Natasha Jaques

Affective Computing
  • Research Assistant

PhD candidate working on Affective Machine Learning: the development and application of machine learning and deep learning techniques to problems related to emotion, wellbeing, mental health, and social interaction. Past projects have involved using multi-task learning techniques to personalize machine learning models and improve accuracy in predicting next day stress, happiness and health, as well as improving sequence generation models via deep reinforcement learning. Experienced in traditional machine learning, deep learning, Bayesian methods, and reinforcement learning. 

PhD candidate working on Affective Machine Learning: the development and application of machine learning and deep learning techniques to problems related to emotion, wellbeing, mental health, and social interaction. Past projects have involved using multi-task learning techniques to personalize machine learning models and improve accuracy in predicting next day stress, happiness and health, as well as improving sequence generation models via deep reinforcement learning. Experienced in traditional machine learning, deep learning, Bayesian methods, and reinforcement learning.