Seminarios

This presentation will focus on computational behavioral phenotyping, illustrating how machine learning (ML) methods can be leveraged to transform raw complex multimodal health data into objective and interpretable biomarkers. I first present computer vision and ML algorithms that (i) extract digital behavioral biomarkers from video and gamified visuomotor assessments and (ii) combine them for early autism screening. I then describe computational approaches for analyzing egocentric video to characterize the execution of a chocolate cake cooking task, by producing compact symbolic representations of behavioral trajectories that enable alignment and comparison across patients with sensorimotor and executive disorders. Finally, I present a methodology for learning compact and generalizable representations of wearable biosignals to improve the sensitivity to subtle cardiovascular health changes.

[1] Perochon S., Di Martino J.M. et al. A scalable computational approach to assessing response to name in toddlers with autism. J Child Psychol Psychiatr (2021).

[2] Perochon S., Di Martino J.M. et al. A tablet-based game for the assessment of visual motor skills in autistic children. npj Digit. Med. (2023).

[3] Perochon, S., Di Martino, J.M. et al. Early detection of autism using digital behavioral phenotyping. Nat Med. (2023).

[4] Perochon S., Oudre L. Recursive prototyping for computational behavior analysis from egocentric videos. In review at ICPR 2026

[5] Perochon, S. et al. Time-varying representations of longitudinal biosignals using self-supervised learning. NeurIPS 2024 Workshop: Self-Supervised Learning-Theory and Practice.

El objetivo de la charla es presentar dos trabajos, uno junto a Françoise Dal'Bo y Sergio Herrero y otro junto con Françoise. En ellos construimos conjuntos fuertemente estables para el flujo geodésico de ciertas superficies hiperbólicas no compactas que no coinciden con el horociclo estable. De hecho, el conjunto estable consiste en una unión no numerable de horociclos.  El primer trabajo se basa en una idea de Alexandre Bellis para superficies con geodésicas cerradas arbitrariamente cortas (en particular, son superficies de tipo infinito). El segundo vale para cualquier superficie con al menos un cusp. Además si la superficie tiene volumen finito, este fenómeno es abundante. Obtenemos una aplicación a la no expansividad del flujo geodésico: existen una cantidad no numerable de órbitas que se mantienen cerca todo el tiempo.