Interested in visiting the Human Sensing Lab  or Component Analysis Lab  to work on machine learning and/or computer vision? Contact me.

 

Fernando De la Torre received his B.Sc. degree in Telecommunications, as well as his M.Sc. and Ph. D degrees in Electronic Engineering from La Salle School of Engineering at  Ramon Llull University, Barcelona, Spain in  1994, 1996, and 2002, respectively.  In 1997 and 2000, he became Assistant and Associate Professor in the Department of  Communications and Signal Theory in Enginyeria La Salle. During his Ph. D, he was visiting researcher at Queen Mary and Westfield College (University of London), Institute of Advanced Computer Studies (University of Maryland), Xerox Palo Alto Research Center and Brown University.  In 2002, he was a post  doctoral researcher  at Brown university (Providence, RI) and Gatsby Neuroscience Unit (London).  Since 2005, he joined the Robotics Institute at Carnegie Mellon University as Research Assistant Professor. His research interests are in the fields of Computer Vision and Machine Learning. Currently,  he is directing the Component Analysis Lab and co-directing  the Human Sensing Lab. Dr. De la Torre has co-organized the first workshop on  component analysis methods for modeling, classification and clustering problems in computer vision  in conjunction with CVPR’07, and the workshop on  human sensing from video in conjunction with CVPR’06.  He has also given several tutorials at international conferences on the use and extensions of component analysis methods.

 

Dr. De la Torre's research interests include machine learning, signal processing and computer vision, with a focus on understanding human behavior from multimodal sensors (e.g. video, body sensors). I am particularly interested in three main topics:

·         Human Sensing: Modeling and understanding human behavior from sensory data (e.g. video, motion capture, audio). This work is motivated by applications in the fields of human health, computer graphics, machine vision, biometrics, and human-machine interface. I co-lead the human sensing lab at CMU, for more information see Human Sensing Lab

·         Component Analysis (CA): CA (e.g. kernel PCA, Normalized Cuts, Multidimensional Scaling) are a set of algebraic techniques that decompose a signal into relevant components for classification, clustering, modeling, or visualization. I am keen on using CA methods to efficiently and robustly learn models from large amounts of high dimensional data. The theoretical focus of my work is to develop a unification theory for many component analysis methods. I lead the component analysis lab at CMU, which can be found at Component Analysis Lab

·         Face Analysis: Developing algorithms for real-time face tracking, recognition, and expression/emotion analysis