Legends of Data & AI: Just Machine Learning - Institute for Experiential AI

Legends of Data & AI: Just Machine Learning

Tina Eliassi-Rad, hosted by Usama Fayyad


January 21, 2022

Legends of Data & AI is a monthly podcast hosted by Usama Fayyad. 
Each episode includes inspiring and actionable data and artificial intelligence insights from global leaders across industries.

In this episode, Usama Fayyad speaks with Tina Eliassi-Rad, Professor and Core Member of the Institute for Experiential AI & Network Sciences @ Northeastern University.

Tina discusses situations where machine learning and data science were key ingredients to success, machine learnings deep data dependence, and the origins of Just ML and what the project is working to do.  



Tina Eliassi-Rad is a professor of computer science at Northeastern University. She is also a core faculty member at Northeastern’s Network Science Institute and the Institute for Experiential AI. In addition, she is an external faculty member at the Santa Fe Institute and the Vermont Complex Systems Center. Prior to joining Northeastern, Tina was an associate professor of computer science at Rutgers University. Before that, she was a Member of Technical Staff and Principal Investigator at Lawrence Livermore National Laboratory.

Tina earned her doctorate in computer sciences (with a minor in mathematical statistics) at the University of Wisconsin-Madison. Her research is at the intersection of data mining, machine learning, and network science. She has over 100 peer-reviewed publications (including a few Best Paper and Best Paper Runner-Up Awards) and has given over 200 invited talks and 14 tutorials.

Tina’s 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 (e.g., IBM System G Graph Analytics) and as open-source software (e.g., Stanford Network Analysis Project).

In 2010, Tina received an Outstanding Mentor Award from the Office of Science at the U.S. Department of Energy. In 2019, she became a Fellow of the Institute for Scientific Interchange Foundation in Turin, Italy. Tina was named one of the 100 Brilliant Women in AI Ethics for 2021.