Thilo Spinner

Thilo Spinner is a Researcher affiliated with the Department of Computer Science at ETH Zürich, contributing to the Professorship for Computer Science. His work focuses on visual analytics, explainable AI, and machine learning interpretability. He has developed tools like iNNspector for deep model debugging and explAIner for interactive machine learning frameworks. His research spans topics such as uncertainty-aware dimensionality reduction, language model explainability, and pandemic data visualization (e.g., Coronavis for Covid-19 tracking). Key contributions include frameworks for model interpretability, real-time parameter optimization, and emergency response analysis tools like NEAT. His articles highlight innovations in visualizing complex AI systems and addressing challenges in model transparency and pandemic management.

Education details are not explicitly provided in the text. His research trends emphasize bridging technical AI systems with human-understandable insights through visualization and interactive tools. He has explored diverse applications from healthcare crisis analysis to neural network debugging.