My work centers on developing innovative algorithms for community detection in network data and conducting inference on networks, with attention to equity and fairness in network structures and algorithms. This interdisciplinary approach allows me to explore and uncover the intricate patterns and structures within complex systems. I focus on developing novel preprocessing methods, validation frameworks, and interpretable algorithms for diverse applications. My research can be applied to any system with entities and relationships among them, making it versatile across various fields and disciplines.

• Postdoc in  Data Science, 2019-2021, University of Virginia
• PhD in Statistics, 2019, Kansas State University
• BS in Industrial Engineering, 2006, Amirkabir University of Technology (Tehran Polytechnic), Tehran, Iran

• Moradi-Jamei, B., Kramer B., Santiago Calder ́on, J., Korkmaz, G., 2021. “Community Formation and Detection on GitHub Collaboration Networks”. IEEE/ACM ASONAM 2021, DOI: https://doi.org/10.1145/3487351.3488278
• Moradi-Jamei, B., Shakeri, H., Poggi-Corradini, P., Higgins, M., 2020. “A new method for quantifying network cyclic structure to improve community detection”. Statistical Mechanics and its Applications, Physica A, DOI: 10.1016/j.physa.2020.125116
• Sam Feuer , Wyatt Hopkins, David Rollo, John Wright, Dinesh Sharma, Moradi-Jamei, B. “A Reciprocity-Based Method for Improving Community Detection on Directed Friendship Networks”, 2022. Proceedings of IEEE MIT/URTC, paper Id: URTC2022-60.