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My main research areas include machine learning from unstructured data in diverse applications such as information retrieval, text mining, multimedia search, context sensitive AI, health data, personalized health and data driven policy making. As a recognized expert in the area of Information Retrieval, I have been active in the application domains (social) media analysis, bioinformatics and health. I am mostly known for my contribution to TRECVid – the global benchmark forum on the evaluation of content-based analysis of digital video. More recently, I have changed focus to the analysis of unstructured health data for the purpose of developing more effective personalized health advice and interventions. An important sub-problem is the challenge to learn from sensitive data without disclosing the data in a federated setting. Other relevant challenges include the development of standardized analytics of lifestyle and health across decades of time span to allow analogical reasoning across large cohorts of health data.
My ambition is to link TNO’s operational excellence with the longer term research horizons of academic research to achieve impact in the domain of AI and health.
My recent work at TNO has focused on learning predictive models from distributed health data [COMMIT/ Prana Data, H2020 RECAP-PRETERM, H2020 BIGMEDILYTICS]. Federated secure machine learning from distributed data has been demonstrated as a Proof-of-Concept at the mid-term review of BigMedilytics.
As scientific lead of the Early Research Programme, I guided the development of novel methods for modeling and reasoning with uncertainty in big data processing pipelines. In February 2018, I was appointed director of the Leiden University Data Science programme covering data intensive research in natural, social and health sciences as well as humanities. In 2017, I was recognized as distinguished member of the ACM for outstanding contributions to computing. In 2018, I was awarded the IEEE PAMI Mark Everingham award for co-leading the ‘TRECVid Video Retrieval Evaluation 2003-2018’.
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