This is the Newsletter of the Centre for Data Science and Systems Complexity (DSSC) at the University of Groningen. The DSSC Newsletter is issued evey two months.

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Centre for Data Science and Systems Complexity (DSSC), University of Groningen

DSSC Newsletter


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DSSC scientific meetings
DSSC research profiles: Prof. Dr. Bayu Jayawardhana (ENTEG) & Prof. Dr. Kanat Camlibel (JBI)

PhD students at the DSSC: Drs. Oscar Portoles Marin & Drs. Victor Arturo Bernal
EuroVis at the University of Groningen

First results of the project BCause: discovering and testing complex causal knowledge grounded in big pharmaceutical data
Horizon 2020

DSSC seminar, Prof. Dr. Frank Allgower (1 Nov., 16.00-17.00, room 5161.0105)
International Conference on Computing in High Energy and Nuclear Physics (10-14 Oct., San Francisco)
The 'Computational Sciences for Future Energy' Conference (11 Oct., Utrecht)
Workshop on Control of Transmission and Distribution Smart Grids (11-13 Oct., Prague)
4th National eScience Symposium (13 Oct., Amsterdam)
International CAE Conference for Simulation based Engineering and Sciences (17-18 Oct., Parma)
i-KNOW Conference (18-19 Oct., Graz)
IAU Symposium Astroinformatics (20-24 Oct., Sorrento)
12 eScience IEEE International Conference (23-27 Oct., Baltimore)
ECW 2016 - Environmental Computing Workshop (23-27 Oct., Baltimore)
Optimization challenges in the evolution of energy networks to smart grids (27-28 Oct., University of Coimbra)
SmartSec Europe - Advanced Cyber-Security Strategies to Achieve Smart Grid Resilience (29-30 Nov. Amsterdam)

DSSC scientific meetings
Beginning this fall the DSSC will organize regular scientific meetings around major topics in data science and systems complexity. The scientific meetings, to which all the DSSC members are invited, will feature 2-3 short lectures from our members, to be followed by Q&A and updates from the Centre.  

The first DSSC scientific meeting will take place on October 3, 10.00-12.00, room 5161.0165 and will include talks by Prof. Dr. Bayu Jayawardhana, Dr. Konstantinos Efstathiou and Dr. Alef Sterk. The topic of the meeting will be Systems&Complexity. More details will be advertised on our website.

DSSC research profiles
The DSSC reunites a group of innovative researchers whose interests will be featured in every issue of the DSSC Newsletter. In this issue, 2 researchers from ENTEG and JBI introduce their projects:

Prof. Dr. Bayu Jayawardhana - Research group Discrete Technology and Production Automation Bayu Jayawardhana photo

Our group focuses on the development of advanced control systems for mechatronic systems (which is, roughly speaking, an integrated system that consists of mechanical, electrical, electronics and information processing systems) and on the analysis of and control design for nonlinear systems.

In this regard, systems and control theory for nonlinear systems has played an important role in our research activities and we always strive to maintain a balance between the development of nonlinear systems & control theories and their applications

On the application-side, in all of the following research projects, we deal constantly with systems complexity (in terms of systems nonlinearity and of high-dimensionality) as well as with data science (such as, 3D signal processing, high-dimensional time-series data and network dynamics).

We have recently completed a demonstrator project of a 2D chopping mechanism for the METIS-EELT instrument, a systems biology project on model reduction method for handling complexity in genome-scale kinetic model and an EU SmartBot project on distributed formation control for mobile robots (such as, unmanned aerial vehicles (UAV) or automated guided vehicles (AGV)).

We have also been working on the control of seaport terminal operations, on the development of mechatronic systems for the Ocean Grazer, on the control design for smart factories, on the mechatronic systems design of smart optics systems (together with SRON and Kapteyn Institute) for future space missions on exoplanets observation and on the control of flexible manufacturing systems.

Although our group has collaborated with a number of researchers within DSSC, we foresee potential future interaction with many other groups within the Centre in the development of aforementioned applications. As an example, we are currently developing data analytic tools for predicting ocean wave surface elevation based on commercial radar systems produced by one of our partners in our Ocean Grazer project.

In order to develop such tools, we are looking for expertise in wave modelling, digital image analysis and model reduction techniques. Another example is in the development of smart optics systems where we need expertise in real-time image processing, in adaptive filtering, in visualisation and in real-time optimisation. Therefore, we are looking forward to establishing new collaborations within DSSC.

