SIGMOD Paper Accepted!

Our paper titled “Where is My Training Bottleneck? Hidden Trade-Offs in Deep Learning Preprocessing Pipelines” has been accepted for publication at the SIGMOD 2022 conference. In this paper, we provide an in-depth analysis of data preprocessing pipelines from four different machine learning domains. We introduce a new perspective on efficiently preparing datasets for end-to-end deepContinue reading “SIGMOD Paper Accepted!”

Project Funded!

I’m happy to announce that we’ve received funding for a new research project at TUM, titled “Federated Learning for Non-Invasive Condition Monitoring”. The funding is sufficient to support one PhD student for three years (see job posting). Many thanks to the Federal Ministry for Economic Affiars and Energy (BMWi) for funding this exciting research!