Phone: 503 748-1160
Fax: 503 748-1306
Central Building, Room 157 (West Campus)
Machine Learning: Local and mixture models, stochastic learning, and Bayesian methods with applications to environmental sensor network monitoring, data and model/data fusion, and fault detection. Computational neuroscience.
Starting August 12, 2012, Todd Leen will be at the National Science Foundation serving as Program Director for Robust Intelligence in the Information and Intelligent Systems Division within the Computer Science and Engineering Directorate.
- R. Sharma, T.K. Leen and M. Pavel, "Bayesian Image Sensor Fusion Using Local Linear Generative Models," Optical Engineering, 40, 1364, 2001.
- C. Archer and T.K. Leen, "The Coding Optimal Transform," in Proceedings of the Data Compression Conference 2001, IEEE Computer Press, 2001.
- C. Archer and T.K. Leen, "From Mixtures of Mixtures to Adaptive Transform Coding," in Advances in Neural Information Processing Systems,
- T. Leen, T. Dietterich, V. Tresp, eds., 13, MIT Press, 2001.
- W. Wei, T.K. Leen and E. Barnard, "A Fast Histogram-Based Postprocessor that Improves Posterior Probability Estimates," Neural Computation, 11, 1235, 1999.
- T.K. Leen, B. Schottky and D. Saad, "Optimal Symptotic Learning: Macroscopic Versus Microscopic Dynamics," Physical Review E, 59, 985, 1999.
- T.K. Leen and J.E. Moody, "Stochastic Manhattan Learning: Time-Evolution Operator for the Ensemble Dynamics," Physical Review E, 56, 1262, 1997.
- N. Kambhatla and T.K. Leen, "Dimension Reduction by Local Principal Component Analysis," Neural Computation, 9, 1493, 1997.
- T.K. Leen, "From Data Distribution to Regularization in Invariant Learning," Neural Computation, 7, 974, 1995.
- T.K. Leen, "A Coordinate-Independent Center Manifold Reduction," Physics Letters, A 174, 89, 1993.