Johannes Maeß

Johannes Maeß is a PhD student in Machine Learning at Technische Universität Berlin and a full-time researcher at the Berlin Institute for the Foundations of Learning and Data (BIFOLD) since 2023. He holds a Master’s in Computer Science from TU Berlin (2020-2022). His research focuses on the theoretical foundations of explainability in neural networks and applications of AI in language education, aiming to bridge theoretical concepts with practical implementations.

• Education:
• PhD in Machine Learning, Technische Universität Berlin (2023–present)
• MSc in Computer Science, Technische Universität Berlin (2020–2022)
• Industry Background:
• Amazon Web Services: Low-latency query processing for Redshift
• IBM: Machine learning-based cardinality estimation in databases

Research interests include Explainable AI, Graph Neural Networks, Computational Neuroscience, Probabilistic ML, and Language Education. He contributes to projects like the SchNetPack toolbox for atomistic machine learning.