Dr. Rudovic is a Marie Curie Fellow in the Affective Computing group at MIT Media Lab, working with Rosalind Picard on machine learning for the new generation of affective robots. He has a background in Automatic Control Theory, Computer Vision, Artificial Intelligence and Machine Learning. In 2014, he received his PhD from Imperial College London, UK, where he 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). His work mainly builds upon the state-of-the-art data modeling frameworks: Gaussian processes, conditional random fields, and deep neural networks. Recently, he moved to industry to work for Apple Inc.. More information: here and here

After receiving his PhD at the Technical University of Darmstadt, Dr. Würtenberger was responsible for several departments at the BMW Group related to in-car experiences, including human-machine interaction, multimedia, speech recognition, and infotainment systems. He also headed the BMW Group IT Innovation Laboratory (BMW Car IT) before taking over responsibility for the BMW Group E/E Research.

Abstract: “Vehicle Interiors as Sensate Environments – Tactile Robotics and Artificial Intelligence Transforming the Future in-Car Experience“
A vehicle’s interior can be considered a highly interactive sensate environment. With the advent of highly automated driving, even more emphasis will be on this smart space and the corresponding user experience. New interior layouts become possible, with the attention shifting from the driver to the wellbeing and comfort of rider passengers in highly reconfigurable interior layouts. Tactile intelligence in particular will add an exciting new modality and help address challenges of safe human-robot coexistence.