Summary
Reihaneh Rabbany is an Assistant Professor at McGill University and former postdoc at Carnegie Mellon with 11 years of experience researching data mining, machine learning, network science, and graph mining. Her PhD and MSc from the University of Alberta focused on principled methods for comparing and modeling modular structure in complex networks, informing both theoretical advances and practical analyses of real-world systems. She blends rigorous academic research with hands-on mentorship and teaching, having supported programming and data-structures courses earlier in her career. Based in Montreal, she pursues “complex data” problems that bridge network theory and machine learning, often extracting interpretable structure from noisy relational data. A detail not obvious from titles: her work emphasizes quantifying modularity in networks—turning qualitative community patterns into measurable, modelable features useful for downstream ML tasks.
11 years of coding experience
7 years of employment as a software developer
Master’s Degree, Computer Science, Master’s Degree, Computer Science at University of Alberta
Amirkabir University of Technology