cs
The important thing in science is not so much to obtain new facts as to discover new ways of thinking about them.
--Sir William Bragg
Brian Patterson
Graduate Student
Iowa State University

I am currently working on my PhD degree under Dimitris Margaritis. I completed my Masters degree (2002-4) under Dimitris Margaritis (with Jack Lutz and Dan Ashlock on my committee). My complete and formal vitae can be found here.

Research Interests
(in rough order of interest)
  • Dealing with uncertainty in computing using Bayesian Networks
    • Parallel Learning of Bayesian Networks
    • Hidden Variables in Markov and Bayesian Networks
    • The Theoretical Family of Bayesian Networks with Hidden Variables
    • Alternative Representations of Uncertainty in Computational Models
  • Computational Complexity Theory
  • Aspect-oriented Programming Languages
Research Goals

My primary research area is investigation of methods of speeding up the learning of Bayesian networks using many, parallel processors. The current algorithms in the field (e.g. PC algorithm) do not leverage parallel processors very well due to interference between tests. I also am interested in representing and discovering hidden variables, or attributes of the environment not measured or unmeasurable, for probabilistic methodologies. I hope to design a natural probabilistic framework that makes discovery of hidden variables easy but remains as computationally tractable as possible. I am also interested in computational complexity theory, aspect-oriented languages, and other areas of Computer Science.

Projects and Research
  • Brian Patterson (2004). Essential Hidden Variables: An Introduction and Novel Algorithm for Detection. Master's Thesis, Iowa State University. Available as PDF.
  • Facundo Bromberg, Brian Patterson, and Sandeep Yaramakala (2003). Mining Bayesian Networks from Streamed Data. Course Project, Principles of Database Systems (CS561), Iowa State University Spring 2003. Available as PDF.
  • Brian Patterson (2002). Methods of Learning Bayesian Networks from Data - A Test on the ADULT Data Set. Course Project, Introduction to Artificial Intelligence (CS572), Iowa State University, Fall 2002.
  • Brian Patterson (2001). Clustering: Unsupervised Learning. Senior Integrative Exercise, Carleton College, Spring 2001. Available as Powerpoint 2001 file (1553k, zipped).
  • Brian Patterson (2000). Quantum Gates - A Closer Look. Quantum Information Theory (course), Carleton College, Fall 2000). Available as web pages.
  • Brian Patterson and Erin Quealy (2000). Fiber Optic. Course Project, Computer Networking, Carleton College, Spring 2000. Available as web pages.
Activities Related to Research and Teaching
  • Instructor, Introduction to Programming; Iowa State Department of Computer Science, August-December 2007, (January-May 2008)
  • Instructor, Algorithms; Iowa State Department of Computer Science, May-July 2007
  • Instructor, Introduction to Programming;Iowa State University OPPTAG, July 2006, July 2007, (July 2008)
  • Instructor, Introduction to Computer Science; Johns Hopkins Center for Talented Youth, Baltimore, MD (Easton, PA site), June to August 2005
  • Preparing Future Faculty (PFF) Student, Iowa State; Fall 2004 to present
  • Teaching Assistant; Iowa State Department of Computer Science, Fall 2002, Spring 2003, Summer 2003, Summer 2004, Fall 2004, Spring 2005, Fall 2005, Spring 2006, Fall 2006, Spring 2007
  • Undergraduate Fellow; Park City Mathematical Institute, July to August 2000
  • Teaching Assistant for Theoretical Foundations of Computer Science; Johns Hopkins Center for Talented Youth, June to July 2000
  • Supplemental Instruction Leader and Grader; Carleton College, 1999-2001