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Sebastian Schelter is a Full Professor at the Berlin Institute for the Foundations of Learning and Data (BIFOLD), where his research focuses on the intersection of data engineering and machine learning. His work aims to lower the technical barriers to effectively and responsibly work with data by designing novel data infrastructure systems for artificial intelligence workloads. The research group he leads conducts fundamental research in areas such as data management and data science, emphasizing the optimization and debugging of machine learning pipelines, as well as ensuring compliance with legal regulations. His contributions to the field have been recognized with several prestigious awards, including the ACM SIGMOD Systems Award and the ACM SIGMOD Demo Runner Award, as well as significant funding from industry sources like Amazon. Previously, he served as an assistant professor at the University of Amsterdam and has experience with major tech companies as a research intern. He is actively involved in academic service as an editor for several journals and is a co-organizer of workshops aimed at bridging gaps in data management and machine learning. In addition to his teaching responsibilities, he advises students and collaborates with various international research institutions.
Berlin Institute on the Foundations of Learning and Data (BIFOLD) • Berlin, Germany
Leading a research group that focuses on data engineering and machine learning.
AI for Retail Lab • Amsterdam, Netherlands
Managed research projects and led innovation initiatives in data engineering.
Amazon Research • New York, USA
Contributed to data science projects and research.
Technical University of Berlin • Berlin, Germany
Conducted research in database systems.
Requirements are consistent for general engineering and computer science programs. Specific advanced master's (MBA/Energy) may require 1 year of professional experience.