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Mike Holenderski is an Assistant Professor at Eindhoven University of Technology, working in the System Architecture and Networking group. His research focuses on machine learning, particularly in domains where machine learning systems can outperform humans, such as image classification. He aims to develop models that are robust against noisy, corrupted, or partially missing data, which often arises in dynamic environments. He investigates critical questions regarding how systems can measure their own performance and adapt to changes in processes based on monitored inputs. He is also dedicated to exploring how machine learning agents can effectively deal with corrupt or missing data. His work emphasizes the application of reliable machine learning in industrial systems, with a focus on optimizing manufacturing and maintenance processes. Mike holds a PhD, awarded in 2012, specializing in real-time systems and continues to integrate theory with practical industry collaborations in both European and national projects.
Specific departments like Industrial Design require a portfolio. Programs like Data Science and AI require a GRE-General test for certain international backgrounds.