Ulisses de Mendonça Braga-Neto, Ph.D.

Academic Website

News

  • Durward Cator, an excellent undergraduate student in our department, developed a model to predict the spread of COVID-19 on campus, which includes optimal filtering methods to track the evolution of cases by using limited tests. Durward was a participant of the Summer Online REU program of the College of Engineering, and got an award for best presentation. This work was featured on the Engineering News and KBTX Local News. (September 2020)
  • Our Ph.D. student Mahdi Imani has received the 2019 Graduate Student Research Award from the Office of Graduate and Professional Studies and The Association of Former Students — only nine graduate students from the entire university received the award this year. Mahdi is graduating in May and will be an Assistant Professor at George Washington University’s Electrical and Computer Engineering Department from September 2019. (April 2019)

  • Our Ph.D. student Levi McClenny’s research was featured by Texas A&M Today. (April 2019)

  • 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 for the 2017-2019 term. The purpose of the committee 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)

  • Dr. Braga-Neto was elected to Texas A&M Council of Principal Inverstigators as a College of Engineering representative for the 2014-2017 term, and re-elected for the 2017-2020 term. He was also elected to the Texas A&M Faculty Senate for the 2017-2020 term. The CPI and the Faculty Senate voice the concerns of the Texas A&M PI community and the faculty at large to the high levels of administration. If you are a PI or faculty member in the CoE and have questions or concerns that you want to be brought to the CPI or the Senate please contact me.

  • 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)

  • 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.

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

  • Erratum: 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]

  • Erratum: The book chapter:
    “Classification,” by U.M. Braga-Neto and E. Dougherty, 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.