Hoogleraar Advanced Data Science in Population Health
Prof. Dr. M.R. (Marco) Spruit
Area(s) of expertise:
Translational Data Science, Natural Language Processing, Machine Learning, Artificial Intelligence, Health Informatics, Cybersecurity
Translational Data Science, Natural Language Processing, Machine Learning, Artificial Intelligence, Health Informatics, Cybersecurity
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Introduction
I was appointed as Professor of Data Science & AI in Population Health at Leiden University in 2020 at both the Department of Public Health & Primary Care (PHEG) of the LUMC as well as at the Leiden Institute of Advanced Computer Science (LIACS) of the Faculty of Science (FWN). I delivered my inaugural lecture on 1 April 2022, as can be seen at https://youtu.be/XfzEEAbNb4w.
Essentially, I connect practical problems in daily healthcare practices with fundamental challenges in data science.
Until 2020, I worked as an associate professor in the Department of Information and Computing Sciences at Utrecht University. Before 2007, I spent 14 years in business as a Natural Language Processing (NLP) and Big Data developer and entrepreneur.
Essentially, I connect practical problems in daily healthcare practices with fundamental challenges in data science.
Until 2020, I worked as an associate professor in the Department of Information and Computing Sciences at Utrecht University. Before 2007, I spent 14 years in business as a Natural Language Processing (NLP) and Big Data developer and entrepreneur.
Scientific research
At the Health Campus The Hague, I started the Translational Data Science (TDS) Lab. Our strategic research objective is to establish an authoritative and open national infrastructure for Dutch health research, education and care to accelerate innovation and democratise data science technologies through, in particular, natural language processing (NLP), automated machine learning (AutoML) and synthetic data generation technologies. My research theme Translational Data Science in Population Health has three complementary lines of research that together address the continuous knowledge discovery process, as operationalised by the CRoss-Industry Standard Process for Data science (CRISP-DM): Data Engineering, Data Analytics, and e-Health Implementation. At the time of writing, we are working on 6 awarded grants and 6 collaborative projects in the TDS Lab, carried out by 10 PhD students and 2 postdocs.
