Research on data collection and collaboration from multiple diverse sources in the pharmaceutical vertical

Enhancement of technology and computer science has helped researchers in multiple fields and industries, from health care to automotive industry. Smoking is one of the habits that could harm humans dramatically. Lung cancer, heart attack is just some of the diseases that come with smoking. A large number of people strive to quit smoking each year by various methods, but not all of them are successful. In this research, we try to study what are the reasons that tempt people who quit smoking to smoke again.

Visual Analytics for Financial Risk Year Two

The second project also relates to the application of VA to financial systemic risk analytics. The report on the NSF-funded workshop on Next-Generation Community for Financial Cyberinfrastructure for Managing Systemic Risk identifies the need to develop robust simulations and computational models in order to manage systemic risk. Many of these are in the form of financial network analyses which combine network analytics with data visualization.

Big Data Research for Open Source Applications

Big data is a collection of data sets so large and complex that it becomes difficult to process using onhand database management tools or traditional data processing applications. The challenges include capture, curation, storage, search, sharing, transfer, analysis, and visualization. In this internship, we analyze a real-world big data set(s) to make sensible inferences by taking into account a selected range of criteria. A number of methods and algorithms are investigated, evaluated and evolved to advance the development of specialized tools and processes.

Big Data Research for Open Source Applications

Big data is a collection of data sets so large and complex that it becomes difficult to process using on-hand database management tools or traditional data processing applications. The challenges include capture, curation, storage, search, sharing, transfer, analysis, and visualization. In this internship, we analyze a real-world big data set(s) to make sensible inferences by taking into account a selected range of criteria. A number of methods and algorithms are investigated, evaluated and evolved to advance the development of specialized tools and processes.