Adam Cannon

Adam Cannon is a Teaching Professor of Computer Science and Senior Lecturer in Machine Learning in the Department of Computer Science at Columbia University, where he joined the faculty in July 2000. As a key member of Columbia's teaching faculty, he develops and teaches large undergraduate computer science courses for both majors and non-majors. Cannon chairs the computer science undergraduate curriculum committee and serves on the SEAS Committee on Instruction. He has also contributed to the development committee for the new AP Computer Science Principles Exam. From 2000 to 2005, Cannon served as a visiting scientist at Los Alamos National Laboratory, where his research focused on machine learning methods for building data-dependent hypothesis classes. His professional affiliations include ACM SIGCSE and ACM SIGKDD.

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).

Professional Email: cannon@cs.columbia.edu