Adriënne has a bachelor degree in computer science and a master degree (cum laude) and PhD degree in biomedical image science.

Her PhD research at the image sciences institute (ISI) in the UMC Utrecht focused on image processing to reduce the X-ray radiation dose in computed tomography (CT) scans while maintaining image quality. She developed three noise reduction methods (one for 3D and two for 4D data) to improve the image quality of CT scans acquired with low radiation dose. As well as a method to derive vascular information from cerebral 4D CT perfusion (CTP) scans that has the potential to replace the additional CT angiography (CTA) scan.

As a post-doc at the biomedical image analysis group (BIGR) in the Erasmus MC (Rotterdam) she focused on noise reduction in 3D XperCT scans acquired with the C-arm CBCT system and compressed sensing, after which she returned to the ISI for her post-doc research on quantitative analysis of MR brain scans for cerebrovascular disease management.

During her post-doc research she developed an interest in grand challenges (, which are open scientific competitions that use evaluation data and metrics to rank the performance of algorithms with respect to an objective. She has organized the MICCAI ( grand challenge on MR brain image segmentation (MRBrainS) workshop in Nagoya (Japan) and has set-up and maintains the open MRBrainS challenge evaluation framework ( At IEEE ISBI 2015 ( in New York, she organized the challenge workshop on neonatal and adult MR brain image segmentation ( She is chair of the challenge workshops at IEEE ISBI ’16 and ’17 and co-organized the tutorial on designing benchmarks and challenges for measuring algorithm performance in biomedical image analysis at IEEE ISBI ’16. At the NFBIA Summer School 2015, she gave a workshop on designing challenges in biomedical image analysis.

She was initiator and organizer of the ImagO colloquium series on medical imaging for the PhD programme of the graduate school of life sciences in the UMC Utrecht and was in the program committee of the international workshop on machine learning in medical imaging (MLMI) ’15 and ’16 at MICCAI.

In October 2016, Adriënne joined the Netherlands eScience Center as an eScience coordinator.

Her research is currently focussed on representation learning and designing a theoretical framework for grand challenges in biomedical image analysis.

Jisk studied physics at the University of Groningen, graduating with honors from the theoretical physics program. After that he did his PhD on the topic of numerical solid state physics at the university of Nijmegen. His thesis focuses on the search for new (magnetic) materials for use in the semiconductor industry; this by using computational methods (density functional theory). This ignited his passion for high performance computing and big data. After finishing his PhD Jisk worked at SURFsara, the national HPC and e-science support center.

Crossing the line between physics and computation science again, Jisk worked several years at the Dutch national weather office (KNMI). As a member of the regional climate department he worked on improving and optimizing the (regional scale) climate model, as well as repurposing the high-resolution weather model for climate research. He also contributed to the KNMI climate scenarios for the Netherlands.

Jisk joined the Netherlands eScience Center as an eScience engineer to work on the Summer in the City project.

Rena holds double MSc in Applied Mathematics from Baku State University and in Computer Science from KTH, Sweden. In 2011 she received her PhD degree in Theoretical Computer Science from VU Amsterdam. Her research focused on (formal) modelling and analysis of large-scale stochastic systems. She worked as a postdoctoral fellow and an Assistant Professor at VU, and research visitor at NICTA Sydney and University of Melbourne on variety of interdisciplinary projects related to large-scale complex sytems.

Rena joined the Netherlands eScience Center in 2016, she is coordinating several climate science and physics projects.

Nicolas obtained a PhD in Nanoscience from University Paul Sabatier, Toulouse, France. During his PhD he studied new solutions for molecular quantum computing, or in simple words teaching single molecule how to count. In 2010, he joined the Theoretical Chemistry Department of Northwestern University where he studied how energy and electric charges propagate in biomolecules such as DNA and light harvesting complexes. He then moved in 2013 to the chemical Engineering Department of Delft University of Technology where he studied new ideas for solar energy research and molecular electronics.

Nicolas has developed different scientific software packages for various applications ranging from quantum transport to the calculation of electronic structure and electronic dynamics of molecular systems. He also has a keen interest in scientific visualization and illustration, statistical analysis of scientific data and the application of deep learning techniques to molecular science.

Nicolas joined the eScience Center in August 2017 as a research engineer.

Yifat has a bachelor degree in Physics from Ben-Gurion university (Israel) and a master in Physics from Tel-Aviv university (Israel), studying gravitational lensing in galaxy clusters. She received her Ph.D. in Planetary Science (Tel-Aviv university), where she focused on studying detection methods for transiting extra-solar planets. In the course of her research she became interested in statistics and algorithms and developed innovative methods for predictive modeling. These methods can be used to increase the yield of transiting planets from low-cadence surveys.

After moving to the Netherlands, Yifat continued to a post-doc position at the exoplanets group in the institute of Astronomy in the University Van Amsterdam, where she worked on statistical analysis of planetary atmospheres. Aside from planets, she is interested in machine learning, Bayesian inference, and algorithms.

Yifat joined the Netherlands eScience Center in summer 2017.

Lars’ research interests cover (bio)chemical informatics and simulations. He is responsible as engineer and project coordinator in multiple projects in the life-sciences and chemistry domains.

Lars studied Molecular Sciences at Wageningen University, graduating with honors in 1996. He obtained his PhD at the Wageningen University on Computational Studies of Enzyme Catalysis. In 2000, he was awarded a Marie-Curie fellowship at the School of Chemistry, University of Bristol, UK, during which he investigated the reaction mechanisms of biotransformation enzymes on the basis of combined quantum mechanical/molecular mechanical simulations.

In 2002, he joined Organon R&D, department of Molecular Design and Informatics. At Organon, which became part of Schering-Plough in 2009 and subsequently of Merck, he was responsible for in silico prediction of drug absorption, metabolism and toxicity and was part of multidisciplinary lead optimization teams.

From 2012 to 2015, Lars led an academic project on computational interpretation of LC-MS/MS and LC-MSn data of complex biological samples. The project resulted in a user-friendly webapplication allowing metabolomics experts to annotate unknown metabolites and supporting them in the discovery of novel biochemical pathways.

In May 2015 he joined the Netherlands eScience Center as senior eScience research engineer and project coordinator.

Ben van Werkhoven did his BSc in Computer Science and a research masters in Parallel and Distributed Computer Systems at the VU University Amsterdam. The focus of his PhD research was developing programming models and performance models for the efficiently using Graphics Processing Units within Supercomputing applications.

Ben’s main research interests involve increasing application performance with the use of Graphics Processing Units and increasing our understanding of the performance behavior of large GPU applications.

Within the Netherlands eScience Center Ben was involved in the following projects:

  • eSALSA
  • A Jungle Computing Approach to Large Scale Online Forensic Analysis project
  • Real-time detection of neutrinos from the distant Universe

In most of the projects Ben works on, he is responsible for accelerating scientific applications targeting GPUs and GPU-clusters.

Ben is also the creator of the Kernel Tuner, a simple tool for testing and auto-tuning CUDA, OpenCL, and C kernels from Python.


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