Eric Tang
Analyst at Auctionomics
California, United States
Summary
👤
Senior🎓
Top SchoolEric Tang is an analyst and Stanford-educated computer scientist with eight years of experience applying machine learning, statistical modeling, and software engineering to problems in auctions, education, and public policy. At Auctionomics he combines theoretical analysis and numerical simulation to evaluate efficiency tradeoffs in high-stakes markets, building on prior research roles at SAIL and Stanford IEPR where he developed PyTorch models and implemented empirical studies of media bias. His internships at AWS and Quill demonstrate production-minded engineering—shipping failure-classification tooling for datacenter launches and NLP-driven feedback systems for student writing. Comfortable across Python, R, Stata, and full-stack web frameworks, he brings a research-first mindset to practical deployments and a recurring interest in projects that improve education and government outcomes. An atypical blend of field experience (refugee support, congressional internship) and rigorous CS/math training informs his pragmatic, policy-aware approach to technical problems.
8 years of coding experience
2 years of employment as a software developer
Phillips Exeter AcademyBachelor’s Degree, Computer Science, Mathematics, Bachelor’s Degree, Computer Science, Mathematics at Stanford University
English, Chinese
Github Skills (26)
optical-flow10
extraction9
quill9
mono9
js8
jupyter-notebook8
react8
rails8
ruby8
learning-management-system8
jupyter7
edtech7
notebook7
javascript7
cuda7
Programming languages (5)
Github contributions (5)
Etang21/octconv-pytorch
Apr 2019 - Jun 2019
A PyTorch implementation of Octave Convolutional layers.
Contributions:45 commits, 62 pushes, 10 branches in 1 month
pytorchdeep-learningconvolutionallayersoctave
empirical-org/nlp-sva-job
Jul 2018 - Aug 2018
Contributions:138 commits, 103 pushes, 22 branches in 1 month
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