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Current projects
Depression Assesment

This project aims to compute quantitative behavioral measures related to depression severity from facial expression, body gesture, and vocal prosody in clinical interviews.
Facial Feature Detection

Detecting facial features in images.
Facial Expression Analysis

Automatic facial expression encoding, extraction and recognition, and expression intensity estimation for diverse applications: teleconferencing, human-computer interaction/interface.
Face Recognition

Recognizing people from images and video.
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.

Past projects
Multimodal Diaries

Summarization of daily activity from multimodal data (audio, video, body sensors and computer monitoring)
Temporal Alignment of Human Behavior

Temporal alignment of two or more subjects recorded from heterogeneous sensors is a challenging problem. This project develops statistical techniques for spatio-temporal alignment of multidimensional time series.
Temporal Segmentation of Human Behavior
Deception Detection

Learning facial indicators of deception.
Hot Flash Detection

Machine learning algorithms to detect hot flashes in women using physiological measures.
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.
Camera Assisted Meeting Event Observer

We are developing the Camera Assisted Meeting Event Observer (CAMEO) - a sensory system designed to provide an electronic agent with physical awareness of the real world.
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.
Indoor People Localization

Tracking multiple people in indoor environments with the connectivity of Bluetooth devices.
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.

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