معرفی
Laurenz Wiskott is a Professor of Computer Science at the Ruhr-Universität Bochum (RUB), leading the Theory of Neural Systems group at the Institut für Neuroinformatik. His research focuses on machine learning, computational neuroscience, and neuro-inspired AI. He holds affiliations with multiple departments including the Department of Physics and Astronomy, Research Department of Neuroscience, and the International Graduate School of Neuroscience. Wiskott earned his PhD in Physics from RUB in 1995, followed by postdoctoral research at the Salk Institute and Humboldt University Berlin. He has authored over 100 publications, including influential work on Slow Feature Analysis (SFA) and its applications in vision, memory, and reinforcement learning. Notable awards include the 'Best Paper Award' at Machine Learning conferences and recognition for his educational tools like the student advising dashboard. His teaching spans courses on machine learning, computational neuroscience, and AI fundamentals. Current projects include explainable AI, curriculum analytics, and neuro-inspired RL efficiency.
Education:
- PhD in Physics (1995), Ruhr-Universität Bochum
- Diploma in Physics (1990), University of Osnabrück
- Studies in Physics (1985–1989), University of Göttingen
Research Interests: Slow Feature Analysis, generative models of episodic memory, reinforcement learning, human-centered AI ethics, curriculum analytics, and neuro-inspired machine learning architectures.
Grants & Projects: Leads the HUMAINE (Human-Centered AI) initiative and contributed to EU-funded projects like NET-humAIn. Active in educational tech through KI:edu.nrw, developing dashboards for student advising.
Labs/Teams: Directs the Theory of Neural Systems lab at INI, collaborating with the Center for Mind and Cognition and the Machine Learning & AI group at RUB.
