I am a Computer Science doctoral
student at The University of Texas at Austin,
working in Prof. Dana Ballard's
Lab. I am interested in computational models of cognitive tasks such as
language learning, motor learning, and attention allocation. My thesis focuses
on the use of efficient codes for representing dynamic, feedback processes like
movements and speech.
Desk: GDC 3.518B
- L Johnson, DH Ballard. "Efficient codes for inverse dynamics during walking." In Proc. Assoc. Advancement of Artificial Intelligence (AAAI). PDF Poster
- L Johnson, DH Ballard. "Classifying movements using efficient kinematic codes." In Proc. Annual Meeting of the Cognitive Science Society. PDF Poster
- L Johnson, B Sullivan, MM Hayhoe, DH Ballard. "Predicting human visuomotor behaviour in a driving task." Phil. Trans. R. Soc. B 2014, 369. PDF
- L Johnson, J Cooper, DH Ballard. "Unified loss functions for multi-modal pose regression." In Proc. IEEE International Joint Conference on Neural Networks. PDF
- L Johnson, B Sullivan, DH Ballard, MM Hayhoe. "A soft barrier model for human behavior in a two-task driving environment." In Proc. Annual Meeting of the Cognitive Science Society. PDF
- L Johnson. "Python tools for coding and feature learning." Talk given at SciPy 2013. slides video
- P Jyothi, L Johnson, C Chelba, B Strope. "Distributed discriminative language models for Google voice-search." In Proc. IEEE International Conference on Acoustics, Speech and Signal Processing. PDF
- P Jyothi, L Johnson, C Chelba, B Strope. "Large-scale discriminative language model reranking for voice-search." In Proc. North American Association for Computational Linguistics - Human Language Technologies. PDF
- R Miikkulainen, E Feasley, L Johnson, I Karpov, P Rajagopalan, A Rawal, W Tansey. "Multiagent Learning through Neuroevolution." In J. Liu et al., eds., Advances in Computational Intelligence, LNCS 7311, 24-46, Berlin, Heidelberg: Springer. PDF
- B Sullivan, L Johnson, C Rothkopf, MM Hayhoe, DH Ballard. "The effect of uncertainty and reward on fixation behavior in a driving task." Journal of Vision 12 (9). DOI 10.1167/12.13.19. PDF
- B Sullivan, L Johnson, DH Ballard, MM Hayhoe. "A modular reinforcement learning model for human visuomotor behavior in a driving task." In Proc. Active Vision Symposium, Artificial Intelligence and the Study of Behavior. PDF
- E Teiniker, S Mitterdorfer, L Johnson, C Kreiner, Z Kovacs, R Weiss. "A Test-Driven Component Development Framework based on the CORBA Component Model." In Proc. 27th Annual International Computer Software and Applications Conference. abstract
- L Johnson, P Wurman, "Information and Product Quality Dynamics in Tiered Supply Networks," In Proc. AAAI Workshop on Multi-Agent Modeling and Simulation of Economic Systems. PDF
See my full profile on Google Scholar.
- 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-grep-plot: A command-line tool for creating quick plots from data in text files.
- 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. You really ought to use their amazing C++ implementation with Python (and Matlab) bindings.
- 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 convolutional sparse coding approach described by Smith & Lewicki (2006). Includes an experimental CUDA implementation !
- py-rbm: Several types of Restricted Boltzmann Machines. Also see theano-nets for neural network implementations using Theano.
- 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.
August 2008 – present Austin, TX
- TA: CS312 Introduction to Programming
- TA: CS378 The Computational Brain
- TA: CS394N Neural Networks (x2)
- TA: LIN350 / CS378 Natural Language Processing
- PSY394U Computational Methods in Cognitive Science
- CS388L Intro to Mathematical Logic
- CS388 Natural Language Processing
- CS395T Cognitive Science
- CS380P Parallel Systems
- LIN386M Semisupervised Learning for Computational Linguistics
- CS391L Machine Learning
- CS380C Compilers
August 1997 – May 2002 Raleigh, NC
- BS with honors, Computer Science
- BA with honors, Multidisciplinary Studies
- BS, Applied Mathematics
- Phi Kappa Phi, Phi Beta Kappa, Benjamin Franklin Scholar
August 1995 – August 1997 Durham, NC
Research Intern May 2010 – August 2010 Mountain View, CA
Research Intern May 2009 – August 2009 Mountain View, CA
Software Engineer March 2008 – August 2008 San Francisco, CA
Software Engineer November 2004 – March 2008 Mountain View, CA
Research Intern August 2002 – May 2003 Graz, Austria
For a year I headed up the Natural Language Learning Reading Group. In 2013 I started (along with fellow students Craig and Wes) the Feature Learning and Representation Encoding reading group for discussing papers in deep models and feature learning.
Among other things, I like pie and sitting on porches. See Leif Johnson for my personal site.