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Prof. Dr. Matthias Böhm


Technische Universität Berlin
Big Data Engineering

Ernst-Reuter-Platz 7, 10587 Berlin

© Böhm

Prof. Dr. Matthias Böhm

Research Group Lead

Before joining BIFOLD Prof. Dr. Matthias Boehm was a BMK-endowed professor for data management at Graz University of Technology, Austria, and a research area manager for data management at the co-located Know-Center GmbH. His cross-organizational research group focuses on high-level, data science-centric abstractions as well as systems and tools to execute these tasks in an efficient and scalable manner. Prior to joining TU Graz in 2018, he was a research staff member at IBM Research - Almaden, CA, USA, with a major focus on compilation and runtime techniques for declarative, large-scale machine learning in Apache SystemML. Matthias received his Ph.D. from Dresden University of Technology, Germany in 2011 with a dissertation on cost-based optimization of integration flows. His previous research also includes systems support for time series forecasting as well as in-memory indexing and query processing. Matthias is a recipient of the 2016 VLDB Best Paper Award, a 2016 SIGMOD Research Highlight Award, a 2016 IBM Pat Goldberg Memorial Best Paper Award, and the 2021 SIGMOD DS&E Best Paper Award. At the BIFOLD Prof. Dr. Matthias Böhm is a Full Professor and Chair of Big Data Engineering (DAMS) Group.

Current Projects: Apache SystemDS (An open source ML system for the end-to-end data science lifecycle), ExDRa (exploratory data science and federated ML over raw data, w/ Siemens, DFKI, and TU Berlin), DAPHNE (an open and extensible system infrastructure for integrated data analysis pipelines, w/ AVL, DLR, ETH Zurich, HPI Potsdam, ICCS, Infineon, Intel, ITU Copenhagen, KAI, TU Dresden, Uni Maribor, Uni Basel), and ReWaste F (recycling and recovery of waste for future, 4 scientific and 14 industrial partners)


2021 SIGMOD DS&E Best Paper Award
2016 IBM Pat Goldberg Memorial Best Paper Award
2016 SIGMOD Research Highlight Award
2016 VLDB Best Paper Award

  • System-oriented research for the end-to-end data science lifecycle from data integration, preparation, cleaning, over efficient ML training, to model debugging and deployment,
  • Large-scale, distributed machine learning and data management,
  • Query optimization (in ML systems, integration systems, database systems), and
  • In-memory indexing, query processing, and high-performance computing.

Sebastian Baunsgaard, Matthias Boehm, Ankit Chaudhary, Behrouz Derakhshan, Stefan Geißelsöder, Philipp Marian Grulich, Michael Hildebrand, Kevin Innerebner, Volker Markl, Claus Neubauer, Sarah Osterburg, Olga Ovcharenko, Sergey Redyuk, Tobias Rieger, Alireza Rezaei Mahdiraji, Sebastian Benjamin Wrede, Steffen Zeuch

ExDRa: Exploratory Data Science on Federated Raw Data

June 18, 2021

BIFOLD Update| Apr 22, 2024

“POLAR” lowers the adoption barrier for adaptive query processing in database systems

A preprint by BIFOLD researchers titled "POLAR: Adaptive and Non-invasive Join Order Selection via Plans of Least Resistance" is set to be presented at the VLDB conference in 2024. The database engineering paper introduces a technique for reordering joins that is adaptive, with a focus on non-invasive integration and low overhead. 

Big Data Engineering| Aug 28, 2023

Professor Dr. Pinar Tözün joins BIFOLD as a Visiting Scientist

Beginning on August 28, 2023, Professor Tözün will join BIFOLD as a Visiting Scientist. Above all, she will collaborate with Prof. Dr. Matthias Böhm and the Big Data Engineering group.

Data Management| Jun 26, 2023

8 researchers represented BIFOLD at SIGMOD 2023

Eight members of the BIFOLD team took the chance to showcase their recent work at SIGMOD 2023 in Seattle through a diverse array of presentations, including research papers, workshop papers, and a demo paper – all of them underscoring the institute's commitment to cutting-edge research in the field of data management. 

BIFOLD Update| Aug 06, 2020

An overview of the current state of research in BIFOLD

Since the official announcement of the Berlin Institute for the Foundations of Learning and Data in January 2020, BIFOLD researchers achieved a wide array of advancements in the domains of Machine Learning and Big Data Management as well as in a variety of application areas by developing new Systems and creating impactfull publications. The following summary provides an overview of recent research activities and successes.