The Institute of Interactive Systems and Data Science (ISDS) is a culmination of two
major streams: Interactive Systems (IS) and Data Science (DS)

Our Mission statement

"We unite research on all aspects of Artificial Intelligence (AI) in order to 

(1) make systems interactive and adaptive to humans based on (big) data, and 

(2) to support humans to interactively create insights from (big) data"

Stefanie Lindstaedt - Director ISDS

Research Groups

International Business Group (IBG) - Team Kappe & Foscht. Global collaborations require models for international business. This group’s general focus lies on international business activities, mostly in academic education and research.

Working, Learning and Technology - Team Pammer-Schindler. This group studies how technology is used in different domains to extract domain-relevant knowledge for design, in the context of learning and knowledge work.

Data Management for Data Science Lab (DAMS Lab) - Team Boehm. Data science sometimes might fall into information at a high level of details. However, high-level insights are essential to inform building systems and tools to efficiently and scalably execute tasks. This group has the primary focus on ML systems and large-scale data.

Game Lab Graz (GameLab) - Team Pirker. Games are one of the greatest examples of interactive experiences. This group studies games from all angles to achieve better virtual experiences that also make people better, tackling the design, analytics, network analysis, and AI in games.

Computational Social Science Lab (CSS Lab) - Team Garcia. Digital societies are complex phenomena, which call for an interdisciplinary approach to achieve a complete understanding on how they are built and evolve. This group combines a team of computer scientists, psychologists, physicists, and sociologists to deduce computational models from large-scale digital data.

Visual Analytics - Team Sabol. Big data is not only a challenge for the analysis but also for the visualization. This group tackles this issue by combining visual and automatic data analysis methods. The goal is to integrate visualization and machine learning methods, addressing big data visualization and explainable AI.

Knowledge Discovery - Team Kern. Data, especially when finer-grained, can include noise and redundant information. Therefore, methods and practices to extract relevant
information are needed. This group made its mission to recover the value out of the data, via machine learning and natural language processing.

Open and Reproducible Research Group (ORRG) - Team Ross-Hellauer. Data science is better when made open, yet this comes with several challenges and factors to consider. This group aims at making the research cultures more open, transparent and participatory through new practices and technologies.

Cognitive & Digital Science Lab (CoDiS) - Team Guetl. People interact with systems having a precise motivation(s) pushing them. Combining computer science and cognitive psychology contributes to know-how and research on human factors, cognition, and motivation from cognitive psychology.

Learning Analytics & Knowledge Services - Team Lindstaedt. Interactive systems are often used as a precious resource in learning and conveying knowledge. This group investigates how data science can be exploited to gather and analyze learners’ data to optimize the learner behaviors, as well as supports discipline-specific use cases in the life sciences.

Immersive Environments - Team Veas. With the advent of new technologies, the concept of immersion has been a leading trend for multiple domains, ranging from showcasing products, installations, and training. This group researches immersive technologies to extend human capabilities via computer-mediated interactions, exploiting artificial intelligence, data science and human-machine interaction.

Social Computing - Team Lex. Online activity is also, and especially, a social experience. However, accounting for sociality introduces a layer of complexity in which the individual (and their needs) must be understood in a social context. This group studies and combines personalized recommender systems, online social networks, and information retrieval.

Information Visualisation - Team Andrews. Data also needs to be interpreted and contextualized. Interpretation, however, can be challenging, as human bias is always a risk. Information visualization is the field that tackles this issue of creating interactive visual representations of abstract information spaces. This group aims to facilitate their rapid assimilation, exploration, and understanding by human users.

Web Science & Engineering - Team Helic. The web revolutionized our lives, and still does so. Online people build a parallel reality in which they explore their own passions or connect to others. This group studies the trace that people leave on the web via network science, data mining, recommender systems, user behavior and social computing.

Science, Technology & Society (STS) - Team Getzinger. Technology is more and more integrated with our society. Inevitably, this interconnectedness has implications on environmental and societal changes. This group conducts inter- and transdisciplinary research to uncover and improve how technology is connected with society.