Heads

PostDocs

PhD Students

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Open Thesis Topics

We're looking for motivated PhD, master, and bachelor students to join our team. Our research focuses on building ML systems and tools for simplifying the data science liefecycle – from data integration over model training to deployment and scoring – via high-level language abstractions and specialized compiler and runtime techniques. If you're interested, please contact the project supervisor via email.
List of topics

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Publications

• Patrick Damme, Marius Birkenbach, Constantinos Bitsakos, Matthias Boehm, Philippe Bonnet, Florina Ciorba, Mark Dokter, Pawel Dowgiallo, Ahmed Eleliemy, Christian Faerber, Georgios Goumas, Dirk Habich, Niclas Hedam, Marlies Hofer, Wenjun Huang, Kevin Innerebner, Vasileios Karakostas, Roman Kern, Tomaž Kosar, Alexander Krause, Daniel Krems, Andreas Laber, Wolfgang Lehner, Eric Mier, Marcus Paradies, Bernhard Peischl, Gabrielle Poerwawinata, Stratos Psomadakis, Tilmann Rabl, Piotr Ratuszniak, Pedro Silva, Nikolai Skuppin, Andreas Starzacher, Benjamin Steinwender, Ilin Tolovski, Pınar Tözün, Wojciech Ulatowski, Aristotelis Vontzalidis, Yuanyuan Wang, Izajasz Wrosz, Aleš Zamuda, Ce Zhang, Xiao Xiang Zhu: DAPHNE: An Open and Extensible System Infrastructure for Integrated Data Analysis Pipelines. CIDR 2022.

• Svetlana Sagadeeva, Matthias Boehm: SliceLine: Fast, Linear-Algebra-based Slice Finding for ML Model Debugging. SIGMOD 2021. [paper]
• 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. SIGMOD 2021. [paper, slides, ACM DL (OpenAccess)]
• Arnab Phani, Benjamin Rath, Matthias Boehm: LIMA: Fine-grained Lineage Tracing and Reuse in Machine Learning Systems. SIGMOD 2021. [paper]

• Prithviraj Sen, Marina Danilevsky, Yunyao Li, Siddhartha Brahma, Matthias Boehm, Laura Chiticariu, Rajasekar Krishnamurthy: Learning Explainable Linguistic Expressions with Neural Inductive Logic Programming for Sentence Classification. EMNLP 2020.
• Matthias Boehm: Technical Perspective: Declarative Recursive Computation on an RDBMS. SIGMOD Record 2020 49(1). [paper]
• Matthias Boehm, Iulian Antonov, Sebastian Baunsgaard, Mark Dokter, Robert Ginthör, Kevin Innerebner, Florijan Klezin, Stefanie Lindstaedt, Arnab Phani, Benjamin Rath, Berthold Reinwald, Shafaq Siddiqi, Sebastian Benjamin Wrede: SystemDS: A Declarative Machine Learning System for the End-to-End Data Science Lifecycle CIDR 2020. [paper, slides]

• Johanna Sommer, Matthias Boehm, Alexandre V. Evfimievski, Berthold Reinwald, Peter J. Haas: MNC: Structure-Exploiting Sparsity Estimation for Matrix Expressions. SIGMOD 2019. [paper]
• Ahmed Elgohary, Matthias Boehm, Peter J. Haas, Frederick R. Reiss, Berthold Reinwald: Compressed Linear Algebra for Large-Scale Machine Learning. Commun. ACM 2019 62(5). [paper, Link]
• Matthias Boehm, Arun Kumar, Jun Yang: Data Management in Machine Learning Systems. Synthesis Lectures on Data Management 11 (1), Morgan & Claypool Publishers 2019. [book]
• Matthias Boehm, Alexandre V. Evfimievski, Berthold Reinwald: Efficient Data-Parallel Cumulative Aggregates for Large-Scale Machine Learning. BTW 2019. [paper, slides]

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Research Projects

SystemDS

Overview: SystemDS is a versatile system for the end-to-end data science lifecycle from data integration, cleaning, and feature engineering, over efficient, local and distributed ML model training, to deployment and serving. To this end, we aim to provide a stack of declarative languages with R-like syntax for (1) the different tasks of the data-science lifecycle, and (2) users with different expertise. These high-level scripts are compiled into hybrid execution plans of local, in-memory CPU and GPU operations, as well as distributed operations on Apache Spark. In contrast to existing systems - that either provide homogeneous tensors or 2D Datasets - and in order to serve the entire data science lifecycle, the underlying data model are DataTensors, i.e., tensors (multi-dimensional arrays) whose first dimension may have a heterogeneous and nested schema.

Project is hosted at github.com

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Sponsors

Our research group is grateful for funding support from BMVIT, TU Graz, AVL LIST, Infineon Technologies Austria, Magna Steyr Fahrzeugtechnik, voestalpine Stahl Donawitz, and Know-Center.