Sergio Escalera obtained the P.h.D. degree on Multi-class visual categorization systems at Computer Vision Center, UAB. He obtained the 2008 best Thesis award on Computer Science at Universitat Autònoma de Barcelona. He is ICREA Academia. He leads the Human Pose Recovery and Behavior Analysis Group at UB, CVC, and the Barcelona Graduate School of Mathematics. He is Full Professor at the Department of Mathematics and Informatics, Universitat de Barcelona. He is an adjunct professor at Universitat Oberta de Catalunya, Aalborg University, and Dalhousie University. He has been visiting professor at TU Delft and Aalborg Universities. He is a member of the Visual and Computational Learning consolidated research group of Catalonia. He is also a member of the Computer Vision Center at UAB. 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 and co-founder of the NeurIPS competition track. He is also Fellow of the European Laboratory for Learning and Intelligent Systems ELLIS working within the Human-centric Machine Learning program, member of the Association for the Advancement of Affective Computing AAAC, the AERFAI Spanish Association on Pattern Recognition, ACIA Catalan Association of Artificial Intelligence, INNS, 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 and participated in the organization of scientific events. He received a CVPR best paper award nominee and a CVPR outstanding reviewer award. He has been guest editor at TPAMI, JMLR, PR, TAC and IJCV, among others. He has been General co-Chair of FG20, Area Chair at NeurIPS, FG, ICCV, and BMVC, and Competition and Demo Chair at FG, NeurIPS, and ECMLPKDD, among others. His research interests include automatic analysis of humans from visual and multi-modal data, with special interest in inclusive, transparent, and fair affective computing and people characterization.