Ognjen (Oggi) Rudovic

Affective Computing
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  • Postdoctoral Fellow

Hi, I am a Marie Curie Fellow in the Affective Computing Group at Media Lab, working with Rosalind Picard on machine learning for the new generation of affective robots! I have a background in Automatic Control Theory, Computer Vision, Artificial Intelligence and Machine Learning. In 2014, I received a Ph.D. from Imperial College London, UK, where I worked on machine-learning and computer vision models for automated analysis of human facial behavior. These are among the first proposed methods for multi-view facial expression analysis and context-sensitive analysis of dynamics of facial expressions. They address a number of modelling tasks including personalized classification of facial expressions of emotions, and their intensity (as well as intensity of facial expressions of pain). My work mainly builds upon the state-of-the-art data modeling frameworks: Gaussian Processes, Conditional Random Fields and Deep Neural Networks (check my googlescholar).

My current research is focused towards the design of novel and more engaging machine learning paradigms for personalized, context-sensitive and multi-modal sensing (from au… View full description

Hi, I am a Marie Curie Fellow in the Affective Computing Group at Media Lab, working with Rosalind Picard on machine learning for the new generation of affective robots! I have a background in Automatic Control Theory, Computer Vision, Artificial Intelligence and Machine Learning. In 2014, I received a Ph.D. from Imperial College London, UK, where I worked on machine-learning and computer vision models for automated analysis of human facial behavior. These are among the first proposed methods for multi-view facial expression analysis and context-sensitive analysis of dynamics of facial expressions. They address a number of modelling tasks including personalized classification of facial expressions of emotions, and their intensity (as well as intensity of facial expressions of pain). My work mainly builds upon the state-of-the-art data modeling frameworks: Gaussian Processes, Conditional Random Fields and Deep Neural Networks (check my googlescholar).

My current research is focused towards the design of novel and more engaging machine learning paradigms for personalized, context-sensitive and multi-modal sensing (from audio, visual and physiological signals) of human behavior. The aim of these models is to improve the personal medicine and healthcare, as well as the existing wellbeing technologies. The project I am working on at the moment and am particularly passionate about aims to build ‘the engagement component’ for humanoid robots (such as NAO) in order to facilitate educational therapies for children with Autism Spectrum Conditions (ASC), and, in general, to achieve a more socially intelligent human-robot interaction.