Alexander Mattick

Alexander Mattick is a research scientist at Fraunhofer Institute for Integrated Circuits, where he pursues a PhD in Constrained Reinforcement Learning. He obtained a bachelors in Computer Science and a Master’s in Artificial Intelligence from Friedrich Alexander University, where he also previously worked on historical document analysis and recognition. He completed his master’s thesis on the topic “Reinforcement Learning for node selection in Branch-and-Bound” through Fraunhofer IIS, where he currently works in the group Self Learning Systems.

Research Interests

• Reinforcement Learning
• Constrained Reinforcement Learning
• Optimization
• Variational Bayesian Methods

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