AI, Big-Data and Robotics in Luxembourg

The Big Data group is a new research group at the University of Luxembourg that has been established in February 2017. The group investigates scalable architectures for the distributed indexing, querying and analysis of large volumes of data. A particular focus is put on information extraction, probabilistic databases and the development of distributed graph- and streaming engines. We thus investigate the whole lifecycle of semantic-data management, beginning with the extraction of entities and relations from textual and semi-structured sources and on to data-cleaning aspects and probabilistic inference. We intensively worked with the Hadoop and Spark platforms for research and teaching in the past, but are also highly interested in developing custom prototypes based on proprietary, asynchronous communication protocols.

For more information, contact Prof. Dr. Martin Theobald

The Individual and Collective Reasoning Group (ICR) is an interdisciplinary research team at the University of Luxembourg which is driven by the insight that intelligent systems (like humans) are characterized not only by their individual reasoning capacity, but also by their social interaction potential. Its overarching goal is to develop and investigate comprehensive formal models and computational realizations of individual and collective reasoning and rationality. ICR is involved in the Interdisciplinary Centre for Security, Reliability and Trust (SnT). The group currently counts more than 15 researchers and is strongly engaged in international cooperation. Our research areas are normative multi-agent systems, autonomous cognitive agents, computational social choice, and the foundations of logic-based knowledge representation and reasoning.

For more information, contact Prof. Dr. Leon van der Torre

Basic areas of competence of the team of Ulrich Sorger are probability, information, and coding theory. The main directions are decoding of error control codes and stochastic interference, where the decoding of error correcting codes can be considered as stochastic inference problem respectively the inversion of a stochastic map. Recent results show that encoding / decoding techniques exist that perform well close to theoretical limits. The team investigates these techniques and their applicability to other stochastic inference problems. Network Traffic Modeling concerns the development of stochastic network traffic models which can help to improve performance of data transfers and network security. The aim is to use these network traffic models to derive useful conclusions from the monitored traffic concerning local congestions, localization of spam sources or denial of service (flood) attacks. Particular attention is focused on elaboration of a new approach to the detection of local network congestions based on spectral analysis of multivariate stationary processes.

For more information, contact Prof. Dr. Ulrich Sorger

We are interested in data exploration and in elaborate on intelligent and adaptive algorithmic concepts to discovery information about the data. Current research is related to the fields of Artificial Companions and Chatbots, Emotion Detection in Texts with Deep Learning, and Topic Modeling. We participate the PRIDE Programs Digital History and Hermeneutics (DHH).

For more information, contact Prof. Dr. Christoph Schommer

We conduct research on parallel and evolutionary computing, in particular how different species may co-evolve featuring different individuals taking local decisions while ensuring global objectives (e.g., search and optimization). This target is approached through various facets like loosely coupled genetic algorithms, distributed immune systems, and iterated multi-player prisoner dilemma. The main application domains of the team fit the University of Luxembourg priorities: – security, trust and reliability, for example: cryptology, intrusion detection, and reliable scheduling and routing on new generations of networks such as p2p, ad-hoc, and hybrids. – sustainable development, for instance, Energy Efficient Data Centers – systems biomedecine, for example, genomic sequencing, proteine folding, genomic modeling.

For more information, contact Prof. Dr. Pascal Bouvry

Rhe team focuses on contributions to Algorithmic Decision Theory.

For more information, contact Prof. emeritus Dr. Raymond Bisdorff