I am an Assistant Professor in the Statistics Department at West Virgina University. My research focuses on statistical signal processing, with specific application to Independent Component Analysis of neural signal recordings.

Courses

• Fall 2022: STAT 445/545 Data Analysis/Applied Regression Analysis

Selected Publications:

• J. A. Palmer, S. Makeig, and K. Kreutz-Delgado, An EM Algorithm for Maximum Likelihood Estimation of Barndorff-Nielsen's Generalized Hyperbolic Distribution, IEEE Workshop on Statistical Signal Processing, La Palma, Mallorca, 2016.
• J. A. Palmer, S. Makeig, and K. Kreutz-Delgado, A Generalized Multivariate Logistic Model and EM Algorithm based on the Normal Variance Mean Mixture Representation, IEEE Workshop on Statistical Signal Processing, La Palma, Mallorca, 2016.
• J. A. Palmer, S. Makeig, and K. Kreutz-Delgado, The Linear Process Mixture Model, IEEE Intl Workshop on Machine Learning for Signal Processing, Southampton, UK, Sept 24, 2014.
• J. A. Palmer and S. Makeig, Contrast Functions for Independent Subspace Analysis, Proceedings of the 10th International Conference on Latent Variable Analysis and Independent Component Analysis, Edited by Ari Yereador et al., Lecture Notes in Computer Science, Springer, 2012.
• J. A. Palmer, S. Makeig, and K. Kreutz-Delgado, Strong Sub- and Super-Gaussianity, Proceedings of the 9th International Conference on Latent Variable Analysis and Independent Component Analysis, Edited by Remi Gribonval and Emmanuel Vincent, Lecture Notes in Computer Science, Springer, 2010.
• J. A. Palmer, K. Kreutz-Delgado, and  S. Makeig, Probabilistic Formulation of Independent Vector Analysis using Complex Gaussian Scale Mixtures, ICA 2009.
• J. A. Palmer, S. Makeig, and  K. Kreutz-Delgado, A Complex Cross-spectral Distribution Model using Normal Variance Mean Mixtures, ICASSP 2009.
• J. A. Palmer, S. Makeig, K. Kreutz-Delgado, and B. D. Rao, Newton Method for the ICA Mixture Model, in Proceedings of the 33rd IEEE International Conference on Acoustics and Signal Processing (ICASSP 2008), Las Vegas, NV, pp. 1805-1808, 2008.
• J. A. Palmer, K. Kreutz-Delgado, B. D. Rao, and S. Makeig, Modeling and Estimation of Dependent Subspaces with Non-Radially Symmetric and Skewed Densities, Proceedings of the 7th International Symposium on Independent Component Analysis, Edited by Mike E. Davies, Christopher J. James, Samer A. Abdallah and Mark D Plumbley, Lecture Notes in Computer Science, Springer, 2007.
• J. A. Palmer, K. Kreutz-Delgado, and S. Makeig, Super-Gaussian Mixture Source Model for ICA, Proceedings of the 6th International Symposium on Independent Component Analysis, Edited by Justinian Rosca, Deniz Erdogmus, Jose C. Principe and Simon Haykin, Lecture Notes in Computer Science, Springer, 2006.
• J. A. Palmer, K. Kreutz-Delgado, D. P. Wipf, and B. D. Rao, Variational EM Algorithms for Non-Gaussian Latent Variable Models, Advances in Neural Information Processing Systems 18, Proceedings of the 2005 Conference, Edited by Yair Weiss, Bernhard Scholkopf and John Platt, MIT Press, 2006.
• J. A. Palmer and K. Kreutz-Delgado, A General Framework for Component Estimation, Proceedings of the 4th International Symposium on Independent Component Analysis, 2003.
• J. A. Palmer and K. Kreutz-Delgado, A Globally Convergent Algorithm for MAP Estimation with Non-Gaussian Priors, Proceedings of the 36th Asilomar Conference on Signals and Systems, 2002.
  
  
• J. A. Palmer, K. Kreutz-Delgado, and S. Makeig,  AMICA: An Adaptive Mixture of Independent Component Analyzers with Shared Components, (in preparation).
• J. A. Palmer, K. Kreutz-Delgado, and S. Makeig,  Dependency Models based on Generalized Gaussian Scale Mixtures and Normal Variance Mean Mixtures (in preparation).