Scientific programmer

2018-2019: E. Brinkhu1s: STRIMP project

Edwin’s work as a senior bughunter on the STRIP Assistant prescriptive polypharmacy platform focuses on making it ready for production in daily primary care practices (Funded by ZonMW/GGG).

Ph.D. students

2015-2019: V. Menger: Knowledge Discovery in Clinical Psychiatry.
Vincent develops the Psychiatry Research Analytics InfraStructurE (PRAISE), aiming to predict psychiatric conditions such as schizofrenia and autism through patient fingerprinting techniques enabled by an interorganisational information integration architecture which combines best practices knowledge with big data analytics explorations (Funded by UMCU).
2011-2019: W. Omta: Big data analytics in cell screening.
Wienand’s research investigates data management efficiency in high throughput processes for drug discovery and functional genomics to improve the effectiveness of data analysis by using advanced data mining techniques and improving the compatibility and openness of systems to connect external databases and ontologies (Funded by UMCU/UU).
2015-2019: Z. Shen: Prescriptive analytics in secondary care.
Ian’s OPERAM WP2 aims to develop a semantically interoperable and artificially intelligent medication prescribing platform named STRIP Assistant (STRIPA) 3.0 for OPtimising thERapy to prevent Avoidable hospital admissions in the Multimorbid elderly throughout Europe inspired by OpenCDS (Funded by Horizon2020).
2015-2020: A. Levebfre: Data stewardship for reusability and reproducibility in computational experiments.
Armel’s research investigates what makes computational experiments (CEs) reusable and reproducible, to facilitate the recording and exploitation of previous work, and how to enable new (meta)analytics of previous scientific work inside and between scientific groups, starting from research fields closely related to *omics technologies to build a preliminary maturity matrix of data and method management in research labs (Funded by UU/ITS).
2015-2020: R. Jagesar: Passive mobile health analytic systems (BeHapp)
Raj’s big data and distributed computing research addresses big data architecture considerations in the context of the BeHapp research initiative which aims to explore and classify communication and exploration patterns of human beings using objective data gathering techniques (Funded by Horizon2020).
2010-2020: J. van Dijk: Data quality management in data spaces within the judical domain.
Jan’s multi-organisational single-domain research investigates the Data Space approach on the crossroads of Data Warehousing, Privacy Preservation, Semantic Web, and Data Quality (Funded by Ministry of Security and Justice).
2016-2020: N. Seddik Tawfik: Text analytics in life sciences & health (TAILS).
Noha’s research focuses on big text data analytics for personalised medicine applications from both machine learning and computational linguistics perspectives. The first resulting analytic system is SNP Curator, which extracts information specifically in the genome wide and candidate genes studies (Funded by AAST).
2017-2020: A. Shojaifar: Web behaviour analytics in cybersecurity.
Alireza’s SMESEC WP aims to develop an automated cybersecurity assessment platform named Cybersecurity Coach (CySEC) which integrates personalised assessments, web usage behaviour, and advice adherence modelling, specifically for SMEs (Funded by Horizon2020).
2017-2020: B. Yigit Ozkan: Maturity modelling in cybersecurity.
Bilge’s SMESEC WP6 will develop a unified and personalised (CHOISS) information security (ISFAM) and cybersecurity (CYSFAM) focus area maturity model for security assessments, specifically for SMEs (Funded by Horizon2020).
2018-2022: I. Sarhan: Deep Learning for Query-based Summarisation (DEQUES).
Ingy’s research will focus on big text analytics for question answering systems, investigating both information retrieval and deep learning architectures, tentatively through an implementation of a query-based summarization approach (Funded by AAST).
2018-2023: C. van Toledo: Real-time Speech Analytic Systems for HR dialogue support (SpeechAS).
Chaïm’s research will focus on real-time speech analytics for real-time dialogue enrichment within a Human Resources context, and other speech and text analytics applications related to large-scale call centres such as those at P-Direct (Funded by P-Direct).