Marc Bux

Jul – Sep 2013		KTH Royal Institute of Technology, Stockholm, Sweden Research stay funded by the German Academic Exchange Service
Since Nov 2011		Humboldt-Universität zu Berlin, Berlin, Germany Knowledge Management in Bioinformatics Ph.D. student in the DFG-funded research training group SOAMED Topic: Adaptive Scheduling of Scientific Workflows
2009 – 2011		Novartis Pharma AG, Basel, Switzerland Research consultant employed by Elan Computing Schweiz AG
Jul – Sep 2009		Novartis Pharma AG, Basel, Switzerland Intern at Text Mining Services of NIBR IT Research study “Chemical structure search for text corpora”
2008 – 2009		Fraunhofer IPK, Berlin, Germany Student assistant at the research division “Security Technologies”
2005 – 2011		Humboldt-Universität zu Berlin, Berlin, Germany Study of Computer Science with minor subject Cognitive Psychology Thesis: Comparing gene co-expression networks in colorectal cancer Degree: Diploma in Computer Science, 1.0 Award: Best degree in Computer Science at Humboldt-Universität in 2011

Scientific Workflows
Today's scientific experiments typically involve running and refining a series of intertwined computational analysis and visualization tasks on large amounts of data. The complexity of these so-called analysis pipelines resulted in the emergence of e-Science and scientific workflows. Scientific workflows are compositions of sequential and concurrent data processing tasks, whose order is determined by data interdependencies. A scientific workflow is usually specified in the form of a directed, acyclic graph (DAG), in which individual tasks are represented as nodes.

Cloud Computing
Cloud computing describes a recently established form of distributed computing, which provides rentable compute and storage resources on-demand and over the Internet. The "pay-per-use" cost model of commercial cloud providers charges users by the hour and is therefore especially interesting in environments where data analysis is infrequent yet computationally intensive.

Next Generation Sequencing
Genomic sequencing aims at revealing the ordered sequence of nucleic acids (DNA) in a given sample, such as a human chromosome. A new generation of sequencing machines is capable of sequencing millions of short DNA reads in parallel, leading to many interesting challenges in data management and processing.

M. Bux, J. Brandt, C. Witt, J. Dowling, U. Leser (2017), Hi-WAY: Execution of Scientific Workflows on Hadoop YARN, 20th International Conference on Extending Database Technology (EDBT), Venice, Italy. (Paper)

A. Bessani, J. Brandt, M. Bux, V. Cogo, J. Dowling, A. Gholami, M. Hummel, M. Ismail, E. Laure, U. Leser, J.-E. Litton, R. Martinez, S. Niazi, J. Reichel (2015), BiobankCloud: a Platform for the Secure Storage, Sharing, and Processing of Large Biomedical Data Sets, First International Workshop on Data Management and Analytics for Medicine and Healthcare, Hawaii, USA. (Paper)

M. Bux, J. Brandt, C. Lipka, K. Hakimzadeh, J. Dowling, and U. Leser (2015), SAASFEE: Scalable Scientific Workflow Execution Engine, PVLDB 8(12): 1892–1903, Hawaii, USA. (Paper)

J. Brandt, M. Bux, U. Leser (2015), Cuneiform – A Functional Language for Large Scale Scientific Data Analysis, in Proceedings of the Workshops of the EDBT/ICDT, volume 1330, pages 17–26, Brussels, Belgium. (Paper)

M. Bux, U. Leser (2014), DynamicCloudSim: Simulating Heterogeneity in Computational Clouds, Future Generation Computer Systems 46(C): 85–99. (Paper)

S. Wandelt, J. Starlinger, M. Bux, U. Leser (2013), RCSI: Scalable similarity search in thousand(s) of genomes, PVLDB 6(13): 1534–1545, Hangzhou, China. (Paper)

M. Bux, U. Leser (2013), DynamicCloudSim: Simulating Heterogeneity in Computational Clouds, Int. Workshop on Scalable Workflow Enactment Engines and Technologies (SWEET'13), in conjunction with ACM SIGMOD Conference, New York, USA. (Paper, Slides)

S. Wandelt, M. Bux, U. Leser (2013), Trends in Genome Compression, Journal of Current Bioinformatics. (Paper)

M. Bux, U. Leser (2013), Parallelization in Scientific Workflow Management Systems, Technical Report CoRR/arXiv:1303.7195. (Paper)

S. Wandelt, A. Rheinländer, M. Bux, L. Thalheim, B. Haldemann, U. Leser (2012), Data Management Challenges in Next Generation Sequencing, Datenbank-Spektrum 12(3):161–171. (Paper)

M. Bux (2011), Comparing Literature-enriched Experimental Gene Co-expression Networks in Colorectal Cancer, Diploma Thesis, Humboldt-Universität zu Berlin. (Exposé)