Prof. Dr. Kanat Camlibel - Systems and controlKanat Camlibel photo

Systems and control theory is an interdisciplinary branch of mathematics and engineering that studies how to shape the behaviour of dynamical systems with external inputs by means of feedback. Within the broad are of systems and control, my research activities are predominantly centred around two main themes: differential variational inequalities and dynamical networks.

One line of research that I have been following since I have obtained my PhD deals with developing a geometric feedback control theory for nonsmooth dynamical systems given by (piecewise affine) differential variational inequalities. On the practical side, I am particularly interested in applications of such systems in the modelling of physical systems mainly in the context of energy systems. On the theoretical side, I am dealing with the fundamental issues such as well-posedness, controllability, observability, observer-based stabilisation, disturbance decoupling, optimal control, and model reduction of differential variational inequalities.

In the area of dynamical networks, my research is focused on robust synchronisation, controllability, fault detection, and model reduction of linear multi-agent systems. My work on dynamical networks was initiated by the investigation of synchronisation, controllability and fault detection problems for systems defined on graphs with a geometric approach. Although my efforts led to solutions for these problems, the methods developed required exponentially increasing computer power with the increase of network complexity. As a result, I have been recently investigating model reduction methodologies based on clustering the agents into groups and/or removal of connections in such a way that a pre-specified feature, such as synchronisation, is preserved. Such model reduction methods lead to simpler models of large-scale complex dynamical networks that can still capture the underlying properties of the connectivity structure.

Within the DSSC framework, I envision establishing new collaborations and enhancing the existing ones in the context of complex systems with an eye towards big data.  

PhD students at the DSSC
This spring 2 PhD students were selected and have already begun research on the projects "Uncovering the information processing underlying the interactions between brain areas" and "Clinical Big Data for Multifactorial Diseases: from Molecular Profiles to Precision Medicine," which were among the 4 winners of the DSSC call for applications organized in December 2015. This month's issue offers more details about their research.

Drs. Oscar Portoles MarinOscar Marin photo

Oscar Portoles Marin is a PhD candidate within the DSSC. He will be supervised by Ming Cao (ENTEG), Marieke van Vugt (ALICE), and Mircea Lungu (JBI) in the multidisciplinary project "Uncovering the information processing underlying the interactions between brain areas."

The project aims at developing new methods to model and visualize the electrophysiological dynamics of the human brain while performing cognitive tasks. In addition, these models will be inverted to make predictions about brain structures and interactions involved on new cognitive task. Eventually, a software application with all developed tools (a “brain parser”) will be made available either as part of an open analysis package or as a stand-alone application.

ALICE contributes to the project with their knowledge on human brain processes as well as computational cognitive models. ENTEG will bring the expertise on complex dynamical systems. And JBI will provide the skills on software development as well as time series analysis. Drs. Marin's role is to be an executive node that passes information through and seeks the right skills present on each department. With this work the team endeavors to shed light on how networks of brain areas work together in the service of cognition.

Drs. Marin's expertise is on electroencephalographic (EEG) signals processing. For the last three years he was part of an Initial Training Network funded by the Marie Curie Actions FP7. He was hosted on Brain Products GmbH (Germany), a leader on neurophysiological solutions for research. He worked on the development of brain computer interfaces and cooperated with developmental psychologists to improve the methods to acquire and analyse EEG recordings on infants. His first serious encounter with EEG signals was during my master thesis. He investigated methods for predicting epileptic seizures with EEG. Drs. Marin's background is on electrical and biomedical engineering. 

Drs. Marin is interested in biomarkers and neurotechnology that can help to have a healthier society. He is also interested in developing methods to analyse neuro-electrophysiological signals. 

Drs. Victor Arturo BernalArturo Bernal photo

At the DSSC Drs. Victor Arturo Bernal will be involved in the project "Clinical Big Data for Multifactorial Diseases: from Molecular Profiles to Precision Medicine." Diseases with multifactorial origin and complex traits (such as various types of cancer and COPD) are among the leading causes of death in the Western Society.

Current treatment approaches of such complex diseases are often inadequate to find efficient treatment for a large proportion of patients, demanding development of precision medicine approaches and personalized treatment of patients, which are pivotal to improve health and patient care.

