Complex Dynamical networks: From Data to Connectivity Structure
Dynamical networks are pervasive in today’s world, ranging from social and economic networks to biological systems and man-made infrastructures. The connection structure plays a crucial role in determining the overall behavior of these networks. For instance, the topology of social networks affects the spread of information and disease, and the topology of the power grid affects the robustness and stability of power transmission. Knowing the connection structure is fundamental in order to predict how these networks might evolve and to anticipate/counteract critical transitions. In this research project, we aim at developing methods and algorithms to infer the connectivity structure of complex dynamical networks from sparsely collected data.
Researchers : Dr. Michael Wilkinson (JBI); Prof. Dr. Alexander Lazovik (JBI); Dr. Johan Messchendorp (Centre for Advanced Radiation Technology - KVI); Prof. Dr. Leon Koopmans (Kapteyn Astronomical Institute - Kapteyn); Prof. Dr. Scott Trager (Kapteyn).
PhD student: Simon Gazagnes, MSc
In many fields of science, data sets are not just big, the data rate is also huge, and the sizes of individual data items or cliques of data that need to processed as a whole become very large indeed. Coping with big data sets is quite a challenge on its own, but the problem is compounded if the individual data items are too large to process on a single node, or if the sensor data rate is so high you cannot possibly store all of it. Our proposal aims to develop fast, multi-scale algorithms for graph-based data processing suitable for efficient distributed-memory parallel processing.
Clinical Big Data for multifactorial diseases: from molecular profiles to precision medicine
Researchers : Prof. Dr. Peter Horvatovich (Groningen Research Institute of Pharmacy - GRIP); Dr. Marco Grzegorczyk (JBI), Victor Guryev (UMCG, ERIBA), Dr. Corry-Anke Brandsma (UMCG), Prof. dr. Kathrin Thedieck (UMCG, Medical Faculty of Oldenburg University), Prof. dr. Rainer Bischoff (GRIP), Prof. dr. Ernst Wit (JBI), Dr. Bart Verheij (ALICE), Dr. György B. Halmos (UMCG), Prof. dr. Wim Timens (UMCG), Prof. dr. Dirkje Postma (UMCG), Maarten van de Berge (UMCG), Prof. dr. Gerald Koppelman (UMCG), Prof. dr. Eelko Hak (GRIP).
PhD student: Victor Arturo Bernal
Multifactorial diseases with complex traits such as various types of cancer and chronic obstructive pulmonary disease (COPD) are among the leading causes of death in the Western society and form primary challenges of current health system. Poor understanding of the complex molecular mechanisms of such diseases and currently available diagnostic options are often inadequate to find efficient treatment for a large proportion of patients. Personalized treatment of patients or identification of subgroups of patients where treatment is efficient using precision medicine approaches are pivotal to improve health and patient care.
The goal of the project is to develop a machine learning method for large multi-omics and clinical data sets to address different clinical questions in projects studying complex diseases such as cancer and COPD.
Uncovering the information processing underlying the interactions between brain areas
Researchers : Dr. Marieke van Vugt (Artificial Intelligence and Cognitive Engineering - ALICE); Prof. dr. ir. Ming Cao (ENTEG); Prof. dr. Niels Taatgen (ALICE); Dr. Jelmer Borst (ALICE); Mircea Lungu, PhD (JBI).
PhD student: Oscar Portoles Marin, MSc
While considerable progress has been made on understanding how the brain works, most of this is focused on the functions of individual brain areas in isolation. The next frontier is to understand how these brain areas work together in the service of cognition. The main aim of the project is therefore to understand how brain areas communicate in the service of information processing.
The Value of Data
Researchers: Dr. Bart Verheij (ALICE), Prof. dr. Ernst Wit (JBI), Prof. dr. Rineke Verbrugge (ALICE)
Data is a driver of value creation. For instance, the value of many tech companies is based on data-guided marketing algorithms. Businesses optimize the gains of their processes using data, e.g. in fraud detection. For a good understanding of the value of data, three theoretical domains are relevant: probability theory, expected utility theory and logic. Probability theory provides the foundations for descriptive statistics, expected utility theory models subjective values, and logic represents complex qualitative relations. In the project, argumentation-based formal methods and algorithms will be developed connecting probability theory, expected utility theory and logic. The project will extend techniques developed for evidential reasoning about the facts to practical reasoning about actions.
|Laatst gewijzigd:||13 december 2017 11:10|