System Data and Society

Modern systems are characterized by both unprecedented complexity and big data. The combination of complex systems and data science is uniquely positioned to leverage one in order to handle the other: use big data to manage system complexity as well as use complex systems to harness big data. This is key in addressing societal problems, such as information privacy, climate change and sustainable growth, etc. Systems, Data and Society is a very rich theme with multiple directions and connections between mathematics, computer science and artificial intelligence.

This theme has many ethical and societal aspects which are of great concern in society today: privacy and security of data; transparency of algorithms; responsible/explainable AI & data science; open science; striving for a balance between automated systems and humans. It is also strongly connected to the centre for Data Science and Systems Complexity (DSSC), but also to the centre for Cognitive Systems and Materials (CogniGron).

Several groups at the BI conduct research within the area Systems, Data and Society.

In Math: systems & control theory, dynamical systems, optimisation, computational mathematics and simulation, statistical data analysis, interacting particle systems, topological data analysis. In CS/AI: machine learning, software intensive systems, distributed systems, formal systems (algorithms, formal methods), computer systems (architecture, networking, security), information systems, intelligent systems (pattern recognition, computer vision), multi-agent systems, interactive systems (computer graphics, human-computer interfaces).