Machine Learning & Data Analytics

In this paper, we propose to learn appearance measures for patches that are combined using a spring model for addressing the correspondence problem.

We propose a metric learning approach for joint class prediction and pose estimation.

We propose an alternating optimization procedure that estimates lower body orientations and detects groups of interacting people.

We explore ensembles of hierarchical spatio-temporal trees, discovered directly from training data, to model these structures for action recognition.

We develop a new scalable and accurate incremental object detection algorithm, based on several extensions of large-margin embedding (LME)

In this paper, we investigate two representations based on raw player detections (and not tracking) which are immune to missed and false detections.

We present a computational design tool for creating physical characters using tensegrities — networks of rigid and elastic elements that are in static equilibrium.

Player movements in team sports are often complex and highly correlated with both nearby and distant players. A single motion model would require many degrees of freedom to represent the full motion diversity of each player and could be difficult to use in practice. I

We present Mole Madness, a side-scrolling computer game that is built to explore multi-child language use, turn-taking, engagement, and social interaction in a fast-paced speech-operated activity.