Konstantinos Roumpas

Athens, Greece

I work at the intersection of human–computer interaction and applied machine learning. I started out in physics, and that still shapes how I think about behaviour — I treat eye movements, gaze, and blinks as measurable signals you can model and learn from.

At Frontier Innovations and through my PhD at the University of Patras, I design and evaluate adaptive systems that draw on multimodal data — eye-tracking, EEG, and behavioural signals — to understand cognitive load, attention, and engagement. My published work ranges from physics-informed neural networks for gaze prediction to telling voluntary blinks apart from involuntary ones, alongside AI-assisted tools that help educators build their own courses.

I’m comfortable across the whole arc of a project: experimental design, data collection, modelling in Python and PyTorch, and the UX evaluation that tells you whether any of it actually helped. Alongside the research, I coordinate EU-funded (Horizon) projects and mentor master’s students through study design, analysis, and publication.

If you’re working on gaze, cognitive load, or human-centred AI — or just want to talk shop — feel free to get in touch!

selected publications

Modelling Eye Mechanics Using Eye-tracking Glasses and Deep Learning

Konstantinos Roumpas and Michalis Xenos

In 2026 4th Cognitive Models and Artificial Intelligence Conference (AICCONF), Apr 2026

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Physics-Informed Neural Networks (PINNs) have gathered a lot of attention in the past years, as they have shown promising results in Physics, Chemistry, and other science-related projects. The application of such networks has also been explored in Human-Computer Interaction (HCI), and there is a lot of interest around eye-tracking (ET), as the partial differential equations (PDEs) that describe their movement are simplistic and can be easily applied in a PINN. Past research showed that with wearable ET devices, the PDEs that were described showed better results; however, there was no further study on them. Also, the potential of exploring different types of deep learning neural networks was discussed, although it was not elaborated further. In this work, we aim to apply Graph Neural Networks, Temporal Convolutional Networks, and their PINN-hybrid counterparts to explore if higher accuracy can be achieved in predicting a user’s gaze. The results showcased that the pure PINN is the best solution to tackling this problem, with the other neural networks falling shortly behind it. This is a significant finding for HCI research, as it showcases the most effective method for predicting a user’s gaze using ET data and deep learning, while also providing insight into the mechanics of eye movement itself.
Distinguishing Voluntary and Involuntary Blinks Through Eye Openness Metrics

Konstantinos Roumpas, Dimosthenis Minas, Alberto Calvo-Cordoba, and 1 more author

In Proceedings of the 3rd International Conference of the ACM Greek SIGCHI Chapter, Sep 2025

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Understanding and characterizing blinks can contribute to developing applications in human-computer interaction, fatigue monitoring, and medical diagnosis. This study involved the collection and analysis of eye-tracking data from 44 subjects to monitor voluntary and involuntary blinks. Using eye-openness metrics, our work builds on previous research that uses the same metric to monitor blinks. Blink duration thresholds were obtained from past studies involving eye images and were categorized as voluntary, involuntary, or invalid. In addition, a neural network model was developed and trained to classify blinks as voluntary or involuntary. The model achieved 92% accuracy on the test set, demonstrating the effectiveness of using eye-openness values for this classification task. This open-access dataset and model can provide a solid foundation for further research and applications in automated blink detection and analysis, and the potential creation of interactive systems that take voluntary blinks as input.
A comparative study of physics-informed and conventional neural networks for predicting on-screen gaze points from eye-tracking data

Konstantinos Roumpas, Eleftheria-Lito Michanetzi, Dimosthenis Minas, and 2 more authors

Expert Systems with Applications, Jul 2025

Abs DOI

Physics-Informed Neural Networks (PINNs) take advantage of physical constraints to improve the predictive performance of neural networks. They have demonstrated remarkable success in physics, chemistry, biology, and medicine, sparking interest in their potential applications in other fields, such as Human-Computer Interaction (HCI). Despite their versatility, PINNs are underutilized in certain disciplines due to the challenge of defining Partial Differential Equations (PDEs) that adequately constrain specific problems. This study investigates the application of PINNs to predict fixations captured by an eye-tracking device, employing equations that model head and eye movements. The data for this study were collected during an experiment involving an adaptive user interface for pilots, encompassing a variety of fixation points and rapid glances. The results establish PINNs as a superior method, achieving a mean absolute error (MAE) of 0.61 for horizontal predictions (X) and 0.35 for vertical predictions (Y), significantly outperforming the conventional neural network (NN). Additionally, the PINN demonstrated stronger predictive accuracy for vertical gaze dynamics, as evidenced by an RY2 of 0.91 compared to 0.85 for the NN. These findings underscore the robustness of the PINN approach in modeling gaze points with enhanced precision and reliability. While conventional neural networks perform adequately, the added benefit of physical constraints in PINNs encourages further exploration of their application in non-natural science domains. These promising results point toward the potential of PINNs in creating more adaptive and context-aware systems for various HCI applications.