Sergio Escalera obtained the 2008 best Thesis award at UAB. He leads the Human Pose Recovery and Behavior Analysis Group. He is Full Professor at the Department of Mathematics and Informatics, Universitat de Barcelona. He is an adjunct professor at UOC, Aalborg University, and Dalhousie University. He is member of the Computer Vision Center. He is series editor of The Springer Series on Challenges in Machine Learning. He is vice-president of ChaLearn Challenges in Machine Learning, leading ChaLearn Looking at People events. He is co-creator of Codalab open source platform for challenges organization. He is Fellow of the ELLIS European Laboratory for Learning and Intelligent Systems, and Chair of IAPR TC-12: Multimedia and visual information systems. He has different patents and registered models. He has published more than 300 research papers. He received a CVPR best paper award nominee and a CVPR outstanding reviewer award. He has been General co-Chair of FG20.

Research interests

Affective computing, computer vision, and machine learning, with special interest in characterizing people: personality and psychological profile computing. The main objective of his research is to reproduce from a computational perspective the human visual system so that intelligent systems can, ethically, provide with automatic interpretations of human behaviour from visual data coming from non-invasive environments/setups. This research is grounded on multi-disciplinarity, combining knowledge from computing, mathematics, artificial intelligence (computer vision and machine learning) and psychology to translate psychological theories into computational models. As a result he created pioneer models, mainly based on deep learning, that allow to detect and analyse people in visual data: from human posture estimation, facial expression analysis, action/gesture/interaction recognition, to the automatic detection of feelings and personality profiles.

Keywords

Computer Vision, Machine Learning, Human Posture Analysis, Human behavior Analysis, Action and Gesture Recognition, Convolutional Neural Networks, Emotion Recognition, Affective Computing, Personality Computing