Research Projects

Principal Investigator

Prof. Dr. Ingo Scholtes

Contact: ingo.scholtes@uni-wuerzburg.de

Project duration: 2018-2024

Funding: CHF 1.5 Mio from the Swiss National Science Foundation (SNSF)

Short description

Graph analytics and (social) network analysis have become cornerstones of data science. They are widely applied to relational data studied in disciplines such as computer science, physics, systems biology, social science or economics. However, we are increasingly confronted with high-frequency, time-resolved data which not only tell us who is related to whom, but also when and in which sequence these relations occurred. The analysis of such data is still a challenge. A naive application of network analysis and modeling techniques discards information on the timing and ordering of relations, which is the foundation of so-called causal or time-respecting paths, i.e. it is needed to answer the question who can influence whom. In my research, I study the effects of temporal ordering in time-resolved relational data from real-world systems. Using a combination of information-theoretic and statistical methods, we could demonstrate that temporal correlations in data from social and biological systems break the transitivity of causal paths. We further showed that the application of network-based data analysis and modeling techniques as well as algebraic methods to time-stamped data yields wrong results.

Addressing the problem that common graphical representations of relational data discard information on the temporal ordering of relations, we developed a data analysis framework based on higher-order graphical models. Extending the common network perspective, it allows to combine information on both topological and temporal characteristics of time-resolved relational data into compact probabilistic graphical models. This approach provides new ways to (i) model dynamical processes like diffusion, cascades or epidemic spreading, (ii) detect temporal-topological clusters based on higher-order Laplacians and spectral methods, (iii) assess the importance of nodes, and (iv) study the controllability of complex systems. This research aims at methodological advances which not only provide us with novel data mining techniques, but whose impact reaches beyond computer science, with applications in the modeling of complex systems in physics, systems biology, social science and economics.

Principal Investigator

Prof. Dr. Ingo Scholtes

E-Mail: ingo.scholtes@uni-wuerzburg.de

Project duration: 2020-2024

Funding: CHF 400,000 from Honda Research Institute GmbH

Academic Partner: Social Computing Group, University of Zurich

Short description

With recent significant advances in intelligent systems, the question on the future relation between human and artificial intelligence has gained more interest. There are a number of reasons to promote cooperative systems as opposed to purely autonomous systems. Cooperative systems basically will not replace the human (not even in cases where this might be functionally possible) but will work together with the human in a team. At the moment, we have a very limited understanding of how such teams should be organized and what would be necessary for the human to feel comfortable in such a new team situation. How would human-robot or in general human-AI teams be different from purely human teams? Can artificial intelligence be integrated with human experience, creativity and intelligence such that the resulting collaboratiion between human and AI surpasses both human- and AI-level performance? Experiences from human-animal teams like in security or rescue operations with dogs or horses, as well as insights about the optimal composition of team members with heterogeneous skills can help us to chart possible routes to optimal cooperation patterns between humans and AI technologies.

Principal Investigator

Prof. Dr. Ingo Scholtes

E-Mail: ingo.scholtes@uni-wuerzburg.de

Industry Partner: genua GmbH

Academic Partner: Chair of Systems Design, ETH Zürich

Short description

Software systems are at the heart of the digital society: They control critical infrastructures like communication or energy systems, fuel the increasing automation in industrial manufacturing and are key drivers of the digital economy. Despite this importance, the development of complex software systems is still a fundamental challenge. Credible reports indicate that the majority of software projects run over time or budget -- or fail altogether, resulting in billions of dollars wasted every year. And while technical aspects like, e.g., programming techniques, testing methods, or developer support tools have improved significantly over the past years, our understanding how human and social factors contribute to success or failure of software projects is still in its infancy.

Addressing these challenges, I use data science to quantitatively study collaborative software engineering processes. As an example, we use network analysis and statistical modeling to study the evolution of software architectures based on large-scale data from software repositories. This not only allows us to trace the maintainability of software systems. We can also assist developers in the refactoring of code. We further extract large data sets from online support tools, and analyze them to better understand how social factors influence software development processes. This approach has helped us to uncover social mechanisms at work in software development, to quantify risks in Open Source communities, and to improve information systems used by software development teams.