Pablo M. Olmos


Research interests

My research interests are in the area of digital communications and machine learning. Currently, I work on the following problems:

  • Analysis and design of finite-length SC-LDPC codes and Generalized LDPC codes.

  • Expectation Consistency methods for Approximate Inference in Probabilistic Graphical Models.

  • Bayesian Multi-view Learning for Heterogeneous Database Integration.

  • Topic Modeling and Text Recognition in Historical Documents.

Latest news!

Paper accepted at IEEE Transactions on Communications (July 2017)

Continuous Transmission of Spatially-Coupled LDPC Code Chains. Pablo M. Olmos, David G. M. Mitchell, Dmitri Truhachev, Daniel J. Costello, Jr.

Files for the Tensor Flow Course uploaded (March 2017)

Python notebooks to get started with Tensorflow, Neural Neworks (NNs), Convolutional NNs, Word Embeddings and Recurrent Neural Networks. This is a personal wrap-up of all the material provided by Google's Deep Learning course on Udacity, so all credit goes to them.

You can find the material in the code section.

Paper accepted at IEEE Transactions on Wireless Communications (March 2017)

Probabilistic Equalization with a Smoothing Expectation Propagation Approach. Irene Santos, Juan José Murillo-Fuentes, Eva Arias-de-Reyna, and Pablo M. Olmos.