AI Colloquium

The Bernoulli Institute AI Colloquium is a regular meeting where faculty and students  can hear and discuss the current research related to the institute's three research themes (Computing and Cognition; Geometry and its Applications; Systems, Data and Society) from inside and outside the University of Groningen. 

Colloquia are scheduled monthly during the teaching blocks, with the exception of December, preferentially on Thursday afternoon. Meetings at the end of the day are followed by the PoCoBo - a post-colloquium borrel.

For external colloquia, there is a limited opportunity to have dinner with the speaker in the evening. If you are interested in this, please contact Harmen or Stephen a few days before the colloquium.

Improving Human-Computer Interaction with Brain-Computer Interfaces

 Maryam Alimardani, VU Amsterdam

Tuesday 10 February 2026, 16:00-17:00

Linnaeusborg 5173.0034

This talk introduces my research in brain-computer interfacing (BCI) and neuroadaptive human-computer interaction. My work leverages wearable sensors (primarily EEG, but also ECG and EDA) to collect neurophysiological data from users engaged in VR and human-robot interaction tasks. The goal of this research is twofold: 1) to investigate how mental states and cognitive performance evolve as users interact with immersive and intelligent systems, particularly in learning and skill-acquisition context, and 2) to develop AI-driven neurofeedback systems capable of decoding users' intentions and mental states in real-time in order to deliver personalized feedback and enhance learning outcome. In this talk, I will present our latest projects on 1) the development of a neuroadaptive framework for robot-assisted language learning, and 2) the design of a BCI system for monitoring cognitive workload in military pilots during VR-based flight training.

Extending Mined Rules for Link Prediction with Schema-based Exceptions

Giacomo Zamprogno, VU Amsterdam

Tuesday 17 February 2026 16:00-17:00

Bernoulliborg  5161.0293

Link Prediction (LP) is the task of expanding information in Knowledge Graphs (KGs) by suggesting new relations between their entities. Mining and applying inference rules for this task can be used, with competitive performances, to maintain transparency with respect to machine learning methods for LP which behave as black-boxes. Yet, these rule-based methods suffer from a lack of expressivity and rarely leverage semantic information of the KG, focusing instead on entity-level triples, leading to inconsistencies when considering the KG schema restrictions.

In this work, we propose to extend rule expressivity with non-monotonicity. In particular, we leverage KG schema information to extend the mined rules with exception cases, in order to make the KG more consistent and semantically accurate. We automatically define exception cases based on the predicted relations, pruning the search space when the rule could produce semantically incorrect facts. We evaluate our approach by analyzing the quality of the materialized triples and the additional computing time required in four KGs of different size and curation level, and we further verify the change in rank-based LP metrics, using and extending rule sets mined by two rule miners. Our results suggest that the semantic quality of mined rules varies depending on the level of curation and schema-complexity of the KG. Further, we show that applying exceptions at single-rule level (thus ignoring between-rule interactions) allows to substantially reduce the number of inconsistent triples in all cases, at the cost of a limited increase in inference time. We finally report that rank-based LP metrics are influenced by the different rule expressivity in a limited way, further suggesting the need for integration with metrics that can take into account the semantic quality of predicted triples.