Pat Virtue

Pat Virtue is an Assistant Teaching Professor at Carnegie Mellon University's School of Computer Science, affiliated with both the Computer Science Department and Machine Learning Department. His primary role focuses on teaching and curriculum development in areas like Machine Learning, AI, and educational methods. He has taught numerous courses including Introduction to Machine Learning, Generative AI, and AI Scholars. Notably, he contributed to the Caffe deep learning framework, enhancing its capabilities in complex-valued neural networks and MRI-related applications.

Research interests include MRI Fingerprinting, complex-valued deep learning, and improving educational methods in CS. His work bridges computer science and medical imaging, with publications on topics like MRI reconstruction and neural network applications. Awards include the EECS Outstanding Graduate Student Instructor Award from UC Berkeley.

Pat has been actively involved in teaching at both CMU and UC Berkeley, designing courses for diverse audiences, including non-STEM students. He has collaborated on projects such as the Berkeley Advanced Reconstruction Toolbox and ESPIRiT, advancing MRI techniques. His teaching efforts emphasize scalability, with courses like Introduction to Machine Learning reaching hundreds of students annually.