Faculty and students at the University of Miami are involved with numerous novel projects that benefit from the use of big data
Across the University of Miami’s three campuses, faculty and students are immersed in research aimed at providing shape and solutions to some of the world’s most pressing issues.
Helping to solve some of the riddles is the use of data science, or big batches of information that are filtered through high performance computers that aid researchers in their efforts to decipher complex formulas and create predictive applications.
Electrical and computer engineering professors Kamal Premaratne and Manohar Murthi, along with their interdisciplinary U-Link team, which includes associate professor of computer science Stefan Wuchty, are investigating extremist groups to determine why some people are enticed to align themselves with these groups to commit violence and to spread hate speech. They are using data from the social networks Facebook and Twitter, as well as their own network science algorithms and machine learning techniques to track how this content spreads, Murthi said.
“We are trying to understand the process by which extremist groups propagate. Why do they resonate with some and not others? And how do they spread on social networks?” Murthi said. His collaborator, Premaratne added: “Our challenge for data science is to scale the algorithm so that it does not fall apart because of these large-scale data sets.”
Professor of computer engineering Mei-Ling Shyu is working on several projects that utilize data science to solve real-world problems. The first, which she is working on with graduate student Saad Sadiq, aims to detect fake news by capturing the complex hidden relationships in natural language. Their machine learning method uses data from fact-check websites and Google’s 100 billion-word dataset to identify satire, sarcasm, and purposefully misleading content. The program has even garnered awards in an international fake news competition, Sadiq said. Machine learning programs train the computer to “learn” how to classify objects based on certain traits; the more examples they are given, the better the program works.
Shyu is also working on a program to help first responders react to natural disasters quicker. The program uses deep learning, a more complex version of machine learning, to crawl dozens of websites (especially social media sites) and cross reference posts with GPS data simultaneously for information about a natural disaster, so that responders can locate the worst areas of destruction as quickly as possible. “In any disaster, we need a way to aggregate the data…and the question is how we utilize all this information on the internet, to provide some kind of situation awareness for this?” she said. “[With this technology] you would have an automated process to show this information to the necessary people at the right time. We could respond more quickly because we could get this information more immediately.”
Shyu and psychology professor Daniel Messinger are collaborating to develop a more objective way to identify children with autism. Currently, psychologists and psychiatrists use a list of symptoms and characteristics to diagnose children on the autism spectrum, however, Messinger and Shyu are working to develop a computer program that would use classroom videos to predict the likelihood of autism based on certain characteristics found in children.
“We are trying to use machine learning methods to assess them electronically, to automate the process,” Shyu said.
