Martin Radfar

Martin Radfar is an Assistant Professor in the Department of Computer Science at Stony Brook University, affiliated with the Institute for AI-Driven Discovery and Innovation. He holds a Ph.D. in Machine Learning and Signal Processing from the University of Toronto (2014). His academic roles include teaching courses such as CSE215 (Foundations of Computer Science), CSE351 (Introduction to Data Science), and ISE316 (Introduction to Networking). He has advised PhD student Xuan Xu and MS student Pranavi Meda.

Radfar's research focuses on voice-based human-machine interfaces, auditory scene analysis, Bayesian networks, and cancer drug target prediction using machine learning. His expertise spans signal processing, computational biology, and healthcare applications. Notable contributions include developing the PGMLAB library for Bayesian networks and pioneering work in microRNA target prediction.

His academic achievements include awards such as the NSERC Postdoctoral R&D Fellowship (2014) and the Edward S. Rogers Graduate Scholarship (2008). His teaching emphasizes foundational computer science, data science tools (Python, Jupyter notebooks), and network architecture. He has published widely in venues like IEEE Signal Processing Society journals and ACM Transactions, addressing topics like speech separation, genomic data analysis, and network protocols.