A big fan of coding and physics, Dino earned a masters in Computational Physics at Faculty of Science Split, Croatia, on the topic of linear feature detection in astronomical images. Having re-analyzed the entire Sloan Digital Sky Survey (SDSS) ~16TB large image dataset he discovered his passion for Big Data and related image analysis problems. Currently works on Kernel Based Moving Object Detector (KBMOD) used to detect moving objects in image below a single image signal-to-noise-ratio. Previously he worked on Vera C. Rubin Middleware code, adding support for cloud services to the Rubin Data Butler and executing the Rubin Science Pipelines in the cloud. To various different extent he is involved in experimental application of new computer vision techniques on images, looking for new ways of detecting, segmenting, measuring and describing astronomical objects in images at scale.