Ulisses de Mendonça Braga-Neto, Ph.D.

Ulisses' picture

Associate Professor
Department of Electrical and Computer Engineering
Genomic Signal Processing Lab
Center for Bioinformatics and Genomics Systems Engineering (CBGSE)
Center for Translational Environmental Health Research (CTEHR)
Texas A&M University

Active Research Areas
Statistical Signal Processing
Pattern Recognition and Machine Learning
Applications in Genomics

Publications | Students | Research | Teaching

News Items:

  • Dr. Braga-Neto has been elected to the Bio Imaging and Signal Processing Technical Committee (BISP TC) of the Institute of Electrical and Electronics Engineers (IEEE) Signal Processing Society for the 2017-2019 term. The purpose of the committe is to promote activities within the broad technical areas of biomedical and biological signal and image processing. (Dec 2017)

  • Dr. Braga-Neto has been elected to the Machine Learning for Signal Processing Technical Committee (MLSP TC) of the Institute of Electrical and Electronics Engineers (IEEE) Signal Processing Society for the 2017-2019 term. The committee is at the interface between theory and application, developing novel theoretically inspired methodologies targeting both longstanding and emergent signal processing applications. Central to MLSP is online/adaptive nonlinear signal processing and data-driven learning methodologies. The IEEE MLSP TC is expected to play a central role in the next few years in shaping the IEEE policies and approaches to Big Data. (Oct 2016)
    Link to News Item

  • Our project "Minimum Mean Square Error Estimation and Control of Partially Observed Boolean Dynamical Systems with Applications in Metagenomics" has been funded by a 3-year NSF CIF Award (starting August 2017). The project aims to develop and apply innovative signal processing techniques to uncover the complex interactions among microbes, human cells and their metabolic products in the gut. The main tools to be used and developed are the optimal MMSE Boolean Kalman Filter and Smoother algorithms for Partially-Observed Boolean Dynamical Systems (POBDS), along with maximum-likelihood and Bayesian parameter estimation methods, and stochastic control techniques for optimal sequential intervention design. This project is supported at Texas A&M by an interdisciplinary team consisting of Dr. Robert S. Chapkin (Nutrition and Food Science), Dr. Arul Jayaraman (Department of Chemical Engineering), Dr. Xiaoning Qian (Electrical and Computer Engineering) and Dr. Ivan Ivanov (Veterinary Physiology and Pharmacology). Dr. Johanna W. Lampe (Fred Hutchinson Cancer Research Center, Seattle, WA) is an external collaborator. More information here.

  • Our R Package BoolFilter, co-authored by L.D. McClenny, M. Imani and U.M. Braga-Neto, has been published by CRAN. BoolFilter implements algorithms for Partially-Observed Boolean Dynamical Systems (POBDS), a Hidden Markov Model that extends the Boolean Network model to include noisy indirect observations, with applications in Genomic Signal Processing. BoolFilter allows the user to estimate the hidden Boolean states using the Boolean Kalman Filter and Smoother, as well as infer network topology and noise parameters, from time series transcriptomic data using exact or approximate (particle) filters, as well as simulate time series transcriptomic data for a given Boolean network model. (Jan 2017)

  • Our book "Error Estimation for Pattern Recognition", co-authored with Ed Dougherty, has been published by IEEE-Wiley Press. This book is the first one dedicated exclusively to the topic of error estimation for pattern recognition. It covers both classical and recent results on the performance of error estimators for nonparametric and parametric classifiers. For a sample, including table of contents and subject index, please access the publisher's website or Google Books. The book is also available from amazon.com and barnesandnoble.com. (July 2015)

    Book Cover
  • The 2-year project "In silico modeling of microbiota-gut epithelial cell interactions for predicting dietary supplement impact on gut health" has been selected for funding by the Interdisciplinary Seed Grants for Strategic Initiatives program of Texas A&M Engineering in partnership with The Texas A&M University Division of Research (starting in Fall 2014). The PI for this interdisciplinary project is Dr. Braga-Neto, and the Co-PIs are Dr. Xiaoning Qian from the College of Engineering, Dr. Ivan Ivanov from Texas A&M Veterinary School, and Dr. Robert S. Chapkin from Texas A&M AgriLife, in collaboration with Dr. Johanna W. Lampe from the Fred Hutchinson Cancer Research Center and Dr. Cheryl L. Walker from the Texas A&M Health Science Center.

  • The project "Identification of Drought Tolerance Genes and Networks by Expression Profiling in Banana" has been funded by the Texas A&M Center for Bioinformatics and Genomics Systems Engineering, a partnership between Texas A&M Engineering and Texas A&M AgriLife. The project is conducted in collaboration with Dr. Martin Dickman from Texas A&M AgriLife and is renewable annually for a maximum of five years.

  • Dr. Braga-Neto was elected to Texas A&M Council of Principal Inverstigators as a College of Engineering representative for the 2014-2017 term. The CPI voices the concerns of the Texas A&M PI community to the high levels of administration. If you are a PI in the CoE and have questions or concerns that you want to be brought to the CPI please contact me.

  • The project "Optimal Estimation and Network Inference for Boolean Dynamical Systems" has been funded by a 3-year NSF CIF Award (starting in July 2013).

  • Dr. Braga-Neto's IEEE membership was elevated to the degree of Senior Member (September 2011).

  • The project "Theory and Application of Small-Sample Error Estimation in Genomic Signal Processing" was funded by a 5-year NSF CAREER Award (Spring 2009-Spring 2014).

Publication Errata:

  • The following conference paper is retracted:

    T.T. Vu, U.M. Braga-Neto and E.R. Dougherty, "Bagging Degrades the Performance of Linear Discriminant Classifiers," Proceedings of VII IEEE International Workshop on Genomic Signal Processing and Statistics (GENSIPS'2009), Minneapolis, MN, May 2009.

    Please consult the journal paper instead:

    T.T. Vu and U.M. Braga-Neto, "Is Bagging Effective in the Classification of Small-Sample Genomic and Proteomic Data?" EURASIP Journal on Bioinformatics and Systems Biology, Special Issue on Applications of Signal Processing Techniques to Bioinformatics, Genomics, and Proteomics, Volume 2009, Article ID 158368, 10 pages, 2009. doi:10.1155/2009/158368 [PubMed] [Hindawi Open Access]

  • The book chapter

    U.M. Braga-Neto and E. Dougherty, "Classification," In Genomic Signal Processing and Statistics, Edited by E. Dougherty, I. Shmulevich, J. Chen and Z. J. Wang, EURASIP Book Series on Signal Processing and Communication, Hindawi Publishing Corporation, 2005. [Preprint]

    has problems in Figures 3.8 and 3.9. However, none of the conclusions in the chapter changes due to these errors.
    Note: the preprint PDF linked above already contains the correct Figures 3.8 and 3.9.

E-mail address:
[my first name]@ece.tamu.edu

Mailing address:
Texas A&M University
Department of Electrical and Computer Engineering
3128 TAMU
College Station, TX
77843-3128 USA

(979) 862-6441 (Office)
(979) 845-6259 (FAX)

Last modified: 2018.2.23