Faculty Profile

John MacCormick

Associate Professor of Computer Science (2007)

Contact Information

jmac@dickinson.edu

Tome Scientific Building Room 242
717.245.1626
http://users.dickinson.edu/~jmac

Bio

John MacCormick has degrees in mathematics from the University of Cambridge and the University of Auckland, and a doctorate in computer vision from the University of Oxford. He was a research fellow at Linacre College, Oxford from 1999-2000, a research scientist at HP Labs from 2000-2003, and a computer scientist with Microsoft Research from 2003-2007. Professor MacCormick joined the faculty of the Department of Mathematics and Computer Science at Dickinson College in Fall 2007. He is the author of two books (Stochastic Algorithms for Visual Tracking, and Nine Algorithms That Changed the Future: The Ingenious Ideas That Drive Today's Computers), has filed over a dozen US patents on novel computer technologies, and is the author of numerous peer-reviewed academic conference and journal papers. His work spans several sub-fields of computer science, including computer vision, large-scale distributed systems, computer science education, and the public understanding of computer science.

Education

  • B.A., University of Cambridge, 1993
  • M.S., University of Auckland, 1996
  • Ph.D., University of Oxford, 2000

2014-2015 Academic Year

Fall 2014

COMP 251 Computer Organization
Completion of both COMP 251 and COMP 332 fulfills the WR Requirement.

COMP 356 Programming Lang Structure
An examination of the major programming language paradigms. The course also explores the basic properties and special facilities of languages representing each paradigm. Topics include data types, scope rules, block structures, procedure calls and parameter types, and storage allocation considerations. Prerequisite: 232. Offered every fall.

COMP 364 Artificial Intelligence
A survey of techniques for applying computers to tasks usually considered to require human intelligence. Topics include knowledge representation and reasoning, search and constraint satisfaction, evolutionary and genetic algorithms, machine learning, neural networks, and philosophical questions. Prerequisites: 232 and MATH 211. Offered in even numbered fall semesters.