Clinical samples obtained at different stage of disease and from different tissue location are accompanied by many clinical parameters (lung function, age, BMI, concentrations of various biomarkers etc.) which are generally collected during patient care. This information combined with molecular profiles of the patients obtained using modern state-of-the-art molecular profiling technology such as genomics, transcriptomics, epigenetics, proteomics and metabolomics open the possibility for personalized or precision patient phenotyping and treatment.

The aim of this project is to develop a machine learning approach based on Bayesian modeling, which is simple to use even for non-expert users and that enables easy adaptation to different study designs, and which approach support personalized and precision medicine. The aimed machine learning approach shall allow to link highly heterogeneous molecular profiles to diverse clinical parameters in order to identify patient subphenotypes, new potential drug targets for patient subgroups, and identify biomarker(s) that predict efficacy of treatment efficiently. The project is highly interdisciplinary and is performed in close collaboration of scientists working in advanced statistics, proteomics, genomics and clinics.

Victor Bernal was born in Caracas Venezuela. He got a bachelor degree in physics at the Universidad Simón Bolívar specializing in general relativity. After completion of his studies in Venezuela, he came to Europe with an Erasmus Mundus scholarship to earn a master degree in mathematical modeling applied to financial problemsHe performed further master studies at the Università degli Studi dell'Aquila (Italy), Universität Hamburg (Germany), and Université Nice Sophia Antipolis (France) and performed a master research project in the Biocore team of the French research institute of INRIA (Institut National de Recherche en Informatique et en Automatique). His master project focused on developing mathematical models for personalized pharmacokinetics. His primary interests include mathematical modeling applied in life sciences, finance and physics.

EuroVis in Groningen
The Eurovis photo18th EG/VGTC Conference on Visualization (EuroVis 2016) was held in Groningen from June 6-10. It attracted close to 300 participants from 24 countries, 22% of which outside Europe from countries like USA, Canada, China, Japan, Korea, Singapore, Brazil and Costa Rica. The main conference featured 2 keynote lectures, 56 full papers, 25 short papers, 10 state-of-the-art reports, and 27 posters.

There were five satellite events, focusing on visual analytics, parallel graphics, environmental visualization, reproducibility in visualization, and machine learning in visualization. More information, including videos of the keynote lectures, is available from the conference website.

First results of the project BCause: discovering and testing complex causal knowledge grounded in big pharmaceutical data.

The availability of big pharmaceutical data - such as the University Groningen pharmacy prescription database - provides new opportunities for developing causal knowledge that is relevant epidemiologically for the population and clinically for patients and doctors. By following the consequences of healthcare policy choices, benefits and risks associated with drug use at the patient and population levels can be assessed and be used to guide future decision making. The project BCause: discovering and testing complex causal knowledge grounded in big pharmaceutical data aims to develop computational tools for the discovery and testing of complex causal knowledge grounded in big pharmaceutical data. The project is developed by a team of DSSC researchers that includes: Prof. Eelko Hak, (GRIP); Prof. Michael Biehl, (JBI); Dr. Bart Verheij, (ALICE); Prof. Jos Roerdink, (JBI); Prof. Ernst Wit, (JBI); Prof. Peter Horvatovich, (GRIP); Dr. Marco Grzegorczyk, (JBI); CSC PhD student (Yuanyuan Wang, MSc Beijing University).

Among the first results produced under the umbrella of BCause is a Systematic Review and Simulation Study for Prescription Sequence Symmetry Analysis. The research was conducted by Demy Idema under the supervision of Prof. Horvatovich, Prof. Biehl and Prof. Hak

Prescription sequence symmetry analysis (PSSA), a case-only design introduced in 1996, has been increasingly used to identify unintentional drug effects. However, the validity of PSSA compared to traditional observational parallel group study designs remains unclear. Hence, the first aim of this study is to systematically review articles that compare PSSA to a parallel group design. The second aim is to determine the effect of the following parameters of PSSA on its effect estimate, the adjusted sequence ratio: population size, length of the risk period, sensitivity and specificity of the proxy for the outcome, prescription trends of the index drug, and probability distribution of the side-effect.

Methods For the systematic review, the MEDLINE®, EMBASE® and Web of Science® databases were reviewed (until February 2016) to identify studies that compared PSSA to a parallel group design. Data from the eligible articles was extracted and analyzed in several ways, including Spearman Rank-Order Correlation tests and calculation of the number of discrepancies between the two designs. For the simulation study, Monte Carlo (MC) simulations were performed in MATLAB using the parameters of the PSSA. The effect of each parameter was determined by varying one parameter while keeping the others constant. Last, the parameter set was optimized to reproduce a real-life situation in which PSSA was performed as best as possible.

