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Fernando De la Torre Frade
Assistant Research Professor

Email address: ftorre@cs.cmu.edu

Mailing address:
Carnegie Mellon University
Robotics Institute
5000 Forbes Avenue
Pittsburgh, PA 15213

For more information, see my personal homepage.

Jump to: Research interests | Keywords | Labs & groups | Projects | Publications

Research interests

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:

Research interest keywords

artificial intelligence, computer vision, data mining, data visualization, gesture recognition, image compression, image processing, information fusion, machine learning, machine understanding of video and human behavior, neural networks, pattern recognition, quality-of-life technology, sensor fusion, and statistics

Current Labs & Groups

Component Analysis - The Component Analysis Lab is devoted to research new learning techniques to encode and decompose a given signal into relevant components for classification, clustering, modeling and visualization.
Face Group - Robust detection, recognition, and tracking of human faces with automated analysis of expressions
Human Identification at a Distance - We are developing and evaluating human identification technologies as part of the Defense Advanced Research Projects Agency (DARPA) sponsored program in Human Identification at a Distance (HumanID).
Human Sensing - The goal of the Human Sensing Lab is to develop new machine learning algorithms to model and understand human behavior from sensory data.

Current Projects [Past projects]

Deception Detection - Learning facial indicators of deception
Depression Assessment - This project aims to compute quantitative behavioral measures related to depression severity from facial expression, body gestures, and vocal prosody in clinical interviews.
Face Recognition - Recognizing people from images and videos.
Facial Expression Analysis - Automatic facial expression encoding, extraction and recognition, and expression intensity estimation for the applications of MPEG4 application: teleconferencing, human-computer interaction/interface.
Facial Feature Detection - Detecting facial features in images.
Feature Selection - Feature selection in component analysis.
Forecasting the Anterior Cruciate Ligament Rupture Patterns - Use of machine learning techniques to predict the injury pattern of the Anterior Cruciate Ligament (ACL) using non-invasive methods.
Hot Flash Detection - Machine learning algorithms to detect hot flashes in women using physiological measures.
Image Alignment - Image alignment with parameterized appearance models.
Indoor People Localization - Tracking multiple people in indoor environments with the connectivity of Bluetooth devices.
Intelligent Diabetes Assistant - We are working to create an intelligent assistant to help patients and clinicians work together to manage diabetes at a personal and social level. This project uses machine learning to predict the effect that patient specific behaviors have on blood glucose.
Learning Optimal Representations - Learning optimal representations for classification, image alignment, visualization and clustering.
Low Dimensional Embeddings - Finding low dimensional embeddings of signals for optimal modeling, classification and clustering.
Multimodal Data Collection - A multimodal database of subjects performing the tasks involved in cooking, captured with several sensors (audio, video, motion capture, accelerometer/gyroscope).
Multimodal Diaries - Summarization of daily activity from multimodal data (audio, video, body sensors and computer monitoring)
Quality of Life Technology Center - QoLT is a unique partnership between Carnegie Mellon and the University of Pittsburgh that brings together a cross-disciplinary team of technologists, clinicians, industry partners, end users, and other stakeholders to create revolutionary technologies that will improve and sustain the quality of life for all people.
Reflective Agents with Distributed Adaptive Reasoning - The focus of the RADAR project is to build a cognitive assistant that embodies machine learning technology that is able to function without requiring expert tuning or specially trained users.
Spatio-Temporal Facial Expression Segmentation - A two-step approach temporally segment facial gestures from video sequences. It can register the rigid and non-rigid motion of the face.
Temporal Segmentation of Human Motion - Temporal segmentation of human motion
Unification of Component Analysis - This project aims to find the fundamental set of equations that unifies all component analysis methods.

Recent publications [View all 37 publications]

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