Education:
B.A., Carleton College
M.S., Ph.D., Iowa State University

Teaching & Research Interests: 
Current areas are centered on pedagogy of computer science education. Past areas include machine learning, theory of real-valued computation, and abstract biological computation, artificial intelligence, computation & complexity, graph theory, machine learning, probability, and statistics.

Publications:

• B. Patterson, C.R. McBride, and J.L. Gieger (2018). Flipping Your Mathematics Classroom Without Videos. Problems, Resources, and Issues in Mathematics Undergraduate Studies (PRIMUS), 18(8), 742-753.
• B. Patterson and J.L. Gieger (2014). A Short College Course in the Spirit of the CS Principles Project. Proceedings of the Twenty-Eighth Annual Consortium for Computing Sciences in Colleges, Southeastern Conference, Charleston, SC, November 7-8, 2014.
• J.I. Lathrop, J.H. Lutz, and B. Patterson (2011). Multi-Resolution Cellular Automata for Real Computation. Proceedings of the
  Computability in Europe Conference (CiE), Sofia, Bulgaria.
• B. Patterson and D. Margaritis (2005). Essential Hidden Variables: An Introduction. Proceedings of The Second Indian
  International Conference on Artificial Intelligence (IICAI), Pune, India. Presentations and Other Informal Activities
• J.L. Gieger, B. Patterson, and C.R. McBride (2015). The Impact of a Flipped Learning Environment on Student Attitudes and Achievement in a Liberal Arts Mathematics Course. Proceedings of the Joint Mathematics Meeting (JMM), San Antonio, TX, January 10-13, 2015.
• B. Patterson and W. Doane (2014). Toolmaker or Scientist? Birds of a Feather session leader, SIGCSE 2014, Atlanta, GA.
• B. Patterson (2013). Response to Against Indifference: Popper’s Assumption of Distribution Preference by Brett Mullins,
  Georgia State University. Invited Response. Georgia Philosophical Society Annual Meeting, Atlanta, GA.
• B. Patterson (2014). Computational Graph Theory. Invited Talk to Southern Polytechnic State University (SPSU) on October 23, 2014. Marietta, GA.

Courses Taught:

• Introduction to Programming CSC 201
• Introduction to Programming Lab CSC 201L
• Data Structures CSC 202
• Algorithms CSC 320
• Computer Organization CSC 270
• College Algebra
• Precalculus MAT 125
• Calculus I MAT 131
• Calculus II MAT 132
• Machine Learning