Inês comes Portugal and holds a masters’ degree in Biomedical and Biophysics Engineering from the Faculdade de Ciências da Universidade de Lisboa (Faculty of Sciences of the University of Lisbon). She did a 1 year internship at EMBL Rome in the field of computational visual neuroscience, and now she is a Ph.D. student at the University of Milan (UNIMI), working in the ESR project 8 to identify reliable predictors of human performance in safety critical human-computer interactions, through the use of explainable deep learning techniques.
About her CISC Research she says
The project’s main goal is to develop a framework for multi-modal user performance-state prediction, that can be applied in a variety of human-computer interaction (HCI) scenarios, using explainable deep-learning techniques. The first step is to collect data from experiments in which different performance-related states will be evoked and labelled during execution of a HCI task, while multiple physiological signals are being monitored.
These experiments will be performed in collaboration with a research project at the Irish Manufacturing Research centre (IMR) that studies the use of telerobots to perform complex tasks requiring fine manipulation in medical device manufacturing environments, using a medical device manufacturer’s highly precise assembly task requiring the user to handle razor blades as the use-case. The developed teleoperated robot cell and the medical device assembly task will serve as the HCI scenario to evaluate and predict the user performance-related state.
About her participation at ESREL2022 she says
I am very grateful to have had the opportunity to attend the ESREL 2022 conference that took place last week, both as a speaker and as part of the amazing staff and organization team.
In collaboration with Dr. Ernesto Damiani and Dr. Gabriele Gianini from Sesar Lab at Unimi, we shared the full paper on Neuro-Symbolic AI for Sensor-based Human Performance Prediction, a topic closely linked to my PhD framed within the CISC project, and the result of my exploration of possible AI solutions to the problem of human performance prediction in safety-critical contexts.
It was a pleasure to learn and share knowledge on reliability, safety and human factors with both academia and industry!
ESREL PAPER: “Neuro-Symbolic AI for Sensor-based Human Performance Prediction: System Architectures and Applications“