Tom Hanika
Co-Director of the Information Systems and Machine Learning Lab at University of Hildesheim
Research Associate (10%) at University of Kassel
Temporary Lecturer at Humboldt-Universität zu Berlin
My research bridges the mathematical foundations of machine learning with practical applications in explainable Artificial Intelligence. I specialize in the interactive extraction of knowledge from complex explicit and implicit data, ensuring that learning systems are both highly effective and structurally sound. At the core of my foundational work is the study of semantics arising from implicational theories, with a particular emphasis on the interplay between metrics, orders and measures. I am especially driven by the mathematical challenges of learning in high dimensions, as these push the boundaries of what current machine learning models can achieve. On the applied side, I explore unsupervised learning from large text corpora and actively translate my theoretical methods into real-world solutions. I frequently collaborate to apply explanation techniques across diverse fields, including Biology, Geography, Physics, and the Digital Humanities.
Beyond my research, I am a senior developer for the social publication sharing system BibSonomy and maintain the Formal Concept Analysis tool conexp-clj.
