A new multimodal dataset and architecture combining egocentric vision and physiological signals for in-the-wild emotion and personality recognition, presented at NeurIPS 2025 …
Systematic study of multimodal emotion recognition in natural human-chatbot interactions, evaluating text, acoustic, and behavioral signal fusion strategies. ICMI 2024.
Personality trait recognition from everyday smartphone typing dynamics in naturalistic conditions, using deep learning on keystroke patterns. IEEE Transactions on Affective …
Multimodal affective state prediction from smartphone touch and sensor data in naturalistic conditions, using deep learning fusion. CHI 2022.
Glyph-based visualization technique for representing multimodal affective state data, designed for intuitive perception and scalable display. EuroVis 2020.
Semi-supervised learning for affective state prediction from smartphone touch data, leveraging abundant unlabeled naturalistic data. CHI 2020.
Affective state prediction during mobile learning using wearable biometric sensors and stylus interaction data. EDM 2019.
Multimodal deep learning systems for affective state prediction from smartphone sensors, biometric data, egocentric vision, and physiological signals — enabling real-world …