Everything Flo’s

Picture of Floris

My name is Floris den Hengst and I am a PostDoc working on reinforcement learning for patient-centric decision-making in the Hybrid Intelligence centre with Annette ten Teije at the Vrije Universiteit Amsterdam and Herke van Hoof at the University of Amsterdam.

I am interested in adaptivity v.s. control in learning systems, sequential decision-making problems, goal and constraint specification and uncertainty quantification. My research touches on practical and fundamental aspects of explainability and safety of AI.

I performed my PhD research in the Quantitative Data Analytics and Learning & Reasoning groups of the Vrije Universiteit Amsterdam and ING’s AI for FinTech Research and have hands-on experience as a software engineer and data scientist.


PostDoc, Vrije Universiteit, current

PhD student, Vrije Universiteit Amsterdam and ING, 2017-2023

Software Engineer, Crunchr, 2014 - 2017

Msc. Artificial Intelligence, Cum Laude, Vrije Universiteit Amsterdam, 2013 - 2016

Web Developer, Zilt&Co, 2009 - 2014

Various teaching- and research assistant positions, 2009 - 2014

Bsc. Artificial Intelligence, GPA 7.9 / 10, Vrije Universiteit Amsterdam, 2008 - 2013
Officially titled `Lifestyle Informatics’ at the time



den Hengst, Floris and Wolter, Ralf and Altmeyer, Patrick and Kaygan, Arda, “Conformal Intent Classification and Clarification for Fast and Accurate Intent Recognition” to appear in NAACL Findings, ACL (2024)


den Hengst, Floris and Otten, Martijn and Elbers, Paul and François-Lavet, Vincent and van Harmelen, Frank and Hoogendoorn, Mark, “Guideline-informed reinforcement learning for mechanical ventilation in critical care” Artificial Intelligence in Medicine, Elsevier (2023)
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Visbeek, Samantha and Acar, Erman and den Hengst, Floris “Explainable Fraud Detection with Deep Symbolic Classification” 2023 Workshop on Explainable AI in Finance
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Otten, Martijn and Jagesar, Ameet R. and Dam, Tariq A. and Biesheuvel, Laurens A. and den Hengst, Floris and Ziesemer, Kirsten A. and Thoral, Patrick J. and de Grooth, Harm-Jan and Girbes, Armand R.J. and François-Lavet, Vincent and Hoogendoorn, Mark and Elbers, Paul W.G., “Does Reinforcement Learning Improve Outcomes for Critically Ill Patients? A Systematic Review and Level-of-Readiness Assessment” Critical Care Medicine, LWW (2023)
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den Hengst, Floris “Learning to Behave: Reinforcement Learning in Human Contexts”.
PhD. thesis
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Petrescu, Stefan and den Hengst, Floris and Uta, Alexandru and Rellermeyer, Jan S., “Log Parsing Evaluation in the Era of Modern Software Systems” The 34th IEEE International Symposium on Software Reliability Engineering
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Smit, Yannick and den Hengst, Floris and Bhulai, Sandjai and Mehdad, Ehsan, “Strategic Workforce Planning with Deep Reinforcement Learning”. Machine Learning, Optimization, and Data Science (2022)
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Den Hengst, Floris and François-Lavet, Vincent and Hoogendoorn, Mark and Van Harmelen, Frank, “Reinforcement Learning with Option Machines”. Proceedings of the Thirty-First International Joint Conference on Artificial Intelligence, 2909-2915 (2022)
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Den Hengst, Floris and François-Lavet, Vincent and Hoogendoorn, Mark and Van Harmelen, Frank, “Planning for potential: efficient safe reinforcement learning”. Machine Learning, Springer (2022)
Presented at BeNeRL 2022
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Den Hengst, Floris and Grua, Eoin Martino and el Hassouni, Ali and Hoogendoorn, Mark, “Reinforcement Learning for Personalization: A Systematic Literature Review”. Data Science (2020)
Presented at RL for Real Life conference 2020, presented at BNAIC 2020
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Van Zeelt, Mickey and Den Hengst, Floris and Hashemi, Seyyed Hadi, “Collecting High Quality Dialogue User Satisfaction Ratings with Third-Party Annotators.” Proceedings of the 2020 Conference on Human Information Interaction and Retrieval, 363-367 (2020)
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Den Hengst, Floris and Hoogendoorn, Mark and Van Harmelen, Frank and Bosman, Joost, “Reinforcement Learning for Personalized Dialogue Management”. 2019 IEEE/WIC/ACM International Conference on Web Intelligence (WI), 59-76 (2019)
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Floris den Hengst - Detecting Interesting Outliers: Active Learning for Anomaly Detection, Master’s thesis Artificial Intelligence, Vrije Universiteit Amsterdam (2016)