Javier Viaña

Javier obtained his Ph.D. in Explainable Artificial Intelligence applied to Aerospace Engineering at the University of Cincinnati. His doctoral research revolved around the conception of novel transparent algorithms that not only provide accurate predictions but also human-understandable justifications of the results. His current research is focused on the design of deep neural network architectures that automatically classify TESS transiting planet candidates. Previously, Javier developed tailored AI solutions for different aerospace organizations such as Aurora Flight Sciences, Boeing, Satlantis Microsatellites, NASA, ESA, Genexia, and the Northern Kentucky International Airport. He received his Bachelor’s in Mechanical Engineering at the University of the Basque Country, and Master’s in Aerospace Engineering at the University of Cincinnati. His main research topics and interest include transparency in AI, deep fuzzy networks, genetic fuzzy systems, bio-inspired evolutionary optimization, white dwarf spectral characterization, exoplanet detection, and black holes.