Not only in science and research is the importance of big data growing, but also in industry. Because more and more companies are interested in the possibilities of machine learning (artificial intelligence), big data architecture or data science in an industrial context. Data volumes are becoming ever larger, more complex and confusing. We are looking for new possibilities in this "data noise" to get the information it contains faster and more efficiently. Therefore, we research machine learning methods, technologies for data mining and innovative ways of visualization to find solutions and decision bases for challenging practical questions.
Visual data analytics refers to an interdisciplinary approach that deals with how data analysis can be enabled by computer-aided visual representations. The translation of data into images is an important basis for working with data in all areas and industries, since humans can understand and interpret visuals much more easily than pure numerical numbers. We develop new visualization techniques for high-dimensional time-based data or ensemble simulations, detach analytical processes from the conventional desktop computer in immersive analytics and also research methods that produce particularly trustworthy visualizations.
Experts know their processes, but have little access to advanced analytics. Data scientists, on the other hand, are proficient in algorithms, but need up to 80% of their time to get to know the data and prepare it for concrete projects. The goal of our research work is to close the gap between domain knowledge and analytics. We develop new approaches to transform raw data into a comprehensive and targeted tool for decision support. We employ various predictive modelling algorithms that visual analytics offers to make predictions based on real data.
Technologies enable big data production and collection and the other way around, thanks to big data technologies are advancing double-quick. We research and develop new and innovative methods of processing visual data for applications in technology. We design machine learning and deep leaning algorithms for data modelling, classification, simulation and anomaly detection to be integrated within our interactive visual tools.
Data Science plays an increasing role in the fields of medicine and life sciences. VRVis has already successfully implemented several data science projects in the areas of neuroscience and medicine for the analysis of health and patient cohort data. Furthermore, VRVis has developed Brain*, a globally used platform for the integration, management and exploration of spatial brain data.
In our applied research project TOHIVA, we developed novel visual analytics methods relevant for industry 4.0 for the more efficient visualization of large, high-dimensional data for customized forecast models to support decision-making processes.
For many years, we have been working intensively with fully automated, AI-based algorithms that solve real medical application problems, for example, and can already look back on several patents and a large number of publications in top-class scientific media, as well as several high-impact publications on machine learning and deep learning.
On March 3, 2020, Katja Bühler, head of our Biomedical Image Informatics Group, was awarded with the renowned TU Women's Prize.
Johanna Schmidt and Sophia Ulonska are nominated for the Hedy Lamarr Award by the city of Vienna.
VRVis gets awarded with the CGI Best Short Paper Award 2019!
The web-based tool "neuroMAP" of VRVis and IMP received the Best Paper Award of the IEEE BioVis 2013.
The aim of the application project IVC Multi is to research novel intelligent visual computing methods supporting decision-making in automotive industry, medicine, and life sciences based on ensembles of heterogeneous, multi-scale and/or multi-temporal data.
The strategic project of the area is the organizational and scientific hub for the realization of the area-wide intelligent visual computing approach for analytics and modelling based on ensembles of dense grid-based data, derived data, and digital embedding.
The primary goal of project INGRESS is to accelerate and improve the process of data scientists working with Industry 4.0 and Internet of Things (IoT) data, by enabling a closer integration of visual analysis into the existing workflows.
The RAILING project deals with the research and development of interactive, scalable and trust-building visualization and analysis tools for the exploration of time-dependent and complex data.
Within the project Larvalbrain 2.0, a dynamic multi-scale multi-level atlas and data collection of structural, molecular, physiological, and behavioral results of Drosophila melanogaster larvae will be established.
The combination of "liquid biopsies", machine learning and data visualization aims to enable earlier and more accurate prediction of a relapse in children with cancer.
Businesses go smart with Data Science: with the help of visual data analysis, machine learning, deep learning, data mining and visual data processing we help companies to fully exploit the potential of their data.
The strategic project forms the organizational and scientific hub for the realization of an area wide integrative visual computing approach. It covers joint strategic research and development on fundamental challenges in all application projects.
In this project we develop new methods from the field of visual analysis and machine learning to automate the quality control and quality assurance of glass articles.
For centuries, neuroscientists have been mapping the brain. Until now, the step from simple maps to a generally accepted model has proved to be extremely difficult. In this project, a 4D atlas of the brain of the fruit fly larva is being built.
DEXHELPP develops new methods to support analysis, planning and control in health care by combining decision analysis, data security, data management, statistics, mathematical modelling, simulation and visual analysis.
VRVis contributes data analytics and visualization tools tailored to support and accelerate research of the Haubensak Group at the Institute of Molecular Pathology Vienna.