School of Computer Science PGR Seminar Berné Nortier and Mirza Hossain

Berné Nortier will present Distilling the Data Deluge: Network Renormalisation as a technique for coarse-graining generalised models of complex systems (Work in progress)

Abstract: Complexity surrounds us. While expanding data storage highlights just how interconnected most systems are, the sheer scale of these networks makes them increasingly difficult to study directly, highlighting the pressing need for tools that can analyse such systems at coarser levels.

Here, we develop a framework for the investigation of complex networks at different resolutions by formulating the geometric Renormalisation Group for bipartite, higher-order and metadata-rich networks. We do this by embedding them into an underlying hidden metric space and derive attributes of downscaled replicas which hold true for the original system. We then show evidence of geometric scaling under this transformation for empirical networks and discuss applications of this multi-resolution approach to large-scale systems where downscaled network replicas could serve as alternatives for analysis and for fast-track exploration of rough parameter spaces.

Mirza Hossain will present AUC Is Not Enough: Compression can preserve classifier performance while altering interpretable structure in pathology embeddings

Abstract: Foundation models have become the standard way to turn pathology slides into data: each tissue patch is encoded as a high-dimensional embedding, and downstream models are trained on those frozen vectors. At cohort scale this creates a real storage and transfer bottleneck, and compression is the obvious fix. Our earlier work showed that nine dimensions are enough to recover 99% of classification AUC across several datasets and models.

But these embeddings are increasingly reused for mechanistic interpretability workflow. Sparse autoencoders (SAE) decompose embeddings into features, and pathologists review each feature by looking at the patches that activate it most strongly. This work asks whether compression that preserves AUC also preserves the evidence those experts reviewed.