Projects
HOME-MATE: HOME-compatible Multimodal Alarm Triggering for Epilepsy Ghent University
This project tackles fundamental research on the technology needed for an automatic epileptic seizure detection system. It uses flexible and stretchable substrates with various sensors, it processes EEG signals and other measurement and it uses the reservoir computing neural network technique. Thsi leads to a fast detection system, tailored to individual patients and fit for a home detection system.
MUST - Multimodal stance-taking in interaction KU Leuven
The function of eye gaze in multimodal interaction management. An interdisciplinary study on speech, music and signed language KU Leuven
Multimodal input & cognition: An experimental study into the effect of text in Simultaneous Interpreting University of Antwerp
The Authoring Side Revisited: Tracking Writing and Reading Processes in Multimodal Technical Communication KU Leuven
Multimodal Hopfield networks: a step towards next-generation AI Ghent University
The field of AI is in constant need for more scalable solutions. One promising option are Hopfield networks, that allow for an analog electronics implementation that is faster and much more energy efficient than modern digital hardware. Unlike traditional feedforward neural networks, these networks also incorporate feedback connections, inspired by the structure of the human brain. Nonetheless, current models are typically limited to unimodal ...
An ancient world of manners. A multimodal approach to politeness theory through Greek documentary papyri Ghent University
This postdoc proposal aims to investigate interpersonal relationships and social interactions in Greco-Roman and Late Antique Egypt (III BCE-VII CE) through Greek documentary papyri. The research will use the linguistic framework of historical politeness, which will be applied in an innovative way, by including a multimodal dimension. In contrast to literary sources, documentary papyri provide a pivotal witness on all social classes, offering ...
Towards precision health by enabling multimodal monitoring in real-life settings using uncertainty based hierarchical and time-dynamic models Ghent University
I will construct a multimodal and dynamic hierarchical sensing framework to tackle the challenges of personalized health monitoring in real-life settings. Multimodal sensing allows me to detect non-physiological symptoms by incorporating context. By fusing behavior modeling with hierarchical anomaly detection using an active learning approach, I will define the optimal moment to gather user feedback for the time dynamic models.