Hi, I’m a research scientist at Simula Research Laboratory in Oslo, Norway. My current research topics are self-supervised neuro-symbolic solvers for constraint satisfaction, which I research in collaboration with the University of Bonn, Germany, under the AutoCSP project funded by the Norwegian Research Council.
Previously, I was a PhD Student at University of Oslo and Simula within the Certus Centre for Software Validation and Verification (Certus SFI) under the supervision of Arnaud Gotlieb, Magne Jørgensen and Morten Mossige.
I’m interested in the intersection and integration of data-driven machine learning and logic-driven symbolic AI methods, such as Constraint Programming, especially with an application in the domain of software testing.
- [Workshop] Ahuja, M.K., Gotlieb, A., Spieker, H. (2022). Testing Deep Learning Models: A First Comparative Study of Multiple Testing Techniques. Artificial Intelligence in Software Testing @ ICST 2022. DOI: 10.1109/ICSTW55395.2022.00035 arXiv
- [Conference] Belaid, M. B., Belmecheri, N., Gotlieb, A., Lazaar, N., Spieker, H. (2022). GEQCA: Generic Qualitative Constraint Acquisition. AAAI. DOI: 10.1609/aaai.v36i4.20282
- [Journal] Spieker, H., Gotlieb, A. (2021). Predictive Machine Learning of Objective Boundaries for Solving COPs. AI. Vol. 2, No. 4. MDPI. DOI: 10.3390/ai2040033 arXiv
- [Book Chapter] Gotlieb, A., Marijan, D., Spieker, H. (2021). Testing Industrial Robotic Systems: A New Battlefield!. In: Cavalcanti, A., Dongol, B., Hierons, R., Timmis, J., Woodcock, J. (eds) Software Engineering for Robotics. Springer, Cham. DOI: 10.1007/978-3-030-66494-7_4
- [Conference] Spieker, H. (2021). Constraint-Guided Reinforcement Learning: Augmenting the Agent-Environment-Interaction. International Joint Conference on Neural Networks (IJCNN). arXiv
- [Conference] Ahuja, M. K., Belaid, M. B., Bernabé, P., Gotlieb, A., Marijan, D., Sharif, A., Spieker, H. (2021). In Proceedings of the 31st European Safety and Reliability Conference (ESREL). ESREL, 2021.
- [Journal] Gotlieb, A., Marijan, D., Spieker, H. (2020). ITE: A Lightweight Implementation of Stratified Reasoning for Constructive Logical Operators. International Journal on Artificial Intelligence Tools. Vol. 29, No. 03n04. DOI: 10.1142/S0218213020600064 arXiv
- [Conference] Spieker, H., Gotlieb, A. (2020). Learning Objective Boundaries for Constraint Optimization Problems. Machine Learning, Optimization, and Data Science. LOD 2020. Lecture Notes in Computer Science, vol 12566. DOI: 10.1007/978-3-030-64580-9_33 arXiv
- [Workshop] Ahuja, M.K., Belaid, M.B., Bernabe, P., Collet, M., Gotlieb, A., Lal, C., Marijan, D., Sen, S., Sharif, A., Spieker, H. (2020). Opening the Software Engineering Toolbox for the Assessment of Trustworthy AI. In 1st International Workshop on New Foundations for Human-Centered AI @ ECAI 2020. arXiv PDF
- [Journal] Spieker, H., Gotlieb, A. (2020). Adaptive Metamorphic Testing with Contextual Bandits. Journal of Systems and Software. DOI: 10.1016/j.jss.2020.110574 arXiv
- [Conference] Spieker, H., Gotlieb, A., & Mossige, M. (2019). Rotational Diversity in Multi-Cycle Assignment Problems. In Proceedings of the AAAI Conference on Artificial Intelligence, 33(01), pp. 7724-7731. DOI: 10.1609/aaai.v33i01.33017724 PDF arXiv
- [Conference] Gotlieb, A., Marijan, D., & Spieker, H. (2018). Stratified Constructive Disjunction and Negation in Constraint Programming. In International Conference on Tools with Artificial Intelligence. DOI: 10.1109/ICTAI.2018.00026 PDF arXiv
- [Workshop] Spieker, H., & Gotlieb, A. (2018). Towards Hybrid Constraint Solving with Reinforcement Learning and Constraint-Based Local Search. In Data Science meets Optimization Workshop at Federated Artificial Intelligence Meeting. PDF
- [Conference] Mossige, M., Gotlieb, A., Spieker, H., Meling, H., & Carlsson, M. (2017). Time-Aware Test Case Execution Scheduling for Cyber-Physical Systems. In Proceedings of the 23rd International Conference on Principles and Practice of Constraint Programming (Vol. 10416, pp. 386–404). DOI: 10.1007/978-3-319-66158-2_25 PDF arXiv
- [Conference] Spieker, H., Gotlieb, A., Marijan, D., & Mossige, M. (2017). Reinforcement Learning for Automatic Test Case Prioritization and Selection in Continuous Integration. In Proceedings of 26th International Symposium on Software Testing and Analysis (ISSTA’17) (pp. 12–22). DOI: 10.1145/3092703.3092709 PDF arXiv
- [Journal] Spieker, H., Hagg, A., Gaier, A., Meilinger, S., & Asteroth, A. (2017). Multi-stage evolution of single- and multi-objective MCLP: Successive placement of charging stations. Soft Computing (Vol. 21, Issue 17, pp. 4859-4872). DOI: 10.1007/s00500-016-2374-9 PDF
- [Conference] Spieker, H., Hagg, A., Asteroth, A., Meilinger, S., Jacobs, V., & Oslislo, A. (2015). Successive evolution of charging station placement. In 2015 International Symposium on Innovations in Intelligent SysTems and Applications (INISTA). DOI: 10.1109/INISTA.2015.7276733 PDF
I am proud to be or have been a program committee member and reviewer of
2019 IEEE AI Testing 2019.
I am a member of the associate editorial board of Applied AI Letters.
It was a great pleasure to have been a co-organizer of the NordConsNet Workshop 2019.
If you want to check out what other great people (and especially bachelor and master students in Germany) are doing, check out the: German InformatiCup. I’m helping out in the jury, but the impressive results stem from the student groups!
Please feel free to contact me by using the contact information in the sidebar. You can also find more information on my Simula Homepage.