Rate My Professor Adam Cannon

Cannon earned his PhD in applied mathematics from Johns Hopkins University in 2000, an MS in aerospace engineering from the University of California, Los Angeles in 1994, and a BS in aerospace engineering from the University of California, Los Angeles in 1991. His research interests encompass machine learning, statistical pattern recognition, approximation algorithms, and computer science education, with a current emphasis on effectively teaching computer science to liberal arts students, especially those in the humanities. Cannon has received prestigious teaching awards, including the Presidential Award for Outstanding Teaching from Columbia University in 2016, the Great Teacher Award from the Society of Columbia Graduates in 2016, the Department of Computer Science Faculty Teaching Award in 2009, and the Distinguished Faculty Teaching Award from the SEAS Alumni Association in 2002. Selected key publications include 'Machine learning with data dependent hypothesis classes' (Journal of Machine Learning Research, 2002), 'Lessons Learned from a PLTL-CS Program' (ACM SIGCSE, 2011), 'Multiple Instance Learning using Simple Classifiers' (ICMLA, 2004), 'Approximation algorithms for the class cover problem' (Annals of Mathematics and Artificial Intelligence, 2004), and 'BackStop: A tool for Debugging Runtime Errors' (ACM SIGCSE, 2008).