Ulisses de Mendonça Braga-Neto, Ph.D.

Academic Website

Research

  1. Scientific Machine Learning (SciML)

    This is an emerging area that brings together the fields of Machine Learning and Scientific Computation. SciML introduces scientific model constraints in Machine Learning algorithms, allowing the extrapolation and prediction of the behavior of complex systems in an interpetable and verifiable way. Our research goals concern improved training of physically-informed neural network models for forward and inverse modeling and its applications in science and engineering problems. We are coordinating the recently established Texas A&M Institute of Data Science (TAMIDS) SciML Lab. For more information: https://sciml.tamids.tamu.edu/.

    Funding: Texas A&M Institute of Data Science (TAMIDS).

  2. Statistical Signal Processing

    This research concerns the development of optimal estimation and control tools for stochastic nonlinear dynamical systems. The main applications of this work are modeling of gene regulatory networks and epidemic spread of viral diseases, in particular, Covid-19. One of the main tools used in this work is the partially-observed Boolean dynamical system (POBDS) model and its optimal state estimator, the Boolean Kalman Filter and Smoother. Joint estimation of state and parameters is accomplished using maximum-likelihood, swarm optimization, and Bayesian approaches. State-feedback, point-based, and reinforcement-learning control algorithms are developed for optimal intervention.

    Funding: National Science Foundation, Awards CCF-1320884, CCF-1718924.

  3. Classification and Error Estimation

    The research goals here concern the solution of significant computational and statistical problems in classifier design and classification error estimation based on small samples in high-dimensional spaces, with application in the classification of genomic, proteomic, immunomic, and metagenomic signals Some of the milestones of this research include the introduction of the Bolstered Error Estimator, the detailed distributional study of error estimation methods in small-sample settings and separate sampling schemes, and the development of Bayesian classification algorithms for transcriptomic, proteomic, and metagenomic data.

    1. National Science Foundation, Award CCF-0845407 (CAREER).
    2. Texas A&M Engineering Interdisciplinary Seed Grants for Strategic Initiatives.

  4. Collaborative Applications

    Our current collaborative work has four main active components: (1) Modeling of Infectious Disease Epidemics. Collaborator: Martial Ndeffo MBah (College of Veterinary Medicine, Texas A&M University). (2) Study of infectious diseases for discovery of diagnostic/prognostic biomarkers. Collaborator: Ernesto Marques (University of Pittsburgh) and Bartolomeu Accioli (FIOCRUZ, Recife, Brazil). (3) Study of Aging-Related Diseases. Collaborator: Nicolaas Deutz, Texas A&M Center for Translational Research in Aging & Longevity (CTRAL). (4) Microstructure Informatics. Collaborators: Raymundo Arroyave (Texas A&M Department of Materials Science and Engineering).

    Funding:

    1. National Science Foundation, Awards CCF-1320884, CCF-1718924,CCF-0845407 (CAREER).
    2. Texas A&M Engineering Interdisciplinary Seed Grants for Strategic Initiatives.
    3. Texas A&M Center for Bioinformatics and Genomics Systems Engineering.
    4. Data-Enabled Discovery and Design of Energy Materials (D3EM) training program.