Visplore is a software technology on the basis of which customized analysis solutions for large, structured data volumes can be implemented quickly. The interactive tool offers flexibly combinable visualizations that allow users without extensive statistical knowledge a completely new, low-threshold access to working with data. Click here for the information flyer on Visplore.
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.