Vrije Universiteit Amsterdam



Mark Hoogendoorn


About me



email
m.hoogendoorn@vu.nl

phone
+31 20 598 7772

office
10A-87 of the NU.VU building (map)

postal address
Prof. dr. Mark Hoogendoorn
Vrije Universiteit Amsterdam
Faculty of Science
Department of Computer Science
De Boelelaan 1111
1081 HV Amsterdam
The Netherlands
I am a Full Professor of Artificial Intelligence at the Department of Computer Science of the Vrije Universiteit Amsterdam and chair of the Quantitative Data Analytics group.

My research focuses on machine learning and its applications, the latter primarily applied in the domain of health and wellbeing. The research ranges from more fundamental machine learning research to application driven research focusing on predictive modeling for diseases, personalized therapies and support systems, and eHealth and mHealth systems. For a list of my publications, click here. Together with my colleague Burkhardt Funk I have recently written a book on Machine Learning for the Quantified Self - On the Art of Learning from Sensory Data, published by Springer. You can find the website of the book here.

I am currently a member of the Board of Directors of ISRII and chair the Special Interest Group on Data Standards and Sharing in the same organization. I am also co-organzing Amsterdam Medical Data Science together with medical specialists from the Amsterdam UMC. In addition, I am a member of the Triple-E E-Health network, Amsterdam Data Science, the Network Institute, and the Amsterdam Center for Business Analytics. Before starting in the Quantitative Data Analytics group, I have been an Associate Professor in the Computational Intelligence group. I have also been a Visiting Scientist within the Clinical Decision Making Group headed by Peter Szolovits at MIT (CSAIL) during the Summer of 2015 and a PostDoc at the Department of Computer Science and Engineering at the University of Minnesota in the group of Maria Gini (Fall 2007). Before 2012 I was part of the Agent Systems Group at the VU. I obtained my PhD degree in 2007 from the Vrije Universiteit Amsterdam as well.

I am currently involved in various research projects:

  • Personalized smart health apps. This project aims to develop techniques to learn to provide feedback and support to users in an effective way by means of health apps on their smart phone. The main focus is on reinforcement learning techniques that adapt to the user to provide the right interventions and feedback at the best moments in a context dependent manner and learn to do this as fast as possible. This project is funded by Mobiquity Inc.

  • Constrained personalization of smart health apps. When personalization of apps on smart phones takes place automatically it is possible that certain undesired behavior is shown, e.g. inappropriate feedback or suggestions for interventions. This could be harmful for users which needs to be avoided. In this project, we aim to develop techniques to personalize within certain boundaries. We also consider this from a software architecture perspective and also study the consequences for the users. This project is a collaboration with the Software and Services Group of the VU (Patricia Lago and Ivano Malavolta).

  • Machine Learning for Intensive Care. This project aims to develop predictive models based on Electronic Medical Records for the Intensive Care department at the VU University Medical Center using machine learning techniques. The predictive models should facilitate more effective decision making of the medical staff. The project is a collaboration with Paul Elbers and Armand Girbes.
  • Predictive modeling and outlier detection for large datasets. A joint project with the Ministry of the Interior, the Department of Mathematics of the VU (Sandjai Bhulai), and the Center for Mathematics and Computer Science (Rob van der Mei).

  • Constrained reinforcement learning for personalization in highly regulated domains. Adaptivity and personalization can be of huge benefit in highly regulated domains such as healthcare and finance. Strong guarantees on safety of behavior is a prerequisite for adoption of systems in these domains. This project is aimed at (a) bridging the gap between regulators' and agent's behavior representation and (b) reinforcement learning under the resulting constraints aimed at the application of a Adaptive Personal Assistant. This project is funded by ING and is a collaboration with the Knowledge Representation and Reasoning group at the VU (Frank van Harmelen).

  • RESCUED. The identification of risk factors for cardiac arrest among diabetes patients is the main goal of this project. These risk factors will be identified based on General Practitioner data as well as other data sources (such as genetic data). Machine learning techniques will be developed and exploited to create accurate risk models. This project is a collaboration with the VUmc, AMC, and LUMC and is funded by CVON (a funding scheme by NWO, the Hartstichting and the joint university medical centers).

  • PIPPI. This project concerns the identification of risk models for mental health problems among children and adolescents using data from general practitioners and the municipality health services. This project is a collaboration with the LUMC and is funded by NWO (ZonMW).

  • Efficient Deep Learning. In this project, funded by NWO (TTW), the goal is to improve the efficiency of deep learning algorithms. Here, efficiency is related to the number of examples needed, the usage of the platform on which these algorithms are run, and also making the results obtained using the deep learning algorithms more insightful. The specific focus of our contribution will be related to sensory data. The project is a collaboration with various research institutes (UvA, CWI, TUE) as well as companies.

