Dr. Mike Sips

Function and Responsibilities:

Research Interests:

I am cooperate with an amazing set of colleagues pursuing research on many diverse projects in big data analytics. Our flagship projects are GeoMultiSens and the Scalable Scientific Data Explorer. Here is a high-level sampling:
  • Scalable Algorithms using GPU's with Tobias Rawald.
  • Automatic generation of Visual Analytics environments based on User Activities with Nils Goldammer and Martina Scholz.
  • Interactive Visual Approach to support building surrogate models with Janis Jatnieks.
Recent Talks: 
  • Wie Unternehmen von Geofernerkundungsdaten profitieren können. - IHK Berlin and Bitkom Big Data Summit Berlin (together with Andre Hollstein), Berlin, September 2016 [Talk-Hollstein] [Talk-Sips]
  • GeoMultiSens - Scalable Multisensoral Analysis of Satellite Remote Sensing Data. - ESA Open Science 2016, ESA-ESRIN, Frascati (Rom), Italy, September 2016
  • Fast Recurrence Quantification Analysis and its Potential for Visual Analytics. - Perspectives in Non-Linear Dynamics (PNLD 2016), Humboldt-Universität zu Berlin, July 2016
  • Toward a Scientific Method for the Development of Visual Analytics Approaches. - Computer Science Colloquium, Friedrich-Schiller-Universität Jena, July 2016
  • Toward a Scientific Method for the Development of Visual Analytics Approaches. - Universität Leipzig, July 2016
  • Scalable Analysis of Big Remote Sensing Data. - VDI - Arbeitskreis Mess- und Automatisierungstechnik, Universität Kassel, June 2016. [Talk]
  • GeoMultiSens - Scalable Analysis of Big Remote Sensing Data. - Big Data All-Hands-Meeting, Dresden, June 2016.

Recent Publications:

  Visual Analytics for Big Data

  • Sips, M., Köthur, P. & Eggert, D.: Toward a Visual Analytics Approach to Support Multi-Sensor Analysis in Remote Sensing Science. Datenbank Spektrum (2016) 16(3): 219--225 doi:10.1007/s13222-016-0232-7 [Article]

Scalable Algorithms

  • Tobias Rawald and Mike Sips and Norbert Marwan:PyRQA - Conducting Recurrence Quantification Analysis on Very Long Time Series Efficiently. Computers & GeoScience. doi: 10.1016/j.cageo.2016.11.016 [Artikel]

Current Activities:

  • Lecture "Visual Analytics for spatio-temporal data", Humboldt-Universität zu Berlin, WS 2017/2018
  • Visual Analytics and Maschine Learning, Research Visit of the Lehrstuhl für Theoretische Informatik II, Friedrich-Schiller-Universität Jena, November 2016



  • Master (2001 Martin-Luther-Universität Halle) und PhD (2005 Universität Konstanz) in computer science. 
  • Stanford University (2006-2008) and Max-Planck-Institut Informatik (2008-2010)
Mike Sips
Dr. Mike Sips
Group Leader
Building A 20 , Room 303
14473 Potsdam
+49 331 288-1982