Utöver uppsatshandledning undervisar jag framför allt kurser inom AI, språkteknologi, informationssökning och big data:

• BIGDATA: Big Data with NoSQL Databases (kursansvarig)
• MAIO: Managing AI in the Organization (kursansvarig)
• NLP: Natural Language Processing
• ISBI: Internet Search Techniques and Business Intelligence

Utvalda publikationer (2023-)

Randl, K., Pavlopoulos, J., Henriksson, A., Lindgren, T. (2025). Mind the Gap: From Plausible to Valid Self-Explanations in Large Language Models. Machine Learning, 114:120.

Vakili, T., Henriksson, A., Dalianis, H. (2025). Data-Constrained Synthesis of Training Data for De-Identification. In Proceedings of the 63rd Annual Meeting of the Association for Computational Linguistics (ACL 2025).

van der Werff, S.D., van Rooden, S.M., Henriksson, A., Behnke, M., Aghdassi, S.J.S., van Mourik, M.S.M., Naucler, P. (2025). The future of healthcare-associated infection surveillance: Automated surveillance and using the potential of artificial intelligence. Journal of Internal Medicine, 298, pp. 54–77.

Randl, K., Pavlopoulos, J., Henriksson, A., Lindgren, T., Bakagianni, J. (2025). SemEval-2025 Task 9: The Food Hazard Detection Challenge. In Proceedings of the 19th International Workshop on Semantic Evaluation (SemEval 2025).

Vakili, T., Hansson, M., Henriksson, A. (2025). SweClinEval: A Benchmark for Swedish Clinical Natural Language Processing. In Proceedings of NoDaLiDa/Baltic-HLT.

Randl, K., Pavlopoulos, I., Henriksson, A., Lindgren, T. (2025). Evaluating the Reliability of Self-Explanations in Large Language Models. In Proceedings of Discovery Science, LNAI, pp. 36-51.

Wu, Y., Henriksson, A. (2024). Selecting from Multiple Strategies Improves the Foreseeable Reasoning of Tool-Augmented Large Language Models. In Proceedings of ECML-PKDD, pp. 197-212.

Randl, K., Pavlopoulos, I., Henriksson, A., Lindgren, T. (2024). CICLe: Conformal In-Context Learning for Largescale Multi-Class Food Risk Classification. In Findings of the Association for Computational Linguistics: ACL 2024, pp. 7695–7715.

Li, X., Henriksson, A., Duneld, M., Nouri, J., Wu, Y. (2024). Supporting Teaching-to-the-Curriculum by Linking Diagnostic Tests to Curriculum Goals: Using Textbook Content as Context for Retrieval-Augmented Generation with Large Language Models. In Proc. of International Conference on AI in Education (AIED), pp. 118-132.

Vakili, T., Henriksson, A., Dalianis, H. (2024). End-to-End Pseudonymization of Fine-Tuned Clinical BERT Models: Privacy Preservation with Maintained Data Utility. BMC Medical Informatics and Decision Making, 24(1), 162.

Henriksson, A., Pawar, Y., Hedberg, P., Nauclér, P. (2023). Multimodal fine-tuning of clinical language models for predicting COVID-19 outcomes. Artificial Intelligence in Medicine, 146.

Wu, Y., Henriksson, A., Duneld, M., Nouri, J. (2023). Towards Improving the Reliability and Transparency of ChatGPT for Educational Question Answering. In Proceedings of the Eighteenth European Conference on Technology Enhanced Learning (ECTEL).