Ben Fielding

Co-Founder & CEO at Gensyn

Los Angeles, California, United States
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Summary

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Rockstar
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Top School
Ben Fielding is a founder-led AI entrepreneur and CEO with 11 years of experience building decentralized and cost-efficient ML infrastructure through Gensyn, aiming to create a network for machine intelligence. He pairs a PhD in Computer Science and academic research in evolutionary methods for neural architecture with hands-on engineering—contributing to open-source privacy-preserving ML work such as integrating scikit-learn into PySyft for remote model execution. Based in Los Angeles, he has founded and led multiple startups in data privacy and ML, consulted on commercial ML products, and now actively invests in early-stage deep tech companies. His background blends rigorous research, production ML systems, and a rare focus on decentralised compute economics that informs both product and investment choices.
code11 years of coding experience
job5 years of employment as a software developer
bookDoctor of Philosophy - PhD Computer Science, Doctor of Philosophy - PhD Computer Science at Northumbria University
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Github Skills (11)

scikit-learn10
pandas10
machine-learning10
jupyter-notebook10
federated-learning10
python10
data-science10
numpy10
scikit10
deep-learning5
deeplearning-ai5

Programming languages (5)

JavaJavaScriptHTMLJupyter NotebookPython

Github contributions (5)

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OpenMined/PySyft

Dec 2020 - Feb 2021

Perform data science on data that remains in someone else's server
Role in this project:
userData Scientist
Contributions:7 commits, 3 PRs, 1 comment in 1 month
Contributions summary:Ben's primary contribution focused on integrating and demonstrating the use of scikit-learn classifiers within the PySyft framework. They created and refined Jupyter notebooks showcasing the serialization and remote execution of scikit-learn models, specifically using a dataset for fraud detection. Further work involved updating and adapting the codebase to support the use of NumPy arrays, essential for data manipulation within the machine learning workflows. The user also addressed pandas-related warnings and fixed linting issues to improve code quality.
data-sciencedeep-learningsecure-computationpytorchprivacy
Contributions:47 commits in 2 months
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