Hi, I’m Nicholas Tan Jerome (TJ), a postdoctoral researcher and group lead for AI for Experiments at the Karlsruhe Institute of Technology (KIT). I work on scalable machine learning for real-world scientific data—focusing on time-series forecasting, causal discovery, and intelligent analytics for large sensor networks. Within the KATRIN experiment, I develop methods to monitor and predict tritium-source stability for precision neutrino-mass measurements, and I help design analysis and visualization tools used across the experiment.

I supervise students and coordinate data-processing workflows, software infrastructure, and HPC pipelines used in large-scale experimental environments. Recent work includes an accepted paper at the ICDM AI4TS workshop, a submitted DFG proposal (“SCOPE”) on scalable causal discovery for scientific time series, and a Helmholtz AI collaboration proposal on a conversational “Co-Pilot” for autonomous research knowledge management.

Beyond research, I’m interested in building practical tools that help scientists interact with noisy, high-dimensional data and make decisions faster and more transparently.

You can find my work on Google Scholar, explore code and datasets on GitHub, or reach out directly via nicholas.tanjerome[at]kit.edu.