Tina Eliassi-Rad

Tina Eliassi-Rad is Professor and the Inaugural Joseph E. Aoun Chair at Khoury College of Computer Sciences, Northeastern University in Boston. She serves as Core Faculty at the Network Science Institute and holds External Faculty positions at both the Santa Fe Institute and Vermont Complex Systems Institute. Additionally, she maintains Affiliated Faculty status across six Northeastern University institutes including the NULab for Digital Humanities and Computational Social Science, Global Resilience Institute, Cybersecurity and Privacy Institute, Institute for Experiential AI, and Internet Democracy Initiative.

Her research spans:

• Data Mining & Machine Learning
• Network Science & Complex Systems
• Artificial Intelligence & Society

She leads two major research initiatives: Trustworthy Network Science, which addresses explainability, transparency, stability, and robustness in network science ML algorithms; and Just Machine Learning, which examines broader complex systems where ML operates to understand and mitigate risks. Her work bridges theoretical foundations with societal applications.

Dr. Eliassi-Rad's publication record demonstrates consistent focus on applying network science to critical societal challenges. Her recent research examines pandemic mobility patterns and cybersecurity threats using network-based approaches that combine epidemiological modeling with network analysis techniques.

She actively mentors doctoral students through her RADLAB research group, currently advising PhD candidates Wan He (Network Science) and David Liu (Computer Science), along with PhD students Zohair Shafi and Samantha Dies (Computer Science). Her research has secured funding from prestigious organizations including the National Science Foundation, Department of Defense, Defense Advanced Research Projects Agency, Army Research Lab, and others.

As leader of RADLAB, she directs research at the intersection of data science, network analysis, and societal impact, with particular emphasis on ensuring that technical advances in AI and network science serve societal needs responsibly and equitably.