Here are some projects that I am working on, or have worked on in the past.
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.
Edward Kim and Shruthika Vangala, "Vinereactor: Crowdsourced Spontaneous Facial Expression Data", in International Conference on Multimedia Retrieval, 2016. [ PDF ].
Visit our website here : vinereactor.org
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!
Edward Kim and James Park "1st place winner of RWJF Games to Generate Data Challenge" and $100,000, 2013.
Visit our website here : WHRGroup.org
See our presentation video here:
Our approach to understanding hospital price variability focuses on the holistic view of the environment in which hospitals operate. We focus on patient-level, health-system, and socio-economic factors to provide a robust context to investigate this pricing dilemma. Our fundamental question asks how these factors relate to the hospital pricing variability. We used the Robert Wood Johnson Foundation’s County Health Ranking and Roadmap framework to profile each county in the United States. This framework is based on rates of smoking, obesity, unemployment, being uninsured, as well as gender, race, and income. We chose six common DRGs and calculated the weighted average price based on the number of hospitals and discharges within a county. That weighted average price, combined with the various measures from the RWJF County Health Ranking, was analyzed using linear regression to determine which county-level factors were associated with higher prices. We then provided mathematical models to allow users to predict their cost of a hospitalization for a particular condition based on their geographic location, health and demographic profile. Our analysis showed three main findings.
Edward Kim and James Park "2nd place winner of RWJF Hospital Price Transparency Challenge" and $3,500, 2013.
Visit our website here : WHRGroup.org
Suppose you want to effectively search through millions of images, train an algorithm to perform image and video object recognition, or research the complex patterns and relationships that exist in our visual world. A common and essential component for any of these tasks is a large annotated image dataset. However, obtaining labeled image data is a complex and tedious task that requires methods for annotating and structuring content. Therefore, we developed a comprehensive online tool and data structure, Markup SVG, that simplifies the collection of annotated image data by leveraging state of the art image processing techniques. As the core data structure of our tool, we adopt Scalable Vector Graphics (SVG), an extensible and versatile language built upon XML. Given the extensibility of our framework, we are able to encode low level image features, high level semantics, and further define interactions with the data to assist the user with image annotation. We also demonstrate the ability to merge multiple online and offline datasets into our system in an effort to standardize image collection and its data representation. Lastly, we present our modular design; each component acts as a plug-in to our system. We developed several novel components and algorithms to highlight the possibilities of semi-supervised segmentation and automatic annotation within our proposed framework. Further, our modular design provides the necessary capabilities to incorporate future image features, methods, or algorithms. Our results show that our tool is able to greatly simplify the process of obtaining large annotated image collections in an online collaborative platform.
E.Kim, X.Huang, G.Tan, "Markup SVG - An Online Content Aware Image Abstraction and Annotation Tool", In IEEE Transactions on Multimedia, Vol. 13, Issue 5, Oct. 2011.
E.Kim and X.Huang, "Crowdsourcing Image Segmentation using SVG", In SVG Open, 2011. [ PDF ]
Visit our website here : MarkupSVG.com