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.

Miniaturized Robot Swarms for Sensing and Inspection Applications

 Bahar Haghighat, ENTEG

Tuesday 11 November 2025, 16:00-17:00

Energy Academy 5159.0114

Over the past two decades, the field of swarm robotics has been very successful at developing ground, aerial, and aquatic robotic swarms that demonstrate a variety of collective behaviors. A main challenge that remains unmet, however, is deployment of robot swarms to deliver applications in uncontrolled real-world settings. Sensing, surveying, and inspection applications stand to largely benefit from deployment of robot swarms. Swarms of miniaturized robots will enable accessing environments that are beyond the reach of typical platforms, realizing extensive networks of mobile sensors. In this talk, I will present a summary of my research work on mechatronic design, modeling, and control of miniaturized robot swarms. I will cover two example miniaturized swarms of aquatic and surface robots and provide an overview of future work towards achieving the aforementioned vision.

User-centric Bayesian Network Construction for Hybrid Intelligence

Annet Onnes, Utrecht University

Tuesday 18 November 2025 16:00-17:00

Linnaeusborg 5173.0045

Bayesian networks (BNs) are known as interpretable models that allow combining uncertain and rule-based knowledge in one representation, by compactly representing a probability distribution using a graph. The manual construction of these types of models, however, requires knowledge from both domain experts, as well as insights from experts in BNs as models. In this talk, I will discuss several of my research projects that have the shared aim to increase the input of domain experts and decrease the liaising required by BN experts in manual BN construction. The resulting construction methodology allows domain experts to construct BNs, with minimal help from modelling experts, to help monitoring AI systems in Hybrid Intelligence settings, where human teammates are the domain experts modelling what desirable collaborative behaviour looks like. By standardising construction methods and relying on knowledge from users, models can be constructed and adjusted to monitor in varying contexts.