António R. C. Paiva

Antonio R. Paiva (Senior Member, IEEE) received the licentiate degree in electronics and telecommunications engineering from the University of Aveiro, Aveiro, Portugal, in 2003, and the MS and PhD degrees in electrical and computer engineering from the University of Florida, Gainesville, Florida, in 2005 and 2008, respectively. Between 2003 and 2004, he was as a research assistant with the Institute of Electronics and Telematics Engineering, University of Aveiro working on image compression. And, from 2008 to 2010, he worked with SCI Institute, University of Utah as a postdoctoral fellow on the application of machine learning to image analysis and neural circuit reconstruction. Between 2010 and 2015, he was a research specialist with ExxonMobil Upstream Research Company working on analysis of geophysical data. He is currently a research associate with Corporate Strategic Research in the ExxonMobil Research and Engineering Company. He is an associate editor of the IEEE Transactions on Neural Networks and Learning Systems, and a past associate editor of the IEEE Signal Processing Letters. His research interests span several areas within machine learning, pattern recognition, and adaptive signal processing and their applications. He has published numerous articles on kernel methods, information-theoretic learning, and fast algorithms for machine learning. Most recently, his research is focused on applications of probabilistic graphical models and probabilistic deep learning models, and approximate methods for large-scale Bayesian inference.