H Mcmahan
Seattle, Washington, United States
Summary
🤩
Rockstar🎓
Top SchoolBrendan Mcglynn is a Security Analyst with seven years of hands-on experience and a Master's in Computing Security from Rochester Institute of Technology, currently working on Mandiant Managed Defense at Google Cloud from Reston, VA. He brings a strong incident response and threat emulation background from internships and roles at MITRE and Mandiant where he replicated APT behaviors in Caldera, built Kibana dashboards, and authored ATT&CK-based playbooks. Early work at Henry Schein gave him practical mobile forensics and systems-administration experience, rounding out his investigative skills. Unusually for a security operations practitioner, he has contributed to the tensorflow-federated open-source project—adding model abstractions and client-side training logic—highlighting an ability to bridge ML tooling and security use cases. An active member of RITSEC, he focuses on turning complex adversary behaviors into actionable detection and response workflows.
7 years of coding experience
17 years of employment as a software developer
Doctor of Philosophy - PhD, Computer Science, Doctor of Philosophy - PhD, Computer Science at Carnegie Mellon UniversityBachelor of Arts - BA, Mathematics, Bachelor of Arts - BA, Mathematics at Whitman College
Github Skills (8)
tensorflow210
machine-learning10
tensorflow10
trainings10
python10
federated-learning10
modeling10
deep-learning9
Programming languages (3)
Github contributions (5)
google-parfait/tensorflow-federated
Nov 2018 - Dec 2022
An open-source framework for machine learning and other computations on decentralized data.
Role in this project:
ML Engineer
ML EngineerContributions:1 release, 86 commits, 1 tag in 4 years 2 months
Contributions summary:H's commits primarily involve the creation and modification of files related to model definition and training within the `tensorflow-federated` repository. Their contributions include the introduction of a new `Model` abstraction, implementation of example models, and modifications to client-side TensorFlow computations for federated learning algorithms such as Federated Averaging. These changes demonstrate a focus on defining and preparing models for federated training scenarios.
pytorchdeep-learningmachine-learningsecure-computationfederated-learning
ZeitgeistQIAN/federated
Apr 2019 - May 2019
A framework for implementing federated learning
Contributions:5 commits in 1 month
pytorchdeep-learningmachine-learningfederated-learningfederated
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