Biography and publications

Biography

Jasper van der Waa is a researcher on human-AI interaction mixing fundamental and applied research.

Dr. Jasper van der Waa, born 1991 in the Netherlands, studied Artificial Intelligence at the Radboud University in Nijmegen. After his master thesis focused on interactive machine learning, he started at TNO in 2016. TNO is the Dutch research institute for applied research, where Jasper’s research revolves around optimizing the human-AI interaction by expanding the AI’s functionality through the identification of requirements and development of new algorithms and technologies. In 2018 he started his PhD at the Technical University of Delft on the topic of explainable AI for improving human-AI collaboration. He defended his thesis four years later which received the cum laude distinction, reserved for the top 5%.

He collaborates closely with companies and ministries to identify the most pressing issues in the application of AI while collaborating with universities to address these issues through research. He worked with financial institutes (ABN Amro, Achmea), AI companies (Xomnia, Enjin), healthcare institutes (Curium, Amsterdam UMC), the Dutch government (Ministry of Defence, Ministry of Internal Affairs, Employee Insurance Agency) and other research institutes (NLR, Marin, Michigan University, MIT). He acted as liaison between the Technical University of Delft and the Ministry of Defence (KMA) on moral value elicitation. Furthermore he participates in NATO meetings and roundtable discussions on the topic of AI in relation to current and future AI regulations. Finally, he leads a lab on applying the principles of hybrid intelligence in practice, providing PhD students and post-docs with a use case to embed their research in.

Aside from his collaboration with various parties, he organizes several community events such as summer schools, hackathons, presentation sessions and panel discussions that revolve around human-AI interaction. Furthermore, he is the founder of two open source projects and their communities. The first is MATRX, a tool to accelerate human-machine teaming research by offering a platform to evaluate and test that research in various tasks. The second is the XAI Toolbox that aims to standardize and ease the use of the responsible application of explainable AI technologies.

His goal in all of these efforts is to enable effective and responsible human-AI collaboration and build and sustain communities that share this goal.

List of publications

1. 2023

2. MATRX: Human Agent Teaming Rapid Experimentation software

   J. van der Waa & T. Haije.

3. 2022

4. An Optimum Well Control Using Reinforcement Learning And Policy Transfer; Application To Production Optimization And Slugging Control

   J. Poort, P. Shoeibi Omrani, T. Mannucci & J. van der Waa.

5. Actionable Explanations for Contestable AI

   J. van der Waa, J. van Diggelen, M. Neerincx & C. Jonker. Journal of Artificial Intelligence Research

6. Towards FAIR Explainable AI: a standardized ontology for mapping XAI solutions to use cases, explanations, and AI systems

   A. Adhikari, E. Wenink, J. van der Waa, C. Bouter, I. Tolios & S. Raaijmakers. Conference on Pervasive Technologies Related to Assistive Environments

7. Intelligent Operator Support Concepts for Shore Control Centres

   H. van den Broek & J. van der Waa. International Conference on Maritime Autonomous Surface Ships

8. Dynamic task allocation algorithms within intelligent operator support concepts for shore control centres

   T. Brug, J. van der Waa, V. Maccatrozzo & H. van den Broek. Conference on Computer Applications and Information Technology in the Maritime Industries

9. The FATE system iterated: Fair, Transparent and Explainable Decision Making in a Juridical Case

   M. de Boer, S. Vethman, R. Bakker, A. Adhikari, M. Marcus, J. de Greeff, J. van der Waa, T. Schoonderwoerd, I. Tolios, E. van Zoelen, F. Hillerström & B. Kamphorst. AAAI Spring Symposium on Machine Learning and Knowledge Engineering for Hybrid Intelligence

10. Data science for service design: An introductory overview of methods and opportunities

    Y. Kunneman, M. Motta-Filho & J. van der Waa. The Design Journal

11. 2021

12. Moral decision making in human-agent teams: Human control and the role of explanations

    J. van der Waa, S. Verdult, K. van den Bosch, J. van Diggelen, T. Haije, B. van der Stigchel & I. Cocu. Frontiers in Robotics and AI

13. 2020

14. Interpretable confidence measures for decision support systems

    J. van der Waa, T. Schoonderwoerd, J. van Diggelen & M. Neerincx. International Journal of Human-Computer Studies

15. Evaluating XAI: A comparison of rule-based and example-based explanations

    J. van der Waa, E. Nieuwburg, A. Cremers & M. Neerincx. Artificial Intelligence

16. Delegation in Human-Machine Teaming: Progress, Challenges and Prospects

    J. van Diggelen, J. Barnhoorn, R. Post, J. Sijs, N. van der Stap & J. van der Waa. International Conference on Intelligent Human Systems Integration

