 These are the professional pages of prof.dr. Marco Spruit. I am Full Professor of Advanced Data Science in Population Health at
the department of Public Health & Primary Care (PHEG) of the Leiden
University Medical Centre (LUMC) and the Leiden Institute of Advanced
Computer Science (LIACS) of the faculty of Science (FWN) of Leiden University.
My overarching research objective is to establish an authoritative national
infrastructure for Dutch Natural Language Processing and Machine
Learning to facilitate and popularise Self-Service Data Science. Current and future work, as well as previous projects performed within UU's Applied Data Science Lab, are now embedded within the Translational Data Science Lab at Leiden University.
Within the broad spectrum of Data Science, the discipline of Translational Data Science encompasses the fields of Applied Data Science and Self-Service Data Science to translate new algorithms to novel applications and introduce new insights from these novel applications into daily practices, respectively. This is analogous to the well-known domain of Translational Medicine, where T1 refers to bench-to-bedside research, and T2 refers to clinial trials in daily practices. Our work, therefore, spans the entire knowledge discovery process (e.g. CRISP-DM). The overarching research objective is to establish an authoritative national infrastructure for Dutch Natural Language Processing and Machine Learning to facilitate and popularise Self-Service Data Science. We focus in particular on the Population Health and Wellbeing domain (more). I have notably conducted several European Horizon2020 studies (OPERAM,
SAF21, SMESEC, GEIGER) and nationally funded research projects (e.g.
STRIMP). I have authored 200+ publications including 85+ journal papers. My research team currently consists of 10 Ph.D students and 1 postdoc. I have successfully completed 7 Ph.D projects as daily supervisor.
| Recently Added News
At the Leiden UMC Campus The Hague (LCDH) Marco is establishing the research line Translational Data Science in Population Health. This line has three themes. In Data Engineering for Population Health,
we investigate the further consolidation, standardisation and
enrichment of the ELAN information infrastructure, in line with
Health-RI. In Data Analytics for Population Health, Natural
Language Processing and Machine Learning techniques are investigated for
their suitability to answer current and new types of LCDH research
questions from a Self-Service Data Science perspective, in collaboration
with the LIACS department. In eHealth Implementations for Population Health,
we design and implement data science interventions through eHealth,
mHealth and NLP software solutions within the LCDH region, in close
collaboration with the LCDH partners and the National eHealth Living Lab
(NeLL).
Before, I was an Associate Professor and, before that, Assistant
Professor in the Natural Language Processing and Organisation &
Information research groups, respectively, of the Department of
Information and Computing Sciences within the Faculty of Science at
Utrecht University for 13 years. Until 2007 I worked in industry for
fourteen years as an Information Retrieval and Big Data engineer at
ZyLAB Europe and the Intelligence Service, among others. |
