Office: 275 Barrows Hall, Orono, ME 04469
Email: chaofan [dot] chen [at] maine [dot] edu
Google Scholar: https://scholar.google.com/citations?user=pJ0vTRUAAAAJ&hl=en
I am an Assistant Professor at the University of Maine.
My research centers on explainable artificial intelligence (AI) and interpretable machine learning. My work aims to design AI and machine learning models that are not only accurate but also understandable to humans—especially in high-stakes domains where trust and transparency are critical.
I am a recipient of the prestigious National Science Foundation (NSF) CAREER Award for the project "CAREER: Opening the Black Box: Advancing Interpretable Machine Learning for Computer Vision" (Award #2442039, $584,034), where I conduct research to develop innovative technologies to allow machine learning-based computer vision models to explain their reasoning processes to human users, and to allow human users to interact with those models to correct the models' mistakes.
I am a co-Principal Investigator (co-PI) on the NSF-funded project "RII Track-2 FEC: Explainable and Adaptable Artificial Intelligence for Advanced Manufacturing" (Award #2218063, $6M), where I lead efforts to build interpretable AI systems to support innovation in manufacturing.
I am also a co-PI on the NSF-funded National Research Traineeship (NRT) program "NRT: Ecosystem science in the face of rapid ocean change: a convergence approach" (Award #2244117, $2.99M), where I contribute to training graduate students to develop and apply computational and machine learning methods to address complex challenges in ecosystem science.
Ph.D. in Computer Science, Duke University (2020).
B.S. (with Honors) in Mathematics, University of Chicago (2013).