Cristian Sminchisescu is a Professor in the Department of Mathematics, Faculty of Engineering at Lund University, working in computer vision and machine learning. He has obtained a doctorate in computer science and applied mathematics with focus on imaging, vision and robotics at INRIA, France, under an Eiffel excellence doctoral fellowship of the French Ministry of Foregin Affairs, and has done postdoctoral research in the Artificial intelligence Laboratory at the University of Toronto. He holds a Professor equivalent title at the Romanian Academy and a Professor rank, status appointment at Toronto, and advises research at both institutions. During 2004-07, he has been a Faculty member at the Toyota Technological Institute, a philanthropically endowed computer science institute located at the University of Chicago, and later on the Faculty of the Institute for Numerical Simulation in the Mathematics Department at Bonn University. Cristian Sminchisescu is a member in the program committees of the main conferences in computer vision and machine learning (CVPR, ICCV, ECCV, ICML, NIPS), an Area Chair for ICCV, ACCV and CVPR during 2007-15, a Program Chair for ECCV 2018, and a member of the Editorial Board (Associate Editor) of IEEE Transactions for Pattern Analysis and Machine Intelligence (PAMI) and the International Journal of Computer Vision (IJCV). He has offered tutorials on 3d tracking, recognition and optimization at ICCV and CVPR, the Chicago Machine Learning Summer School, the AEFRAI Vision School in Barcelona, the Computer Vision summer school at ETH in Zurich and Prague, and Visum in Porto. Over time, his work has been funded by the US National Science Foundation, the Romanian Science Foundation, the German Science Foundation, the Swedish Science Foundation, the European Commission under a Marie Curie Excellence Grant, and the European Research Council under an ERC Consolidator Grant. Cristian Sminchisescu’s research goal is to train computers to `see’ and interact with the world seamlessly, as humans do. His research interests are in the area of computer vision (articulated objects, 3d reconstruction, segmentation and recognition) and machine learning (optimization and sampling algorithms, structured prediction and kernel methods). The visual recognition methodology developed in his group was a winner of the PASCAL VOC object segmentation and labeling challenge over the past four editions, 2009 - 2012, as well as state of the art and winner, respectively, in the Reconstruction Meets Recognition Challenge (RMRC) 2013 and 2014. His work on deep learning for graph matching has received the best paper award honorable mention at CVPR 2018.