Rate My Professor Matt Boutell

Boutell specializes in image recognition, machine learning, pattern recognition, computer vision, scene classification, and probabilistic graphical models. He developed systems for classifying photographs into semantic categories by integrating image-based features, camera metadata, and temporal context. Key publications include "Scene parsing using region-based generative models" with Jiebo Luo and Christopher Brown (IEEE Transactions on Multimedia, 2007), "Pictures are not taken in a vacuum: An overview of exploiting context for semantic scene content understanding" (IEEE Signal Processing Magazine, 2006), "Learning multi-label semantic scene classification" (Pattern Recognition, 2004), "A multidisciplinary robotics minor" with Carlotta Berry, David Fisher, and Steve Chenoweth (ASEE Computers in Education Journal, 2010), and "SPLICE: Self-Paced Learning in an Inverted Classroom Environment" with Curt Clifton (SIGCSE, 2011). Among his honors are the Rose-Hulman Dean’s Outstanding Teacher Award in 2016, Fulbright Scholar at Copperbelt University in Zambia from 2011 to 2012, Best Student Paper award at the IEEE Western New York Image Processing Workshop in 2004, and Federal GAANN fellowship during Ph.D. studies from 2000 to 2003. Boutell established Rose-Hulman’s multidisciplinary robotics minor, introduced robotics into the introductory software development course, created courses in image recognition and Android application development, directed the RHIT Imaging Systems Laboratory since 2007, supervised undergraduate theses leading to publications, and served on committees including Quality of Education, Admissions, and Graduate Studies, as well as program committees for conferences.