Eni Mustafaraj is a data scientist who studies web-based sociotechnical systems. She implements new ways to collect the digital traces that users generate while interacting with these systems and invents new techniques for gaining meaningful insights from these traces. Such insights can be used to improve existing systems or build new ones to better serve user needs. Her current research project is about understanding what criteria users apply to assess the credibility of online sources. The goal is to design machine learning algorithms to extract credibility signals from various web platforms and use them to advance web literacy by building platforms that make it easy to access and understand these credibility signals.
During 2008-2014, she collaborated with Prof. Takis Metaxas, Samantha Finn '12 and a large number of Wellesley students in studying how rumors or other kinds of information spread in social networks like Twitter. Their research led to the creation of TwitterTrails, an online tool for reporters and everyone else interested in the origin and spread of false or true information. Since 2013, she has also been studying sociotechnical systems that enable online learning at a large scale. In one of these projects, she has been collaborating with Prof. Franklyn Turbak, Maja Svanberg '18, and Isabelle Li '21 to understand how people learn to program in the MIT App Inventor online platform, which has millions of users. They have been using data analysis, visualization, and machine learning to model learners and their learning.
Prof. Mustafaraj loves the Python programming language and teaches CS111 Computer Programming and Problem Solving, one of the most fun and challenging intro courses at Wellesley. She teaches two data science related courses: CS 234 Data, Analytics, and Visualization and CS 315 Data and Text Mining on the Web, in which her students work on real-world projects closely related to her research and learn how to use data science to make a social impact. Finally, she also teaches CS 232 Artificial Intelligence, the field that drew her to study computer science. She aspires to get more women of all backgrounds involved in computer science and more broadly in STEM fields, so that they can shape a more just and equal technological future for our societies, a recurrent theme in all her teaching.
Visit her website for more information on her research, teaching, and other interests.
