Prof. Dr. Tilmann Rabl

Andreas Kipf

Affiliation: University of Technology Nuremberg
Title: What We Learned and What We Will Learn



In this talk, I will provide an overview of my past, ongoing, and future ML for systems research, starting with my postdoc years at MIT, where I worked on benchmarking learned indexes, learning cardinality estimates, and developing SageDB, an instance-optimized Data Analytics System. I will also talk about my experience at Amazon, discussing my contributions to intelligent scaling in Amazon Redshift and Text2SQL translations. I will shine some light on the characteristics of cloud data warehouse workloads, and will emphasize the potential of (sub) query caching. Finally, I will talk about my ongoing and future research at UTN, where we work on simplifying data loading with LLMs, correlation-aware column compression, and optimizing queries by learning from query history.

Short CV

Andreas Kipf is a professor of data systems at the University of Technology Nuremberg (UTN). Previously, he was an applied scientist at AWS and a postdoc researcher in the MIT Data Systems Group where he worked with Prof. Tim Kraska. His research explores applications of AI to build next-gen data systems that are efficient and easy to use. Andreas earned his PhD at TUM where he worked with Prof. Alfons Kemper and Prof. Thomas Neumann. During his PhD, he interned with Google in Mountain View & Zurich to work on query-driven materialization and lightweight secondary indexing. Andreas won the 2016 SIGMOD Best Demonstration Award and the 2017 SIGMOD Programming Contest.