Our group focuses on the foundation of algorithms and artificial intelligence, with a particular emphasis on computational problem-solving in the context of collective decisions. This means that we are interested in developing and analyzing algorithms that can help groups, organizations, and societies make informed decisions. Our research combines theoretical computer science with practical implementations and experimental evaluations, allowing you to bridge the gap between theory and practice. Your core research field is computational social choice.

Computational social choice is a field that combines computer science and social choice theory to study the design and analysis of methods for collective decision making. Researchers in this field use mathematical models and algorithms to explore topics such as voting systems, fair division, preference aggregation, and cooperative games. They aim to develop computational tools that can help us understand and improve the decision-making processes of groups, organizations, and societies. This interdisciplinary field has applications in areas such as political science, economics, psychology, and computer science.

In our group, the focus is on algorithm analysis and implementation, parameterized complexity, and formal properties in the context of computational social choice. This means that our research involves developing and analyzing algorithms that can efficiently solve problems in context of collective decisions. We are interested in understanding the computational complexity of these problems, and how to design algorithms that can handle large-scale data sets and complex social preferences. Parameterized complexity is a central topic in our research, as we explore how to efficiently solve problems that are hard in general but become easier when certain parameters are fixed. We also investigate the properties of social choice mechanisms, such as fairness, efficiency, and stability, and how they can be optimized in different contexts. Overall, our group's research aims to provide insights into the design and analysis of computational tools for collective decision making.