M. G. S. Bruno received the B.S. and M.S. degrees in electrical engineering from the University of São Paulo, Brazil, and the Ph.D degree in electrical and computer engineering from Carnegie Mellon University, Pittsburgh PA, U.S.A. His main area of research is statistical signal processing, with an emphasis on sequential Monte Carlo methods/particle filters, Markov Chain Monte Carlo (MCMC), probabilistic models on graphs (HMMs, Bayesian networks, Markov random fields) and their applications in target detection and tracking, navigation, image/video processing, mobile robotics, machine learning, and telecommunications. Current interests include distributed signal processing, particularly Bayesian methods for distributed estimation of signals or parameters in cooperative sensor networks combining sequential Monte Carlo methods with consensus, diffusion and/or random information dissemination strategies.
(Source: Lattes Curriculum)