Prominent areas of investigation include:

Developing methods to analyze and understand large-scale text data from domains such as marketing, tourism, social sciences, and engineering. Research also explores conversational AI, including chatbots and agents for information access, recruitment, human resources, and educational support.

Advancing machine learning approaches to interpret visual information and combine it with structured knowledge representations. Applications include classifying tourist photography, extracting destination branding, and exploring neuro-symbolic AI for human-like visual perception.

Huge amounts of data available on the Web and in social media allow us to analyze important social and economic phenomena such as diffusion of information, emergence of communities, or self-organization in collaborative efforts. To analyze such large datasets methods from graph theory and network science are of primary importance as the data often comes in form of relations between different entities. For example, data in social media is typically represented with social networks and the data on the Web with infromation networks. In our research we quantitatively analyze large emprirical datasets but also develop new methods for network analysis.

Predicting the future is valuable for individuals and businesses, enabling strategic changes to maximize benefits or minimize potential damage. With advancements in big data, AI technologies, and predictive analytics, our School of Data Science researches how various data inputs can improve accuracy in open domain settings. While perfect knowledge of the future is elusive, our findings demonstrate how knowledge extracted from big data can guide organizations in making informed decisions. Join us to explore the power of predictive analytics in shaping a more probable future.

Modeling complex decision-making processes in dynamic environments. Applications range from stock market prediction to sustainable systems and smart city management.