Sven did a bachelor in Psychobiology and a master in Brain and Cognitive Science at the University of Amsterdam. After obtaining his degree he was a PhD candidate at the University of Amsterdam in the field of cellular and systems neuroscience. He specialized in the recording and analysis of activity patterns in networks of brain cells.
Subsequently, he started working as web developer at Goldmund, Wyldebeast and Wunderliebe where he mostly worked on the website of Vacansoleil. Finally, he worked at Company.info as a data scientist, where he worked on building machine learning applications using structured and unstructured company data.
He joined the eScience center in June 2020 and works mostly on the big data & health projects.
After graduating from TU Darmstadt/Germany in Material Science, Jens obtained a PhD in physics from the TU/e Eindhoven in the Netherlands working on quantum chemistry and multiscale models for exciton transport in organic materials. Since his Ph.D., Jens has been developing software for scientific applications.
Jens focuses on the implementation of numerical algorithms and for high-performance computing using C++. His fields of expertise are computational chemistry and materials science.
Djura studied Artificial Intelligence at the University of Amsterdam (UvA) and received her Msc in 2015. After graduation she as worked as a Data Science consultant at Xomnia, Java developer at Eagle Eye networks, before becoming a Data Engineering consultant at Eonics. As a consultant she has seen big data being applied at various companies such as Nuon, Nationale Nederlanden, NDW, and Sanoma. She also worked on a R&D project for developing drones that can count warehouse inventory. Her focus was mainly on how to recognize shelf inventory from camera images.
Djura joined the Escience center in 2020 to work on the Big Data & Health projects.
Jesus studied Computer Science and Engineering at the Carlos III University of Madrid and has an MSc in Artificial Intelligence. Over the past 10 years, he has been developing multi-platform web applications with a focus on combining strong design aesthetics with technical know-how. During his career he has partnered with researchers and designers to develop and deliver new features, to translate concepts into living, breathing prototypes, interactions, animations and details to deliver the best user experience.
Breixo Soliño Fernández obtained his Computer Engineering degree at the University of Santiago de Compostela in 2013. After graduation, he worked for two years as an R&D engineer at Imatia Innovation, a small technological company.
In 2017 he came to the Netherlands to study a MSc in Artificial Intelligence at Utrecht University, and he is currently pursuing an internship at the eScience Center.
His main interests lie in autonomous robots, cognitive science, and the overlap between both disciplines. Other interests cover topics such as teaching and computer vision.
Ou Ku studied Geodesy in Central South University (China) where he obtained his BSc and discovered his interest in Earth Observation (EO). He continued his study in Delft University of Technology (TUDelft) and obtained a MSc degree in Geosciences and Remote Sensing. For his Master thesis, he focused on a specific EO technology: Interferometric Synthetic Aperture Radar (InSAR), which is applied for large-scale ground deformation monitoring. After his graduation in 2017, he continued his interest in InSAR and worked in an InSAR company named SkyGeo for two and half years.
Ou Ku has research interest in scientific computation and visualisation for Earth Observation applications. He also has interest in Deep Learning and GPU acceleration techniques.
Robin has a background in Physics and High Performance Computing, with a particular focus on the biophysical and materials simulation domains.
As a postdoctoral researcher at University College London he worked on the optimization of HemeLB, a high performance lattice-Boltzmann code for simulations of blood flow, integrating it into a clinical pipeline (CERREBRAL project, Qatar), and overseeing a complex coupling of two instances representing the human arterial and venous trees, with an external heart model (under the EU CompBioMed project). As part of the EU COMPAT project he co-designed the SCEMa material properties prediction model, cyclically coupling Molecular Dynamics simulations to a Finite Element macroscale model, for which he designed a graph based clustering algorithm to drastically reduce the number of MD simulations required. Under the EU VECMA project he led development on the EasyVVUQ python framework for Validation, Verification and Uncertainty Quantification of multiscale applications.
During his PhD at the University of Leeds, he wrote the Fluctuating Finite Element Analysis (FFEA) code, modelling molecular motors involved in the beating of flagella such as sperm tails. He obtained a master’s in Physics from the University of Edinburgh in 2010.
Robin joined the Netherlands eScience Center in February 2020 as senior eScience research engineer.
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 ( https://grand-challenge.org/All_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 (http://www.miccai2013.org/) grand challenge on MR brain image segmentation (MRBrainS) workshop in Nagoya (Japan) and has set-up and maintains the open MRBrainS challenge evaluation framework (http://mrbrains13.isi.uu.nl/). At IEEE ISBI 2015 (http://biomedicalimaging.org/2015/) in New York, she organized the challenge workshop on neonatal and adult MR brain image segmentation (neatbrains15.isi.uu.nl). 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.
Arnold graduated in Molecular Biology (2004, Comenius University in Bratislava, Slovakia). As an exchange student at Wageningen University and Research (WUR) in the Netherlands, he followed courses in bioinformatics and worked on his masters thesis in bioinformatics (Comparative genome analysis of White spot syndrome virus isolates).
He started his PhD research on comparative genomics of fungi and later focused on (phylogenetic) sequence-based clustering methods and data integration at the Laboratory of Bioinformatics, WUR (2009). In close collaboration with SURFsara, he used the Dutch Life Science Grid to compute a comprehensive matrix of corresponding genes (called orthologs) for all species with sequenced genomes. For this, he developed an efficient graph heuristics and an integrated protein resource.
As a postdoctoral researcher at the Department of Ecology and Evolution, University of Lausanne, Swiss Institute of Bioinformatics, he focused on genome-wide detection of positive selection in animal genomes using the Swiss Multi-science Computing Grid. Arnold returned to the Netherlands where he joined the Department of Genetics, Erasmus University Medical Center to study cellular effects of (non-)ionizing radiation using mass spectrometry-based proteomics.
In 2015 Arnold joined the team at the eScience Center. His interests include optimized (linked) data handing, analytics and/or efficient scientific workflows.
Alessio studied Computer Science at the University of L’Aquila (Italy), where he obtained a BSc and MSc (cum laude). He also obtained a MSc in Computer Science from the Vrije Universiteit (VU) in Amsterdam.
In Amsterdam he developed an interest in high-performance and parallel computing and began working with Graphics Processing Units (GPUs) for his master’s project, under the supervision of Prof. Dr. Rob van Nieuwpoort and Prof. Dr. Ana Lucia Varbanescu.
After his graduation he worked for over a year as a researcher at the VU, where he worked on the acceleration, with GPUs and other accelerators, of radio astronomy algorithms. He later started a PhD at the VU, on the topic of accelerating radio astronomy algorithms using many-core accelerators and auto-tuning, under the supervision of Prof. Dr. Henri Bal and Prof. Dr. Rob van Nieuwpoort.
During his PhD he also spent a year working as a scientific programmer at ASTRON, the Netherlands institute for radio astronomy, where he worked on the real-time pipeline for ARTS, the Apertif Radio Transients System. He is expected to defend his PhD at the VU in the first half of 2017. His research interests include high-performance and parallel computing, algorithms, and auto-tuning; his reference programming languages are C++ and OpenCL.
Alessio joined the Netherlands eScience Center in February 2017.