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:
Component Analysis (CA): CA methods (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 interested in 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 http://ca.cs.cmu.edu.
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 interfacing. I co-lead the Human Sensing Lab at CMU, for more information see http://www.humansensing.cs.cmu.edu.
Face Analysis: Developing algorithms for real-time face tracking, recognition, and expression/emotion analysis.