Matthias Boehm

Graz University of Technology
Computer Science and Biomedical Engineering
Institute of Interactive Systems and Data Science
BMVIT endowed chair for Data Management
Office: 8010 Graz, Inffeldgasse 13/V, PZ 205 014
Matthias Boehm

Matthias Boehm is a full professor for data management in data science at Graz University of Technology, Austria, where he holds a BMVIT-endowed chair for data management. Prior to joining TU Graz in 2018, he was a research staff member at IBM Research - Almaden, USA, with a major focus on compilation and runtime techniques for declarative, large-scale machine learning. Since 2015, Matthias also serves as a PMC member for Apache SystemML. He 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, and a 2016 SIGMOD Research Highlight Award.


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 me directly via email.


This publication list covers the last five years. For a full list see DBLP and Google Scholar.
  • Matthias Boehm, Berthold Reinwald, Dylan Hutchison, Prithviraj Sen, Alexandre V. Evfimievski, Niketan Pansare: On Optimizing Operator Fusion Plans for Large-Scale Machine Learning in SystemML. PVLDB 2018 11(12). [paper, slides, poster]
  • Ahmed Elgohary, Matthias Boehm, Peter J. Haas, Frederick R. Reiss, Berthold Reinwald: Compressed Linear Algebra for Large-Scale Machine Learning. VLDB Journal 2018 27(5). [paper]
  • Matthias Boehm: Apache SystemML – Declarative Large-Scale Machine Learning. Encyclopedia of Big Data Technologies 2018. [paper]
  • Niketan Pansare, Michael Dusenberry, Nakul Jindal, Matthias Boehm, Berthold Reinwald, Prithviraj Sen: Deep Learning with Apache SystemML. SysML 2018. [paper]
  • Arun Kumar, Matthias Boehm, Jun Yang: Data Management in Machine Learning: Challenges, Techniques, and Systems. SIGMOD 2017. [paper, slides, video]
  • Ahmed Elgohary, Matthias Boehm, Peter J. Haas, Frederick R. Reiss, Berthold Reinwald: Scaling Machine Learning via Compressed Linear Algebra. SIGMOD Record 2017 46(1). [paper]
  • Tarek Elgamal, Shangyu Luo, Matthias Boehm, Alexandre V. Evfimievski, Shirish Tatikonda, Berthold Reinwald, Prithviraj Sen: SPOOF: Sum-Product Optimization and Operator Fusion for Large-Scale Machine Learning. CIDR 2017. [paper, slides]
  • Ahmed Elgohary, Matthias Boehm, Peter J. Haas, Frederick R. Reiss, Berthold Reinwald: Compressed Linear Algebra for Large-Scale Machine Learning. PVLDB 2016 9(12). [paper, slides, poster]
  • Matthias Boehm, Michael Dusenberry, Deron Eriksson, Alexandre V. Evfimievski, Faraz Makari Manshadi, Niketan Pansare, Berthold Reinwald, Frederick Reiss, Prithviraj Sen, Arvind Surve, Shirish Tatikonda: SystemML: Declarative Machine Learning on Spark. PVLDB 2016 9(13). [paper, slides]
  • Matthias Boehm, Alexandre V. Evfimievski, Niketan Pansare, Berthold Reinwald: Declarative Machine Learning - A Classification of Basic Properties and Types. CoRR 2016 abs/1605.05826. [paper]
  • Arash Ashari, Shirish Tatikonda, Matthias Boehm, Berthold Reinwald, Keith Campbell, John Keenleyside, P. Sadayappan: On Optimizing Machine Learning Workloads via Kernel Fusion. PPOPP 2015. [paper]
  • Botong Huang, Matthias Boehm, Yuanyuan Tian, Berthold Reinwald, Shirish Tatikonda, Frederick R. Reiss: Resource Elasticity for Large-Scale Machine Learning. SIGMOD 2015. [paper, slides, poster]
  • Matthias Boehm: Costing Generated Runtime Execution Plans for Large-Scale Machine Learning Programs. CoRR 2015 abs/1503.06384. [paper]
  • Matthias Boehm, Douglas R. Burdick, Alexandre V. Evfimievski, Berthold Reinwald, Frederick R. Reiss, Prithviraj Sen, Shirish Tatikonda, Yuanyuan Tian: SystemML's Optimizer: Plan Generation for Large-Scale Machine Learning Programs. IEEE Data Eng. Bull. 2014 37(3). [paper]
  • Matthias Boehm, Dirk Habich, Wolfgang Lehner: On-Demand Re-Optimization of Integration Flows. Inf. Syst. 2014 45. [paper]
  • Peter D. Kirchner, Matthias Boehm, Berthold Reinwald, Daby M. Sow, J. Michael Schmidt, Deepak S. Turaga, Alain Biem: Large Scale Discriminative Metric Learning. IPDPS Workshop ParLearning 2014. [paper, slides]
  • Matthias Boehm, Shirish Tatikonda, Berthold Reinwald, Prithviraj Sen, Yuanyuan Tian, Douglas Burdick, Shivakumar Vaithyanathan: Hybrid Parallelization Strategies for Large-Scale Machine Learning in SystemML. PVLDB 2014 7(7). [paper, slides, poster]


This list summarizes PC memberships and review activities, again of the last five years.
  • Program Committee SIGMOD 2019, PVLDB 2019, ICDE 2019, EDBT 2019
  • Program Committee PVLDB 2018, EDBT 2018 Industry, DEEM 2018, WebDB 2018, EBDVF 2018
  • Program Committee ICDE 2017 Demo, DEEM 2017
  • Program Committee SSDBM 2015
  • Journal Reviewer SIGMOD Record 2018/17, TKDE 2017/16, ACM Computing Surveys 2016, IBM Journal R&D 2016, Information Systems 2015
  • External Reviewer CIKM 2016, SIGMOD Record 2015


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.