecast` repository. They added multiple unit tests, including those for functions related to time series cleaning, seasonal decomposition, and ARIMA models. They also removed a failing test and updated existing tests to enhance the accuracy and coverage of the testing suite.
forecastingr-packagetidymodelscranrstats
topepo/caret
Jul 2016 - May 2019
caret (Classification And Regression Training) R package that contains misc functions for training and plotting classification and regression models
Role in this project:Data Scientist
Data ScientistContributions:21 commits, 5 PRs, 28 comments in 2 years 10 months
Contributions summary:David primarily focused on improving the grid and random search functionalities within the `caret` R package. Their commits involve modifying model-specific grid definitions for various rule-based classifiers (PART, J48, JRip) and deep neural networks (DNN). They also added default grid configurations and corrected errors in existing search implementations to enhance model training. This indicates a focus on optimizing the model selection process.
r-packagecaretstatisticsregressionregression-models
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