Joel Frank is a software engineer and PhD candidate focused on the intersection of machine learning and cybersecurity, currently building systems at Meta with 11 years of engineering experience. His research blends hands-on tooling and high-impact publications—ranging from automated Rust-based smart-contract analysis that scaled to scan entire blockchains to ICML and Usenix Security papers on deepfake detection and adversarial attacks on Bayesian neural networks. He teaches and mentors, co-designing a practical ML & IT-security course and supervising experiments that bridge academic rigor with reproducible engineering. Based in London, Joel pairs production-grade engineering (PyTorch, Pyro, TensorFlow, Rust, Go) with deep domain expertise in safety, interpretability, and adversarial robustness, often turning novel research ideas into open-source tools and media-covered projects.
11 years of coding experience
3 years of employment as a software developer
Master of Science - MS, IT-Security, Master of Science - MS, IT-Security at Ruhr-Universität Bochum
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