Forschungseinheitenprofil

TU-Wien > Institut für Information Systems Engineering >Institut für Information Systems Engineering >Forschungsbereich Data Science

Übersicht
Fachgebiete

Leitbild (Deutsch) ..

Leitbild (Englisch) We perform foundational and applied research and drive development in Data Science. A particular focus is interdisciplinary work, bridging the gap between generic approaches and specific applications. Application areas in which we are applying our techniques include: Agriculture, Cultural Heritage, Education, Health, Industry 4.0, Legal text analysis, Logistics, Music retrieval, and Social Media analysis.

A further focus is on providing the means to allow us to trust the complex socio-technical systems we create and interact with. Our motto, Insights for Society, highlights our consideration of the wider consequences for society of our research, such as climate change related to computationally intensive tasks, filter bubbles caused by recommender systems, and the effects of decision support on everyday work.

Our research is summarised in the diagram. We describe each of the four main areas in more detail:

Analysis has the goal of discovering or extracting useful information from data for informing conclusions. The data can be in the form of natural language (Natural Language Processing), music or numerical data (Data Mining). Semantic Systems bring pre-existing knowledge in the form of ontologies and related structures into the analysis. The data can also be mathematically modelled, for example through machine learning. Simulations can be run to investigate the outcomes based on different parameterisations of the models.

Interaction deals with how people are supported in their interactions with large repositories. For Information Retrieval, the user provides a query used to retrieve relevant objects from the repository. Recommender Systems retrieve relevant objects based on previous user interactions with the system. With Conversational Systems, such as chatbots, getting to the required information should be through a natural language conversation with a system. Many tasks in both the Analysis and Interaction areas need to learn from large amounts of manually annotated data - here we work on approaches to make the generally time-consuming process of manual annotation more efficient.

Infrastructure research deals with supporting various aspects of doing Data Science. Secure Data Infrastructures allow calculations to be done on data without the data itself being shared. Research Data Management ensures that data remains findable, accessible, interoperable, and reusable indefinitely. Virtual Research Environments facilitate online collaboration of researchers. High Performance Computing investigates how to satisfy increasing demands for computational power through energy-efficient and sustainable edge and cloud systems.

We ensure that our research has Impact on society. Our approaches to support Reproducible Research contribute to overcoming the reproducibility crisis in multiple areas of science. Our analyses form the basis for Evidence-Based decisions in industry and government. Our work on transparency of AI models and their outputs contributes to developing Trustworthy AI. The Digital Humanism initiative ensures that technology development remains centred on human interests.

Research domains within the Research Unit include:

Decision Support for Health Policy and Planning

Digital Humanism

High Performance Computing

Natural Language Processing

Recommender Systems

Semantic Systems Research

Webseite https://informatics.tuwien.ac.at/orgs/e194-04

Personen

Titel	Nachname	Vorname
Projektass.in M.Comp.Sc.	Aayesha	Aayesha
Projektass. MSc	Ahmad	Sabtain
Univ.Ass.in MA	Arzt	Varvara
B.A.	Baban	Alina-Ioana
Projektass.(FWF) Dipl.-Ing. Dr.techn.	Bicher	Martin
Univ.Prof.in Mag.a rer.soc.oec. Dr.in rer.soc.oec.	Brandic	Ivona
Projektass. Dipl.-Ing.	Brunmeir	Dominik
Hon.Prof. Dr.phil. Dr.h.c.mult.	Buchberger	Bruno
MA	Czuray	Marie
Projektass. PhD	De Maio	Vincenzo
Ao.Univ.-Prof.i.R. Dipl.-Inf. Dr.-Ing.	Dorn	Jürgen
BSc	Fahringer	Jakob
BSc	Farmohammadi	Pouya
Senior Scientist Dipl.-Ing. Mag.rer.soc.oec. Dr.techn.	Fenz	Stefan
Projektass.in B.A. M.A.	Flicker	Katharina
Projektass. BSc MSc	Giannandrea	Daniele
Projektass. Mag.rer.soc.oec. BSc	Goldenits	Georg
Univ.Doz. Hon.Prof. Dipl.-Ing. Dr.techn.	Heiss	Michael
Projektass.in(FWF) Dipl.-Ing.in BSc	Herbst	Sabrina
Projektass. Mag.rer.soc.oec. Dr.rer.soc.oec.	Heurix	Johannes
BSc	Hofstätter	Daniel
BSc	Hubmaier	Lukas
BSc	Höller	Maximilian
Univ.Ass. M.Tech. PhD	Ilager	Shashikant Shankar
BSc	Irsyam	Ayu Amalia

Fachgebiete nach Statistik Austria

Code	Fachgebiet(Deutsch)	Fachgebiet(Englisch)
1109	Informations- und Datenverarbeitung	Information and data processing
1122	Artificial Intelligence	Artificial intelligence
1133	Computerunterstützte Simulation	Computer-aided simulation
1148	Computerlinguistik	Computational linguistics