Dr. Ulisses Braga-Neto wins prestigious NSF CAREER Award
March 02, 2009; Dr. Ulisses Braga-Neto, a faculty member of GSP Lab in the Department of Electrical and Computer Engineering at Texas A&M University, has received the prestigious Faculty Early Career Development (CAREER) Award sponsored by the National Science Foundation’s (NSF).
Dr. Ulisses Braga-Neto
Dr. Ulisses Braga-Neto received the prestigious award, which was established to support junior faculty within the context of their overall career development, combining in a single program the support of research and education of the highest quality and in the broadest sense. Through this program, the NSF emphasizes the importance on the early development of academic careers dedicated to stimulating the discovery process in which the excitement of research is enhanced by inspired teaching and enthusiastic learning.
Braga-Neto received his CAREER award for his proposal, “Theory and Application of Small-Sample Error Estimation in Genomic Signal Processing.”
He joined the Biomedical Imaging and Genomic Signal Processing group of the department in January 2007. He was previously an assistant researcher with the Aggeu Magalhães Research Center from 2004-2006 and from 2002-2004 he was a post-doctoral researcher with the Section of Clinical Cancer Genetics of the University of Texas M.D. Anderson Cancer Center.
Braga-Neto received his Ph.D. degree in electrical and computer engineering from The Johns Hopkins University in 2002, and M.S.E. degrees in electrical and computer engineering and mathematical sciences, also from The Johns Hopkins University, in 1998. He received an M.S. degree from the State University of Campinas (Brazil) in 1994 and his B.S. degree from the Federal University of Pernambuco (Brazil) in 1992. Honors include being awarded the Abel Wolman Fellowship from The Johns Hopkins University in 1996. Braga-Neto’s research interests include genomic signal processing and statistical pattern recognition, with applications in the study of cancer and infectious diseases. He is particularly interested in the design and analysis of statistical methods of small-sample classification and error estimation for genomics and proteomics applications.
