David Thrower
Chief Technology Officer at Cerebros NotGPT
Florida, United States
Summary
👤
Senior🎓
Top SchoolDavid Thrower is a CTO who blends a pharmaceutical chemistry background with nine years of hands-on AI and MLOps leadership to deliver compliant, production-grade solutions for life sciences, healthcare, and enterprise. He has built novel LLM architectures and a biologically inspired NAS, architected cloud-native, SOC II-ready MLOps on Kubernetes, and led technical strategy that secured pre-seed funding and accelerator placements. Comfortable from lab bench to CI/CD pipelines, he translates domain constraints and regulatory needs into practical AI products that cut costs and streamline workflows. Notably, he tailors models beyond off-the-shelf options—fine-tuning or engineering custom LLMs and AutoML systems to achieve strong performance with limited data.
9 years of coding experience
4 years of employment as a software developer
Bachelor of Science (B.S.), Multi/Interdisciplinary Studies, Bachelor of Science (B.S.), Multi/Interdisciplinary Studies at University of South AlabamaMaster of Science - MS, Pharmacy - Pharmaceutical Chemistry, Master of Science - MS, Pharmacy - Pharmaceutical Chemistry at University of Florida
Github Skills (107)
python10
automl10
mean-square-error10
neurons10
architecture10
gpt10
neural-architecture-search10
nas10
keras-tensorflow10
machine-learning10
llm10
keras10
mlflow9
ai9
neural-network9
Programming languages (9)
Github contributions (5)
david-thrower/residual_MLP_Tests
Dec 2021 - Feb 2022
Tests I am performing on a Python package for building residual multi - layer perceptrons and tandem [any model] -> ResMLPs models, useful for effective transfer learning. A pypi package should be coming soon.
Contributions:57 PRs, 118 pushes, 34 branches in 2 months
pythontandemmulti-layerpypineural-architecture-search
david-thrower/cerebros-core-algorithm-alpha
Nov 2022 - Mar 2023
The Cerebros package is an ultra-precise Neural Architecture Search (NAS) / AutoML that is intended to much more closely mimic biological neurons than conventional neural network architecture strategies.
Contributions:7 releases, 1 review, 189 commits in 4 months
neural-architecture-searchultraautoml-productskeras-tensorflownetwork-architecture
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