Here are some of the projects that I'm working on, or have worked on in the past with associated multimedia.
Spatiotemporal Sequence Memory for Prediction using Deep Sparse Coding. For our project, we sought to create a predictive vision model using spatiotemporal sequence memories learned from deep sparse coding. This model is implemented using a biologically inspired architecture: one that utilizes sequence memories, lateral inhibition, and top-down feedback in a generative framework.
The Villanova Immersive Studies CAVE, managed by CEET and housed in the Falvey Memorial Hall of Falvey Library, is a four-sided (aka "C-4") CAVE. Its development was supported in part by NSF MRI/ACI grant #1338052. Like all CAVEs, it provides viewers with an immersive experience viewing and interacting with 3D virtual-reality worlds, 3D big-data displays, and 3D models of real-world locations and objects.Learn more at villanova.edu
Although machines are more pervasive in our everyday lives, we are still forced to interact with them through limited communication channels. Our overarching goal is to support new and complex interactions by teaching the computer to interpret the expressions of the user. Towards this goal, we present Vinereactor, a new labeled database for face analysis and affect recognition. Our dataset is one of the first to explore human expression recognition in response to a stimulus video, enabling a new facet of affect analysis research. Furthermore, our dataset is the largest of its kind, nearly a magnitude larger than its closest related work.Learn more at Vinereactor.org
In the field of digital pathology, there is an explosive amount of imaging data being generated. Thus, there is an ever growing need to create assistive or automatic methods to analyze collections of images for screening and classification. Machine learning, specifically deep learning algorithms, developed for digital pathology have the potential to assist in this way. Deep learning architectures have demonstrated great success over existing classification models but require massive amounts of labeled training data that either doesn’t exist or are cost and time prohibitive to obtain. In this project, we present a framework for representing, collecting, validating, and utilizing cytopathology features for improved neural network classification.
wHealth is an interactive storytelling application that can provide insight into a user's willingness to pay for health care, provide insight into how quality information, and compare aggregate rates or perform subgroup analyses, i.e. gender/age/income differences in what factors are most important.
We use gamification techniques such as storytelling, personalization, and immediate feedback to drive user engagement. We are excited to announce that we are the first place winner of the Robert Wood Johnson Foundation Games to Generate Data Challenge and the recipient of $100,000!Learn more at whrgroup.org