Benjamin Andow
Senior Software Engineer at Google
Greater Cleveland United States
Summary
👤
Senior🎓
Top SchoolBenjamin Andow is a Senior Software Engineer and security-focused researcher with 12 years of experience building ML/AI-driven malware and privacy-abuse detection systems for Android at Google. He pairs a PhD in Computer Science with hands-on contributions to production threat intelligence, authoring detection models and infrastructure that reduce user risk while preserving privacy. His background spans industry research at IBM Watson and practical systems work—from kernel and middleware modifications for power and dataflow control to authorization infrastructure for insider risk. An active contributor to open-source data-science tooling, he improved categorical clustering algorithms by strengthening dissimilarity measures and test coverage in kmodes, reflecting an eye for robust, reproducible code. Based in Greater Cleveland, he brings a rare combination of deep privacy-compliance knowledge (CCPA/GDPR/PCI-DSS) and applied ML engineering to secure mobile ecosystems.
12 years of coding experience
9 years of employment as a software developer
Doctor of Philosophy (Ph.D.), Computer Science, Doctor of Philosophy (Ph.D.), Computer Science at North Carolina State UniversityBachelor of Science (B.S.), Computer Science, Bachelor of Science (B.S.), Computer Science at Cleveland State University
Github Skills (5)
python10
numpy10
scikit-learn9
scikit9
unit-testing9
Programming languages (2)
Github contributions (5)
nicodv/kmodes
Jun 2017 - Jun 2017
Python implementations of the k-modes and k-prototypes clustering algorithms, for clustering categorical data
Role in this project:
Data Scientist
Data ScientistContributions:7 commits, 1 PR, 7 comments in 2 days
Contributions summary:Benjamin primarily contributed to the kmodes and kprototypes clustering algorithms, focusing on enhancing dissimilarity measures and improving test coverage. They unified and modified initialization and dissimilarity functions, including error checking. Furthermore, the user added unit tests for the ng_dissim dissimilarity function and implemented bug fixes related to data handling during prediction, ensuring the algorithms function correctly when new data is used.
implementationspythonprincipal-component-analysisk-meansgaussian-mixture-models
benandow/PrivacyPolicyAnalysis
May 2020 - Oct 2022
This repository holds the code for PolicyLint and PoliCheck, which identifies internal contradictions within privacy policies and analyzes data flow to privacy policy consistency.
Contributions:3 commits, 1 push in 2 years 4 months
policyinternalprivacysecurityanalyzes
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