Gregory Peatfield
Chief Science Officer at Greg Peatfield Consulting
Atkinson, New Hampshire, United States
Summary
👤
Senior🎓
Top SchoolGregory Peatfield is a seasoned technology leader and Chief Science Officer with a decade of focused experience modernizing infrastructure for the post-quantum era and a multi-decade track record leading R&D and product development across medical devices, AI, and cybersecurity. He blends hands-on machine learning engineering—contributing a Dueling network implementation to the Tensorforce reinforcement learning library—with executive experience as an interim CTO/CPO and founding member of Web3 innovation efforts. Gregory has led large technical teams at GE Healthcare and HP, rebuilt failing product lines, and guided regulatory-compliant medical device development, demonstrating a rare mix of deep technical craft and program-level delivery. At QuSmart.AI he’s translating quantum readiness into pragmatic infrastructure solutions, while his consulting practice drives time-to-market for complex regulated products. Colleagues describe him as a systems thinker who prefers practical, maintainable designs—evident in his open-source focus on flexibility and clean refactors.
10 years of coding experience
25 years of employment as a software developer
UMass LowellGeorgetown High
English
Stackoverflow
553reputation
42kreached
8answers
14questions
Github Skills (14)
neural-network10
deep-reinforcement-learning10
machine-learning10
tensorflow10
python10
reinforcement-learning10
controlling9
cross-entropy6
q-learning6
gradient-descent6
deep-learning6
artificial-intelligence6
mnist6
tensorflow-serving6
Programming languages (11)
Github contributions (5)
tensorforce/tensorforce
Oct 2017 - Feb 2018
Tensorforce: a TensorFlow library for applied reinforcement learning
Role in this project:
ML Engineer
ML EngineerContributions:26 commits, 19 PRs, 80 comments in 3 months
Contributions summary:Gregory primarily focused on developing and implementing a Dueling network architecture, a reinforcement learning technique within the TensorFlow library. They introduced the "Dueling" layer, including its implementation details, the necessary network infrastructure, and references to the original research paper. The user also made subsequent refinements to the Dueling layer by adding new features and fixing bugs, with a particular focus on flexibility and code maintainability.
tensorflow-librarytensorforcereinforcement-learningdeep-reinforcement-learningmachine-learning
Mazecreator/TensorFlow-SIRDS
Mar 2017 - Oct 2020
Contributions:21 commits, 17 pushes, 1 branch in 3 years 8 months
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