Leif Johnson

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I am a Computer Science graduate student at The University of Texas at Austin. I am interested in computational models of cognitive tasks such as language learning, motor learning, and concept formation. My work relies heavily on basis functions as an efficient code for natural stimuli, and on the idea that motor representations are important (and maybe even necessary) for learning in cognitive domains.

Contact

Email: leif@cs.utexas.edu
Desk: ENS 32NE02

Publications

• 2012. R Miikkulainen, E Feasley, L Johnson, I Karpov, P Rajagopalan, A Rawal, W Tansey. "Multiagent Learning through Neuroevolution." In J. Liu et al., editors, Advances in Computational Intelligence, LNCS 7311, 24-46, Berlin, Heidelberg: Springer. PDF
• 2012. P Jyothi, L Johnson, C Chelba, B Strope. "Distributed discriminative language models for Google voice-search." IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP). PDF
• 2011. B Sullivan, L Johnson, D Ballard, M Hayhoe. "A modular reinforcement learning model for human visuomotor behavior in a driving task." Active Vision Symposium, Artificial Intelligence and the Study of Behavior (AISB). PDF
• 2003. E Teiniker, S Mitterdorfer, L Johnson, C Kreiner, Z Kovacs, R Weiss. "A Test-Driven Component Development Framework based on the CORBA Component Model." 27th Annual International Computer Software and Applications Conference. Abstract
• 2002. L Johnson, P Wurman, "Information and Product Quality Dynamics in Tiered Supply Networks," AAAI Workshop on Multi-Agent Modeling and Simulation of Economic Systems. PDF

Software

Most of my code these days is written in Python, with a lot of help from the amazing packages included with scipy.

• py-c3d: A small set of utilities—at this point consisting of a file reader and writer, and a simple OpenGL visualization tool—for dealing with motion capture data files in the C3D binary format.
• python-depparse: A Python library and command-line tool for non-projective dependency parsing of natural language text.
• py-kohonen: A collection of several vector quantizers, including self-organizing (Kohonen) map, neural gas, and growing neural gas.
• py-lars: A naive implementation of Least Angle Regression, plus an implementation of Mairal et al.'s 2009 ICML paper on dictionary learning for sparse coding.
• py-particle: A Python implementation of a generic particle filter.
• py-perceptron: The classic perceptron and the averaged perceptron (an approximation to the voted perceptron).
• py-pursuit: The matching pursuit sparse coding algorithm, and an implementation of the sparse coding approach described by Smith & Lewicki (2006). Includes an experimental CUDA implementation, too !
• py-rbm: Several types of Restricted Boltzmann Machines.
• py-sound: A collection of code for representing and manipulating sound data.
• py-trm: A Python wrapper for the Gnuspeech Tube Resonance Model, a vocal synthesizer.

Education

Industry

Etc

Among other things, I like pie and sitting on porches. See Leif Johnson for my personal site.