Peter Lorenz is an applied scientist with 11 years of experience bridging adversarial ML research and practical AI deployments across academia and industry. He holds a PhD from Heidelberg University and is a Postdoctoral/Research Scientist with a track record in trustworthy AI, diffusion-model forensics, and model extraction attacks—work that led to a EUROCRYPT publication and top-tier conference awards. Peter has implemented defenses and datasets (including diffusion deepfake corpora) and demonstrated counterintuitive findings like AutoAttack suboptimality and deeper-layer model-weight reconstruction feasibility. His industry work spans autonomous driving simulators, PAD face-spoofing fine-tuning, and drone vision, reflecting a focus on safety-critical, real-world systems. An active reviewer for ICML, ICLR, and NeurIPS, he also contributes open-source tools and prototypes (e.g., Virtuals.io and AgentArc) that translate research into usable assets. Based in Switzerland, he is interested in adaptive, evolving AI and pragmatic defenses for open-world deployment.
11 years of coding experience
5 years of employment as a software developer
Computer Science, Computer Science at Wayne State University
Doctor of Philosophy - PhD, Artificial Intelligence, Doctor of Philosophy - PhD, Artificial Intelligence at Heidelberg University
Master of Science - MS, Computer Science, Master of Science - MS, Computer Science at Technische Universität Graz
Contributions:8 releases, 25 commits, 56 pushes in 5 months
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