Big Data Engineering

Research Profile

Our overall mission is to simplify data science by providing high-level, data science-centric abstractions and building systems and tools to execute these tasks in an efficient and scalable manner. To this end, the primary research focus is on ML systems internals for the end-to-end data science lifecycle (from data integration and preparation, over efficient model training, to debugging and serving), large-scale, distributed data management and analysis, as well as infrastructure and benchmarks for data and ML systems.

Current Projects: Apache SystemDS (An open source ML system for the end-to-end data science lifecycle), and DAPHNE (an open and extensible system infrastructure for integrated data analysis pipelines, with Know Center, AVL, DLR, ETH Zurich, HPI Potsdam, ICCS, Infineon, Intel, ITU Copenhagen, KAI, TU Dresden, Uni Maribor, Uni Basel)

Completed Projects: ExDRa (06/2019-08/2022, exploratory data science and federated ML over raw data, w/ Siemens, DFKI, TU Berlin, and TU Graz)

PhD Committee Memberships (completed): Andreas Kunft (TU Berlin, 2019), Joseph Vinish D'Silva (McGill University, 2020), Shaoduo Gan (ETH Zurich, 2021), Gábor Gévay (TU Berlin, 2022), Clemens Lutz (TU Berlin, 2022), Alexander Renz-Wieland (TU Berlin, 2022), Gencer Sümbül (TU Berlin, 2023), Martino Ciaperoni (Alto University, 2023), Philipp Grulich (TU Berlin, 2023), Viktor Rosenfeld (TU Berlin, 2023), Lisa Raithel (TU Berlin, 2024)