Results There was a significant correlation between the effect estimates of the PSSA and the parallel group designs, but the percentage of discrepancies (70-80%) showed that this correlation was not accompanied by considerable similarity of the effect estimates. Overall, the effect estimates of the parallel group designs were higher than those of the PSSA, and the parallel group designs also generated more significant signals. In the simulation study, varying the probability distribution for the side-effect had a greater effect with shorter, optimized risk periods. Lowering the population size widened the confidence intervals and lowered the power in most situations. With the most extensive simulation it was possible to find an appropriate population size, probability distribution, and prescription trends that mimicked the real-life situation.

Conclusions The review shows the potential of PSSA as a signal detection method, because it eliminates bias generated by control-group selection and time-invariant confounders. However, PSSA is still sensitive to confounding by contra-indication, and it usually has a lower power than the traditional study designs. If conducted with databases that are large enough, PSSA allows fast hypothesis generation on drug safety that can be evaluated further using traditional study designs. The results of the simulation show that it is important to chose an appropriate risk period when performing PSSA and to take into account the sensitivity and specificity of the proxy for the outcome. They also show that the simulation can be used to find probability distributions for real-life scenarios.


For more information, please click on the calls:

ICT-07-2017: 5G PPP Research and Validation of critical technologies and systems
Deadline: 8 November 2016

EE-07-2016-2017: Behavioural change toward energy efficiency through ICT
Deadline: 19 January 2017

EE-20-2017: Bringing to market more energy efficient and integrated data centres
Deadline: 19 January 2017

MG-5.2-2017: Innovative ICT solutions for future logistics operations
First stage deadline: 26 January 2017
Second stage deadline: 19 October 2017

ART-01-2017: ICT infrastructure to enable the transition towards road transport automation
First stage deadline: 26 January 2017
Second stage deadline: 27 September 2017

SC1-PM-15-2017: Personalised coaching for well-being and care of people as they age
Deadline: 31 January 2017

MG-8.2-2017: Big data in Transport: Research opportunities, challenges and limitations (Coordination action)
Deadline: 1 February 2017

LCE-01-2016-2017: Next generation innovative technologies enabling smart grids, storage and energy system integration with increasing share of renewables
Deadline: 14 February 2017

LCE-04-2017: Demonstration of smart transmission grid, storage and system integration technologies with increasing share of renewables
Deadline: 14 February 2017

LCE-05-2017: Tools and technologies for coordination and integration of the European energy system
Deadline: 14 February 2017

EO-2-2017: Earth observation Big Data Shift
Deadline: 1 March 2017


DSSC seminar, Prof. Dr. Frank Allgower

1 November, 16.00-17.00

The DSSC seminar features prominent local and guest speakers in data and complexity science.

Guest speaker: Prof. Dr. Ing. Frank Allgower
Title: TBA

We will follow soon with more information. For recordings of our previous seminars, please visit our website

International Conference on Computing in High Energy and Nuclear Physics

10-14 October, San Francisco

The CHEP conferences address challenges in computing, networking and software for the world’s leading data-intensive science experiments that currently analyze hundreds of petabytes of data using worldwide computing resources.

Hosted by: SLAC National Accelerator Laboratory; Lawrence Berkeley National Laboratory

For more information, please visit the website.

Workshop on Control of Transmission and Distribution Smart Grids

11-13 October, Prague

The IFAC Workshop CTDSG´16 is the forum for the exploration of the frontiers in control of transmission and distribution systems. This Workshop is attended by a worldwide audience of scientists and engineers from academia and industry, with the widest coverage of application Smart Grids fields. Among the topics covered: power systems, smart grids, power electronics, energy market and trading, WAMs and WAMPaC Systems, PMU, simulators.

For more information, please visit the website.

International CAE Conference for Simulation based Engineering and Sciences

17 - 18 October, Parma

The event will feature contributions that describe applications of CAE technologies in areas such as, mechanics, industrial applications, structural engineering, optimization, manufacturing process simulation, computational fluid-dynamics, emerging technologies, durability and fatigue, rapid and impact dynamics, CAD/CAE integration, etc.

