Jacob Kauffmann

Dr. Jacob Kauffmann is a Postdoctoral Researcher at the Technical University of Berlin, specializing in research areas such as Unsupervised Learning, Explainable AI, and Anomaly Detection. His work focuses on enhancing model interpretability in unsupervised techniques and uncovering biases like the Clever Hans effect in machine learning systems.

Education:

• M.Sc. Computer Science, Technische Universität Berlin (2017)

Research Interests: Jacob’s research bridges theoretical foundations and practical applications of explainable AI (XAI) in unsupervised learning. He investigates clustering explanations, anomaly detection mechanisms, and kernel-based methods to improve model transparency. His recent work explores dataset shifts via Wasserstein distances and neural network-driven cluster analysis.

Publications: Dr. Kauffmann’s articles emphasize XAI advancements, such as explainable anomaly detection and model bias analysis. Key themes include algorithmic transparency, dataset transport phenomena, and deep learning applications in unsupervised tasks.

Advising & Grants: No formal advisees listed. Research supported by grants related to XAI and unsupervised learning methodologies (specific grants not detailed).