Summary
Stanislav Chekmenev is a postdoctoral researcher based in Berlin specializing in Geometric Deep Learning and AI for Science, with eight years of experience bridging machine learning, physics, and biology. He brings deep academic rigor from a PhD in Nuclear Physics and hands-on applied ML across industry projects—from anomaly detection in industrial vision to traffic-light automation with multi-agent RL. As a founding ML scientist he prototyped privacy-preserving generative systems combining LLM fine-tuning and fully homomorphic encryption, including a mobile-local QA system with a Rust backend. He teaches and mentors practitioners in graph neural networks and debugging at Data Science Retreat, translating cutting-edge research into practical skills. Stanislav’s work consistently blends creative problem-solving with strong engineering discipline, often applying geometric and probabilistic methods to real-time, safety-critical pipelines. Colleagues value his ability to move projects from theoretical concepts to robust, deployable prototypes.
7 years of coding experience
6 years of employment as a software developer
Doctor of Philosophy - PhD Nuclear Physics, Doctor of Philosophy - PhD Nuclear Physics at RWTH Aachen University
Master’s Degree Elementary Particle Physics, Master’s Degree Elementary Particle Physics at Peter the Great St.Petersburg Polytechnic University
English, Russian, German