Stefan Grafberger

Stefan Grafberger

I am a Ph.D. student at BIFOLD and TU Berlin in the DEEM Lab, conducting research at the intersection of data management and machine learning. I mainly publish at conferences like SIGMOD and VLDB.

My Ph.D. advisors are Sebastian Schelter and Paul Groth. I work on responsible data management (also in collaboration with Julia Stoyanovich). I spent the first three years of my Ph.D. at the University of Amsterdam in the Intelligent Data Engineering Lab, before Sebastian transitioned to TU Berlin. Before my Ph.D., I did my masters at TU Munich with Thomas Neumann and Alfons Kemper and focused on databases.

During my studies, I interned with Microsoft GSL, Amazon Research, Oracle Labs, and worked as a research assistant at TU Munich. I also interned and worked as a working student at TNG Technology Consulting in Munich and worked as a teaching assistant at University of Augsburg.

In the past, I have been working on deequ, a library for ‘unit-testing’ large datasets with Apache Spark, PGX, an in-memory graph analytics framework, and Umbra, a disk-based database with in-memory performance. Currently, I work on mlinspect and mlwhatif. The goal is to diagnose and mitigate robustness and reliability issues in machine learning pipelines.