Welcome to Data Science @ Uni Vienna!
Data Science @ Uni Vienna is a research network at the University of Vienna that presents a hub on all activities in data science at the University of Vienna. Our primary focus is to bring researchers from different areas together to work on and solve several of the challenges that this new field presents. To this end we will primarily be driven by application problems whose solution requires novel methodological developments. The main research challenges to be tackled by this research network can be summarized as (a) application challenges, (b) methodological challenges, and (c) translational challenges. We specifically focus on problems arising in one of the following five domains, Astronomy, Digital Humanities, Finance, Industry 4.0, Medical Sciences. While these areas are broad, they have in common that they are data-driven and use similar methods from computer science, mathematics, and statistics. The focus in these areas is described as follows:
Astronomy
Astronomy is currently undergoing a data deluge with multiwavelength missions on earth and space. The focus of the PhD project in this area is the development of algorithmic and visual analysis techniques for the Gaia mission data, an ambitious ESA satellite currently charting a three-dimensional map of our Galaxy with accurate positions and velocities of about 2 billion stars. The student will focus on large data exploration and data analysis to tackle astrophysical questions, making use of Data Science tools.
Digital Humanities
The Digital Humanities area will have a particular focus on digital historical studies. The student will focus on the development of suitable data models for information about historical people and cultures that is harvested from the digitisation of texts and artifacts. Another goal will be to look at how these models, and machine learning techniques that make use of them, will coexist with the interpretative critical frameworks through which historical analysis is usually done.
Finance
Potential topics in the area of Finance are visual analysis tools for the analysis of volatility, liquidity and market microstructure relations based on large cross-sections of limit order book data. A second area will focus on the development and application of dimension reduction techniques for high-dimensional dependence and network structures. Among others, further topics will be the development of monitoring tools to analyze market dynamics around singular events.
Industry 4.0
In Industry 4.0, the production process in a shop floor consisting of cyber-physical production systems produces huge amount of data. In addition a current trend in modern societies is the increased need in personalized products. In such dynamic environments exceptions and disruptions are frequent and often lead to unforeseen situations and possibly negative consequences. Hence, we focuses on detecting dynamic process changes or unexpected disruptions early by exploiting the available data. Moreover, strategies to avoid negative impacts whenever such disruptions occur have to be developed. Such strategies may apply predictive methods for planning in advance or adopt real-time planning approaches with the aim to revise the original plans quickly.
Medical Sciences
In the area of Medical Sciences the goal is to develop new data analysis methods supporting an integrative view on information originating from different sources including medical imaging, genetic data, clinical biomarkers and demographic data. We will particularly focus on clustering methods supporting the stratification of patient collectives with the long term goal of personalized medicine. As applications we will consider Alzheimer’s disease and breast cancer.
