Christian Contreras-Campana
Research Scientist @Institute Of Control Systems Tuhh
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WORK HISTORY
Research Scientist @Institute Of Control Systems Tuhh
Hamburg, DE
Conducting research with the Intelligent Process Controls (IPC) group to develop reinforcement learning algorithms for real-time electron beam trajectory tuning in particle accelerators. Focused on surpassing expert-level performance by reducing tuning times while maintaining high precision, contributing to automation and AI-driven control in complex physical systems.
EDUCATION
UCLA
Bachelor of Science - B.Sc, Physics and a minor in Mathematics
Rutgers University
Doctor of Philosophy - PhD, Physics
SKILLS
ABOUT CHRISTIAN CONTRERAS-CAMPANA
I am a Senior Research Scientist with a Ph.D. in High-Energy Physics and 10+ years of experience in machine learning, reinforcement learning, and computer vision, delivering high-impact solutions for autonomous systems and simulation platforms.At Hamburg University of Technology, I applied scalable RL algorithms to optimize beam control in simulated particle accelerators, integrating AI with classical control theory. I also developed interactive 3D web-based visualization tools (JavaScript + Blender) to help researchers explore accelerator dynamics more effectively.As a Senior Research Scientist at Automotive Artificial Intelligence (AAI), I led the development of EducAgent, a deep RL-based traffic simulation platform that improved interaction efficiency by 40%, and ReplicaR-Lite, an open-source library achieving 80% behavioral alignment with real-world datasets. Delivering human-like driver agents for a TÜV SÜD-validated ADS simulation platform, demonstrating scalable, safety-critical AI deployment.My expertise spans RL (Ray RLlib, PyTorch, TensorFlow), computer vision (YOLOv5, RetinaFace), and performance-critical C++ in high-fidelity Unity environments. I build scalable, GDPR-compliant pipelines and low-latency simulation frameworks, combining rigorous scientific methodology from CERN with HPC and AI-assisted workflow optimization.Today, I focus on advancing autonomous and agentic systems by integrating reinforcement learning, generative AI, and vision-language-action models to enable adaptive, reliable, and intelligent real-world deployment.
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