معرفی
Bert Kappen is a Professor of Physics at Radboud University Nijmegen, affiliated with the Department of Biophysics and the Donders Center for Neuroscience. His research focuses on the intersection of physics, machine learning, and neuroscience, with emphasis on quantum machine learning, stochastic control theory, and Bayesian inference methods applied to neural systems.
His core research integrates statistical physics and quantum mechanics to develop computational methods for AI, exploring how intelligence emerges in biological systems. Key areas include:
- Quantum Machine Learning: Developing quantum algorithms for Boltzmann machines and exploring quantum advantage in optimization
- Path Integral Control: Creating efficient solutions for stochastic optimal control problems in robotics and neuroscience
- Bayesian Inference: Building probabilistic models for medical diagnosis and DNA identification (e.g., Bonaparte system used by Interpol)
- Atomic-scale Neural Networks: Implementing neuromorphic computing using nanoscale atomic switches
Kappen leads an active research group with multiple PhD and Master's students, working on projects ranging from quantum perceptrons to multi-agent UAV control. His publication output shows strong recent focus on quantum computing applications (2020-2025), atomic-scale machine learning implementations, and advanced control theory.
He teaches courses including Introduction to Machine Learning, Advanced Computational Neuroscience, and Statistical Machine Learning at Radboud University. Externally funded projects include EU FP7 initiatives (NETT, CompLACS), NWA Quantum Learning, and collaborations with Thales Nederland.