For details and deadlines for abstract and poster submission, please visit the website

IAU Symposium Astroinformatics

20 - 24 October, Sorrento

IAU symposium on Astroinformatics will bring together world-class experts to address the methodological and technological challenges posed by the scientific exploitation of massive data sets produced by the new generation of telescopes and observatories.

Topics: Database Management Systems, Data Mining, multiprocessor computing for astronomy, machine learning methods for classification and knowledge extraction, algorithms for N-point computations, time series analysis and image processing, advanced visualization for astronomical Big Data, cross-disciplinary perspectives and advanced training.

For more information, please visit the website.

ECW 2016 – Environmental Computing Workshop 

23 - 27 October 2016, Baltimore
The workshop will assemble practitioners, policymakers, and environmental modelling experts to present the latest developments in Environmental Computing.

Topics: Environmental modelling and optimisation techniques; Novel environmental computing applications; Multi-scale, multi-model and multi-physics systems; Scalability of environmental HPC and Big Data applications; Risk analysis, assessment, management, and mitigation; Dynamic multi-directional model coupling approaches; Multifaceted data and metadata frameworks; Urgent computing and probabilistic models; Data visualisation and interactive analysis; Uncertainty quantification and visualization.

For more information, please visit the website.

The ‘Computational Sciences for Future Energy’ conference 2016

11 October, Utrecht

The conference will bring together the Dutch research community in computational science for energy applications. This event is particularly relevant for members of the Shell-NWO/FOM programme `Computational Sciences for Energy Research’ (CSER) and the NWO programmes `CO2 Neutral Fuels’ and `Uncertainty Reduction in Smart Energy Systems’ (URSES). 

For more information, please visit the website.

4th National eScience Symposium

13 October, Amsterdam

'Science in a Digital World' is a day-long event where scientists and researchers from different fields meet to discover how digital technology impacts scientific practice. The symposium will feature five thematic sessions showcasing world-class data-driven and compute-intensive research in different fields: Sports & eHealth, Social Data, Astronomy, Smart Energy, Data Science.

For more information, please visit the website.

i-KNOW Conference

18-19 October, Graz

i-KNOW 2016 aims at advancing research at the intersection of disciplines such as Knowledge Discovery, Semantics, Information Visualization, Visual Analytics, Social (Semantic) and Ubiquitous Computing. This year the Conference will focus on Cognitive Computing and Data-Driven Business.

For more information, please visit the website.

12th eScience IEEE International Conference

23 - 27 October 2016, Baltimore

The objective of the eScience Conference is to promote and encourage all aspects of eScience and its associated technologies, applications, and tools. In short, eScience promotes innovation in collaborative, computationally- or data-intensive research across all disciplines, throughout the research lifecycle. The conference invites experiences, insight, techniques, and tools that bridge the gap between scientific data management, scientific computing and science infrastructure. We seek novel contributions in the line of this conference series.

For more information, please visit the website.

Optimization challenges in the evolution of energy networks to smart grids

27 - 28 October, University of Coimbra

The objectives of this workshop are bringing together scientists, engineers, researchers, and students from academy and industry to share recent research and unveil new research directions concerning the use of optimization models and methods to address the challenges arising in the evolution of energy networks to smart grids. Amont the topics: integration of renewable energy generation, storage, demand forecast, demand side management and demand response, smart metering, system reliability and provision of ancillary services, microgrids, electricity smart grids, gas smart grids, network integration, information and communication technologies, internet of things, big data, and improved methods to optimize the resulting complex energy models.

For more information, please visit the website

SmartSec Europe - Advanced Cyber-Security Strategies to Achieve Smart Grid Resilience

29 - 30 November, Amsterdam

At the 3rd annual SmartSec Europe 2016 conference, exhibition and networking forum, 120+ utility IT and OT cyber-security leaders come together to share the lessons learnt from recent implementations of advanced cyber-security technologies and processes. End-to-end architectures will be discussed in conjunction with specific approaches for key points of vulnerability within the substation, SCADA systems, advanced metering infrastructure and more. The latest regulatory guidelines, standardisation activity and utility collaboration initiatives will be explored, and the longer term benefits of new technologies such as anomaly based intrusion detection systems and quantum computers will be investigated. 

For more information, please visit the website
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Centre for Data Science and Systems Complexity · Nijenborgh 9 · Groningen, Gr 9747 AG · Netherlands

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