Research

I am a passionate data scientist with special interest in Artificial Intelligence (AI), a background in computing science, AI and mathematics and deep interest in learning mechanics and the application of AI methods to challenging problems. >15 years of experience in a large multinational industrial research organization, including technical supervision and leadership, managing internal and external stake holders and working with sensitive data. I have a strong academic network, and acted as co-promotor. I enjoy working in multi-disciplinary teams; am keen on learning new domain knowledge and integrating that in own work; love solving puzzles and technical challenges, and like programming and creating prototypes and demonstrators not only as proof-of-concept but also as tool for voice-of-customer interaction.

During my career I have continuously developed, deepened and broadened my skill set by working at a large variety of technical challenges in different application domains, including time-series data from physiological and activity data, audio, photo, video, clinical data from Cardiac and Pulmonary patients, and healthcare claims data; by applying statistical and machine learning techniques, both supervised and unsupervised, to datasets ranging from hundreds to millions of samples.
I quickly learn any new programming/scripting language and enjoy exploring new techniques. My strength is in integrating domain knowledge throughout the pipeline to extract information from data, ranging from signal processing, feature extraction, interpretation, statistical analysis, machine learning & AI, all the way to implementing prototypes and product integration, including cloud services such as MS Azure and Amazon AWS, but also data acquisition, processing, suitable storage and security. My extensive experience in this end-to-end process extends beyond a pure research environment, as I also closely collaborate with businesses and other internal and external stake holders. My creativity has not only lead to highly performant algorithms but is also reflected in more than 30 patents and patent applications.

During the years 2002-2005 I studied Computing Science (Bachelor) at Groningen University (Rijksuniversiteit Groningen), directly followed by the Master studies specializing in 'Intelligent Systems'. In 2007 I received my Master's degree in Computing Science (Cum Laude) after successfully completing my Master's Thesis on the topic of "Analysis of Robust Soft Learning Vector Quantization", and started working at Philips Research. During the years 2010-2014, next to the full time job, I completed my Ph.D. dissertation on the topic of Modeling Affective State using Learning Vector Quantization, also at Groningen University.

In my first 4 years with Philips Research, my work focused on the Lifestyle domain and was around the topic of measuring emotions and stress using physiological measurements (e.g., skin conductance, electrocardiogram, respiration measurements). Key elements in this work are automated feature extraction and interpretation of physiological signals, including classification of interpreted signals, as well as modeling of emotions. Part of this work has been done within the European project named REFLECT, in which I contributed to a demonstrator system, built into a Ferrari sports car.
In 2011 I moved to the Medical domain and started working on the topic of Clinical Decision Support (CDS), focusing at Readmission Management and Discharge Planning in particular for Heart Failure (HF) patients and multi-morbid, chronic patients in general. Later, my clinical domain focus moved to COPD and Parkinson's disease. This work comprises the development of intelligent algorithms and models to quantify and predict patients' risks of various adverse outcomes. Recently, I also performed explorative data analysis of population health data. This encompasses the application of unsupervised machine learning and natural language processing and natural language generation techniques to obtain actionable insights from patient population data to support clinicians in making better decisions.

My expertise as Senior Data & AI Scientist is in the area of Pattern Recognition/Classification, Machine Learning, Clustering, Data Mining; in particular white-box methods (e.g., prototype based classifying), Signal Processing, Signal Interpretation, Feature Extraction, Mathematical Modeling, Algorithm Development, Clinical Decision Support, Clinical Research, Affective Computing, Physiological Measurements. My skillset is broad and spans from data collection via interpretation all the way building functional prototypes and deploying demonstrators.

On the subpages you can find an overview of my Publications (note that this is my maintained list of publications and that the 'academic profiles', of which you can find icons and links below, might be incomplete and incorrect) and Professional Activities.