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A list of all the posts and pages found on the site. For you robots out there, there is an XML version available for digesting as well.
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Blog Post number 4
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Blog Post number 1
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portfolio
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publications
Learning Longitudinal Stress Dynamics from Irregular Self-Reports via Time Embeddings
Published in ACII 2025 - Canberra, 2025
The widespread adoption of mobile and wearable sensing technologies has enabled continuous and personalized monitoring of affect, mood disorders, and stress. When combined with ecological self-report questionnaires, these systems offer a powerful opportunity to explore longitudinal modeling of human behaviors. However, challenges arise from missing data and the irregular timing of self-reports, which make challenging the prediction of human states and behaviors. In this study, we investigate the use of time embeddings to capture time dependencies within sequences of Ecological Momentary Assessments (EMA). We introduce a novel time embedding method, Ema2Vec, designed to effectively handle irregularly spaced self-reports, and evaluate it on a new task of longitudinal stress prediction. Our method outperforms standard stress prediction baselines that rely on fixed-size daily windows, as well as models trained directly on longitudinal sequences without time-aware representations. These findings emphasize the importance of incorporating time embeddings when modeling irregularly sampled longitudinal data.
talks
teaching
Doctoral Teaching Assistant
Undergraduate course, Sorbonne University, Engineering Department, 1900
[2022 - 2025] Teaching 1st and 2nd year analog & digital electronics, C programming, and Signal Processing