177 Huntington Avenue
Boston, MA 02115
ATTN: Tina Eliassi-Rad, 1010 - 177
360 Huntington Avenue
Boston, MA 02115
- Data mining
- Machine learning
- Network science
- Ethics and artificial intelligence
- PhD in computer science, University of Wisconsin-Madison
- MS in computer science, University of Illinois at Urbana-Champaign
- BS in computer science, University of Wisconsin-Madison
Tina Eliassi-Rad is a professor of computer science at Northeastern University. She is also a core faculty member at Northeastern University’s Network Science Institute. Prior to joining Northeastern, she was an associate professor of computer science at Rutgers University; and before that she was a technical staff member and principal investigator at Lawrence Livermore National Laboratory.
She earned her PhD in computer science with a minor in mathematical statistics at the University of Wisconsin-Madison. Her research is rooted in data mining and machine learning, and her work spans theory, algorithms, and applications of big data from networked representations of physical and social phenomena. She has more than 80 peer-reviewed publications, including multiple best paper and best paper runner-up awards, and has given more than 190 invited talks and 13 tutorials.
Her work has been applied to personalized search on the World-Wide-Web, statistical indices of large-scale scientific simulation data, fraud detection, mobile ad targeting, cyber situational awareness, and ethics in machine learning. Her algorithms have been incorporated into systems used by the government and industry, such as the IBM System G Graph Analytics, and open-source software, such as the Stanford Network Analysis Project.
In 2017, she served as the program co-chair for the ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, which is the premier conference on data mining. She also served as the program co-chair for the International Conference on Network Science, which is the premier conference on network science. In 2010, she received an Outstanding Mentor Award from the Office of Science at the U.S. Department of Energy.