Summary
Sungjae Cho is a PhD student at the Université de Montréal working at the intersection of computational neuroscience and deep learning, with eight years of research experience modeling how biological and artificial neural networks learn memory and skills. His work spans expressive speech synthesis and biologically grounded models of neocortical circuits, producing publications at venues like ICASSP, CCN, and Cognitive Computational Neuroscience. Currently focused on integration of bottom-up and top-down signals in neocortical circuits, he combines theory, large-scale simulation, and task-driven neural network design to bridge brain mechanisms and AI. Past projects include biologically realistic handwriting motor sequence models and investigations of excitation–inhibition balance, reflecting a consistent interest in translating experimental neuroscience into computational algorithms. He has collaborated across top labs (Mila, KAIST, KIST, Seoul National University) and contributed to oral and poster presentations at major conferences. Beyond publications, he brings a rare blend of maths-and-systems training—double degrees in CS and math—plus hands-on experience in speech and motor modeling that informs his approach to learning rules that make AI more brain-like.
8 years of coding experience
6 years of employment as a software developer
Master of Science - MS, Cognitive Science (Artificial Intelligence), Master of Science - MS, Cognitive Science (Artificial Intelligence) at Seoul National University
Philosophiae Doctor - PhD, Computational Neuroscience & Deep Learning, Philosophiae Doctor - PhD, Computational Neuroscience & Deep Learning at Université de Montréal
Bachelor of Science - BS, Computer Science and Mathematics, Bachelor of Science - BS, Computer Science and Mathematics at Kwangwoon University