17. Allocation of Moral Decision-Making in Human-Agent Teams: A Pattern Approach

    J. van der Waa, J. van Diggelen, L. Cavalcante Siebert, M. Neerincx & C. Jonker. International Conference on Human-Computer Interaction

18. 2019

19. Streaming virtual reality video

    G. Schilt, H. Stokking, A.M. Brouwer, J. Van Der Waa & M. Hogervorst. US patents

20. Intelligent autonomous vehicles with an extendable knowledge base and meaningful human control

    G. Beckers, J. Sijs, J. van Diggelen, R. van Dijk, H. Bouma, M. Lomme, R. Hommes, F. Hillerstrom, J. van der Waa, A. van Velsen, T. Mannucci, J. Voogd, W. van Staal, K. Veltman, P. Wessels & A. Huizing. Counterterrorism, Crime Fighting, Forensics, and Surveillance Technologies III

21. Pluggable Social Artificial Intelligence for Enabling Human-Agent Teaming

    J. van Diggelen, J. Barnhoorn, M. Peeters, W. van Staal, M. van Stolk, B. van der Vecht, J. van der Waa & J.M. Schraagen. NATO HFM symposium on Human Autonomy Teaming

22. 2018

23. BCI to potentially enhance streaming images to a VR headset by predicting head rotation

    A.M. Brouwer, J. van der Waa & H. Stokking. Frontiers in Human Neuroscience

24. Contrastive Explanations for Reinforcement Learning in terms of Expected Consequences

    J. van der Waa, J. van Diggelen, K. van den Bosch & M. Neerincx. IJCAI-18 Workshop on Explainable AI (XAI)

25. Using Perceptual and Cognitive Explanations for Enhanced Human-Agent Team Performance

    M. Neerincx, J. van der Waa, F. Kaptein & J. van Diggelen. International Conference on Engineering Psychology and Cognitive Ergonomics

26. Predicting head rotation using EEG to enhance streaming of images to a Virtual Reality headset

    A.M. Brouwer, J. Van Der Waa & H. Stokking. 2nd International Neuroergonomics Conference

27. SAIL: a social artificial intelligence layer for human-machine teaming

    B. van der Vecht, J. van Diggelen, M. Peeters, J. Barnhoorn & J. van der Waa. International Conference on Practical Applications of Agents and Multi-Agent Systems

28. The SAIL Framework for Implementing Human-Machine Teaming Concepts

    B. van der Vecht, J. van Diggelen, M. Peeters, W. van Staal & J. van der Waa. International Conference on Practical Applications of Agents and Multi-Agent Systems

29. Contrastive Explanations with Local Foil Trees

    J. van der Waa, M. Robeer, J. van Diggelen, M. Brinkhuis & M. Neerincx. ICML-18 Workshop on Human Interpretability in Machine Learning (WHI'18)

30. The design and validation of an intuitive confidence measure

    J. van der Waa, J. van Diggelen & M. Neerincx. In Workshop On Explainable Smart Systems (EXSS)

31. ICM: An Intuitive Model Independent and Accurate Certainty Measure for Machine Learning.

    J. van der Waa, J. van Diggelen, M. Neerincx & S. Raaijmakers. ICAART '18

32. 2017

33. An Intelligent Operator Support System for Dynamic Positioning

    J. van Diggelen, H. van den Broek, J.M. Schraagen & J. van der Waa. International Conference on Applied Human Factors and Ergonomics

34. Human Robot Team Development: An Operational and Technical Perspective

    J. van Diggelen, R. Looije, J. van der Waa & M. Neerincx. International Conference on Applied Human Factors and Ergonomics

35. A feasible BCI in real life: Using predicted head rotation to improve HMD imaging

    A.M. Brouwer, J. van der Waa, M. Hogervorst, A. Cacace & H. Stokking. Proceedings of the 2017 ACM Workshop on An Application-oriented Approach to BCI out of the laboratory

36. 2015

37. Interactive Reinformcement Learning; Two successful solutions for handling an abundance of positive feedback

    J. van der Waa. Radboud University

38. 2013

39. Connecting the Demons: How connection choices of a Horde implementation affect Demon prediction capabilities.

    J. van der Waa. Radboud University