  • EFRO Smart Maintenance. In this project, funded by the EU (EFRO), the goal is to use and improve machine learning techniques for predictive maintenance and optimization of maintenance. Hereby, TATA steel is the main case study and various companies are involved that provide additional sensors to fuel the machine learning with richer data. The project is a collaboration with the Department of Mathematics of the VU (Ger Koole)
A selection of past projects include: E-COMPARED (FP7 Health project) studying the effectiveness of therapies for depression where we focused on the generation of predictive models for patients to enable more effective policy making on what therapy is best suited for which patients. The project uses an automated mobile therapy developed in the EU FP7 funded ICT project ICT4Depression, a project I jointly coordinated with Michel Klein. In another project (funded partially by Philips Research) we studied machine learning approaches that enable more personalized forms of support for a healthy lifestyle. Finally, I have been involved in a project on Prediction of Colorectal Cancer (joint with various University Medical Centers and Annette ten Teije at the VU).

I'm also involved in teaching several courses related to Artificial Intelligence and Machine Learning. In addition, I'm the chair of the Examination Board (for the subcommittee for the Department of Computer Science) of the Faculty of Sciences. For a bit more detail, see the information below.



Team

Current
  • Frank Bennis (PostDoc, working on the RESCUED project)
  • Lucas Fleuren (PhD student, working on Machine Learning for Intensive Care)
  • Eoin Grua (PhD student, working on Constrained Personalization of Smart Health Apps)
  • Ali el Hassouni (PhD student, working on Personalized Smart Health Apps)
  • Floris den Hengst (PhD student, working on Constrained reinforcement learning for personalization in highly regulated domains)
  • Jan Klein (PhD student, working on Predictive modeling and outlier detection for large datasets)
  • Luca Roggeveen (PhD student, working on Machine Learning for Intensive Care)
  • David Wilson Romero Guzman (PhD student, working on Efficient Deep Learning)
  • Luis Silvestrin (PhD student, working on EFRO Smart Maintenace)
Past
  • Ward van Breda (PhD student, graduated on June 23, 2020)
  • Amin Tabatabaei (PostDoc, 2018-2019)
  • Bart Kamphorst (PostDoc, 2016-2019)
  • Giorgos Karafotias (PhD student, graduated on February 24, 2016)
  • Robbert-Jan Merk (PhD student, graduated on February 6, 2013)
  • Fiemke Both (PhD student, graduated on June 5, 2012)
  • Rianne van Lambalgen (PhD student, graduated on April 3, 2012)
  • Muhammad Umair (PhD student, graduated on February 6, 2012)
  • Syed Waqar Jaffry (PhD student, graduated on September 9, 2011)


Teaching

I am the coordinator of the various artificial intelligence master programs. Furthermore, I teach several courses. Information regarding the courses I teach can be found on Canvas. I'm currently involved in teaching/coordinating the following courses:

  • Data Mining Techniques
  • Machine Learning for the Quantified Self
  • Bedrijfscase
  • Mini Master Project (coordinator)


Recent Committee Memberships (not very complete)

  • Co-organizer, Amsterdam Medical Data Science (AMDS, 2018-present)
  • Associate Editor, Internet Interventions (Elsevier, 2016-present)
  • Board Member International Society for Research on Internet Interventions (ISRII, 2016-present)
  • Chair Special Interest Group in Data Standards and Sharing as part of ISRII (2015-present)
  • Organizing Committee Member ISRII 9th Scientific Meeting (ISRII, 2017)
  • Finance Chair 28th Benelux Conference on Artificial Intelligence (BNAIC 2016)
  • Guest Editor (together with Tibor Bosse) for Special Issue of Applied Intelligence on Advances in Applied Artificial Intelligence (published by Springer)
  • IEEE/WIC/ACM International Conference on Web Intelligence (merged with IAT) (PC Member @ WI 2017/2016)
  • IEEE/WIC/ACM International Conference on Intelligent Agent Technology (PC Vice Chair @ IAT 2008, PC Member @ IAT 2015/2014/2013/2012/2011/2010)
  • IEEE/RSJ International Conference on Intelligent Robots and Systems (Associate Editor @ IROS 2014/2012)
  • International Conference on Autonomous Agents and Multiagent Systems (Doctoral Consortium Chair @ AAMAS 2013, PC Member @ AAMAS 2015/2012/2011/2010/2009/2008)
  • International Conference on Industrial, Engineering & Other Applications of Applied Intelligent Systems (PC Chair @ IEA/AIE 2013, PC Member @ IEA/AIE 2014/2012/2010/2009, Special Session Organizer @ IEA/AIE-2012/2011/2010)
  • International Workshop on Computing Paradigms for Mental Health (Co-Chair @ Mindcare 2013/2012)
  • AAAI Conference on Artificial Intelligence (PC Member AAAI 2013/2012)
  • Invited Session on Innovative Technology in Mental Healthcare at the 4th International KES Conference on Innovation in Medicine and Healthcare (PC Member @ Session on Innovative Technology in Mental Healthcare at KES-InMed-16)
  • International Conference on Agents and Artificial Intelligence (PC Member @ ICAART 2017/2016/2015/2014/2013/2012/2011/2010/2009)
  • European Conference on Modeling and Simulation (PC Member @ ECMS 2017/2016/2015/2014/2013/2012/2011/2010)
  • International Conference on Pervasive and Embedded Computing and Communication Systems (PC Member @ PECCS 2015/2014)
  • International Conference on Electronic Commerce (PC Member @ ICEC 2017/2016/2015/2012/2011)
  • Symposium on Artificial Life and Intelligent Agents (PC Member @ ALIA 2016)