Ulisses Braga-Neto

Ulisses Braga-Neto is a Professor in the Department of Electrical and Computer Engineering at Texas A&M University, part of the College of Engineering. He holds a Ph.D. from The Johns Hopkins University (2002), with earlier degrees including an M.S. from the Federal University of Pernambuco (1992). His research focuses on statistical signal processing, pattern recognition, and machine learning, with applications in bioinformatics, materials informatics, and environmental modeling. He leads the TAMIDS Scientific Machine Learning Lab and has authored over 100 publications.

Key research areas include physics-informed neural networks for environmental and engineering problems, error estimation in classification systems, and gene regulatory network inference. His recent work emphasizes machine learning applications in agriculture (e.g., cotton detection in corn fields using UAS), CO2 sequestration modeling, and wildfire prediction. He has contributed to foundational texts like Error Estimation for Pattern Recognition (Wiley-IEEE, 2015).

Prof. Braga-Neto's interdisciplinary approach bridges computer science, engineering, and biology. His lab develops algorithms for data-poor environments, with applications in genomics, proteomics, and metagenomics. He advises graduate students through Texas A&M's ECE program, emphasizing rigorous statistical methods alongside machine learning innovation.