Benjamin Striner
Senior Machine Learning Engineer at 3M
Washington, District of Columbia, United States
Summary
👤
Senior🎓
Top SchoolBenjamin Striner is a Senior Machine Learning Engineer in Washington, D.C. with nine years of experience blending research-grade ML and full‑stack engineering to deliver production systems. He built scalable, serverless ML pipelines at CMU—leveraging SageMaker, MLflow and AWS for real‑time audio profiling—and now applies that operational rigor to enterprise problems at 3M. His open-source work includes implementing adversarial GAN architectures in Keras, reflecting hands‑on expertise in generative models and training mechanics. Earlier experience as an expert witness honed his ability to translate technical complexity into clear, auditable findings for engineering and legal audiences. He holds an MS in Machine Learning from Carnegie Mellon and a BA in Neuroscience, a combination that informs his focus on signal and voice applications.
9 years of coding experience
1 year of employment as a software developer
Master of Science - MS, Machine Learning, 3.85, Master of Science - MS, Machine Learning, 3.85 at Carnegie Mellon UniversityBachelor of Arts (BA), Neuroscience, Bachelor of Arts (BA), Neuroscience at Oberlin College
Stackoverflow
457reputation
482kreached
10answers
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Github Skills (12)
keras10
machine-learning10
python10
machine-learning-models10
generative-adversarial-network10
tensorflow9
neural-network6
callback6
recurrent-neural-networks6
logging6
lstm6
ubuntu6
Programming languages (1)
Github contributions (5)
bstriner/keras-adversarial
Dec 2016 - Jan 2017
Keras Generative Adversarial Networks
Role in this project:
ML Engineer
ML EngineerContributions:53 commits in 1 month
Contributions summary:Benjamin's contributions primarily involve implementing and refining an adversarial model within a Keras framework. The code changes show the creation of an `AdversarialModel` class, the definition of generator and discriminator models, and the configuration of the training process. They also worked on loss functions, optimizers, and metrics tailored for adversarial training, demonstrating a focus on the core architecture and mechanics of generative adversarial networks (GANs).
deep-learningadversarialtheanoneural-networksgenerative-adversarial-networks
bstriner/gym-traffic
Dec 2016 - Jan 2017
Contributions:17 commits in 16 days
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