I am a graduate student at Harvard University, where I am a member of DASlab, the Data Systems Laboratory. I am advised by Prof. Stratos Idreos. My research interests concern efficient data management implementations that cross the hardware/software-boundary. I am also interested in self-managing and auto-tuning data systems architectures.
Prior to joining Harvard, I spent about a year investigating data processing on modern hardware at the Oracle Labs in Redwood Shores, California. Even earlier in my life I was an undergraduate student at TU Dresden, where I was a part of the database group. In my undergraduate research, I investigated techniques for high-performance in-memory indexing. I was advised by Thomas Kissinger and Prof. Wolfgang Lehner.
My research is supported by a Microsoft Research PhD Fellowship (2016 – 2018).
- Self-designing data systems
- Ever-increasing amounts of data, changing hardware and new applications are forcing us to continuously evolve and redesign the systems that store and process the data we collect and generate. Implementing and tuning new techniques in existing or novel systems is a time-consuming and incremental process that involves careful design, benchmarking and analysis. Self-designing data systems aim to significantly improve the productivity of data system architects and researchers through an improved and, in large parts, automated software design process.
Designing Access Methods: The RUM Conjecture.Proceedings of the 19th International Conference on Extending Database Technology, EDBT 2016, Bordeaux, France, March 15-16, 2016.
Throughout my academic career, I have spent time at Harvard, TU Dresden, Microsoft Research, the Oracle Labs and the IBM Almaden Research Center. I am the recipient of a Microsoft Research PhD Fellowship (2016 – 2018) and a SIGMOD Undergraduate Award (2013).
Please see my resume for more details.
- contact [AT] lukasmaas.com
lastname [AT] g.harvard.edu
- Lukas Maas
Harvard SEAS - DASlab
33 Oxford Street
136 Maxwell Dworkin
Cambridge, MA 02138