People are faced with an overwhelming amount of news, only some of which comes from credible sources and is fact verified. The main idea behind this project is to address such issues. The scope of work includes creating an accessible and efficient method for members of the public to get a news summarization from credible sources with diverse ideologies to provide a variety of perspectives. The methodologies used in this project include NLP, machine learning, deep learning and text mining techniques.

Johnny Gillings Jr., OR PhD Student

Predicting and Deterring Outbreaks of Malaria

 (INFORMS presentation)

The potential of a malaria infection threatens nearly half of the world’s population. While methods exist for controlling the spread of the disease and lessening the severity of infection, the most optimal usage of these methods is still uncertain. Additionally, malaria requires quick intervention to prevent the worst of symptoms, making predictions of the risk of outbreak important. This research aims to utilize machine learning to predict outbreaks of malaria and to determine the most effective application of intervention methods, with the goal of minimizing the burden of malaria and averting an outbreak, when possible.