Johannes Maeß

Johannes is a PhD student in the Machine Learning Group at Technische Universität Berlin and a full-time researcher at the Berlin Institute for the Foundations of Learning and Data (BIFOLD) since 2023. He completed his Master’s in Computer Science at TU Berlin and has a rich background in industry, having worked at Amazon Web Services and IBM. At Amazon, he focused on low-latency query processing for the Analytical Database Redshift, and at IBM, he contributed to both engineering and research efforts, notably improving cardinality estimation in databases using novel machine learning methods.

Currently, Johannes’ research revolves around the theoretical foundations of explainability algorithms for neural networks and the application of AI in language education. His work aims to make AI systems more transparent and understandable, bridging advanced theoretical concepts with practical applications in AI and education.