1. My research interests lie in the fields of Healthcare Informatics, Distributed Systems including Internet of Things (IoT), Peer-to-Peer (P2P) computing, Cloud computing, and Semantic Web technologies. I focus on applying artificial intelligence and semantic web technologies to (distributed) systems to enable automatic and effective management and defense of these systems. The results of my research can be applied to solve data management and security issues in electronic-healthcare, disaster management, and social networking. Below, I provide brief descriptions for some of my ongoing research projects.

Personalized Smart and Connected Health

  1. This NSF funded project proposes a proactive diabetes self-care mobile platform based on the unique socio-economic, cultural, and geographical status of American Indian (AI) patients. The mobile platform connects AI diabetic patients to their medical devices, healthcare team and similar patients, and offers personalized prediction, recommendation, and social networking regarding diabetes care. It transforms diabetes management from the traditional reactive and hospital-centered care to preventive, proactive, evidence-based, and person-centered care.
    More information about the project can be found at the project website.

Human-Centric Internet of Things

  1. This project aims to create an infrastructure for Human-Centric Internet of Things (HIoT). Nowadays, we are noticing a more human-centric category of IoT activity heavily used and permeating the everyday life of human beings. Objects such as smartphones, tablets, and all kinds of wearable devices (e.g., wrist band, smart watches, and smart glasses) are connecting people with other people or devices directly or indirectly through various links. In this project, we are focusing on this type of IoT activity and interested in providing a human-centric infrastructure where systems encourage human involvement and be proactive and adaptive to human needs. The proposed HIoT computing infrastructure goes beyond previous efforts in device-centric IoT and opens up exciting avenues in a more scalable, sustainable and human-centric IoT.

Trust and Privacy Management in Mobile Social Networks

  1. Mobile social networking does enable social networking anytime and anywhere, but it also leads to various issues regarding how to protect and trust data in the social network. There are some fundamental questions to be answered: What are the trust and privacy issues in new context-rich mobile social networks? How can we help people better manage, protect and more clearly understand the information they are sharing and with whom they are sharing with? How much can the information and people in the social network be trusted? How can all of these issues be solved in an acceptable, trustworthy, open, and scalable manner? Despite the continuing advances in mobile computing and social computing, these questions have yet to be addressed. We have been working on several projects to address these challenging questions. In particular, we apply machine learning and semantic web techniques to social networking to enable intelligent privacy and trust recommendation and configuration.

Enterprise Knowledge Discovery and Management

  1. One of the greatest challenges of an enterprise is to ensure that their employees and customers are provided with the right information in a timely fashion. For this purpose, modern organizations operate a wide range of information support systems. It is often the case that relevant information is scattered over the internet and/or maintained on disparate systems, buried in large amount of noisy data, and in heterogeneous formats, thereby complicating the access to reusable knowledge and extending the response time. To address these challenges, we are working on efficient knowledge management module to transform the mountain of data into usable knowledge. Our model integrates rich semantics, advanced search, natural language processing with data mining and machine learning technologies. The goal of this project is to make fundamental contributions towards realizing a usable, intelligent, and effective knowledge management framework.

Large-scale Information System

  1. Distributed information sharing is a vital part of a distributed community. An efficient data sharing infrastructure is crucial to make the distributed information available to users in a timely and reliable manner. However, data sharing in large-scale communities is very challenging due to the potential large amounts of data, diverseness, distributed arrangement, and dynamic nature. Moreover, it also is challenging to integrate information originating from different domains with heterogeneous representative data formats. Furthermore, in a community network, disconnections and data failures become the rule rather than exceptions. The aforementioned challenges compel us to investigate mechanisms on how to manage, i.e., store, retrieve, explore, and analyze this abundance of community data. To address these problems and overcome the limitation of existing work, we integrate semantic web technologies, graph theory, and peer-to-peer based distributed commuting with data integration mechanisms to develop effective information interoperability networks.