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The advent of the digital information age has led to an inevitably increasing growth of heterogeneous data. Caused by the rapid spread of internet-enabled modern devices and their powerful processing capabilities as well as advanced means of data communication, users and systems are able to generate, process, and share data in a massive scale. Concomitant with the resulting multitude and versatility of data made available in scientific and non-scientific domains, today’s data analysis methods are supposed to adapt to complex data types in order to provide insights with respect to diverse information needs.

Our research is rooted in the domains of data science and data engineering, where we aim to develop intelligent data analysis methods that scale to challenging research questions in various interdisciplinary application areas across industry, engineering, chemistry, medicine, and humanities. Our goal is to explore, explain, and exploit complex data spaces comprising time series, streaming, spatiotemporal, social, scientific, graph, and multimedia data in order to discover novel insights and domain knowledge. Our reserach interests cover the following areas and topics:

Research Focuses:

  • Data Science
  • Data Engineering
  • Big Data
  • Machine Learning
  • Multimedia Databases