Summary
Justin Ales is a lecturer and researcher with 12 years of experience at the intersection of vision science and electrical engineering, combining PhD-level expertise in vision science with hands-on signal processing and machine learning. He leads multimodal neuroimaging projects at the University of St Andrews that model spatial and temporal processing of motion in complex 3D and cluttered environments, translating insights from EEG/fMRI into computational models. Previously at Stanford and Smith-Kettlewell he developed algorithms to fuse high-dimensional neural data, pioneered BCI methods for assessing video quality, and built software suites and prototype displays for cross-lab experiments. Known for bridging theory and engineering, he often turns natural-scene pixel statistics into testable models of perception and has a track record of deploying novel analysis pipelines for neural data. Based in Cupar, Scotland, he teaches statistics, data science, and perception, bringing interdisciplinary rigor and practical tooling to both research and curriculum development.
12 years of coding experience
3 years of employment as a software developer
Ph.D., Vision Science, Ph.D., Vision Science at University of California, Berkeley
BS, Biomedical/Electrical Engineering, BS, Biomedical/Electrical Engineering at University of Southern California