Courses
Aktuelle Themen der Theoretischen Informatik I: Networks and High-dimensional Inference
This course covers statistical and computational methods for understanding complex networks — from social platforms to biological systems. The central challenge it addresses is the inverse problem: inferring hidden network structure and interaction rules from indirect or incomplete observations.
Students learn to work with real-world networks that are noisy, partially observed, or entirely unobserved, drawing on tools from statistics, applied mathematics, computer science, and physics. Key outcomes include inferring latent structure, reconstructing interaction networks from time series data, and handling core difficulties like ill-posedness and underdetermination using Bayesian and information-theoretic frameworks.