Quick bio: [BS] computer science + math → [MS] deep learning + cognitive modeling → [Researcher] deep learning for speech synthesis → computational neuroscience → [PhD] computational neuroscience + deep learning

Present: I am a PhD student at University of Montreal, affiliated with Mila - Quebec AI Institute and CHU Sainte-Justine hospital. I am generally interested in modeling neural networks for understanding the brain and improving artificial intelligence (AI). Particularly, learning algorithms that form memory and skills are of my interest because it contributes understanding the uniqueness of my self. These algorithms may inspire researchers to innovate AI models that outperform in a new way. I am now narrowing down research topics related to the integration of bottom-up and top-down signals in neocortical circuits.

Past: I researched on computational neuroscience, specifically, excitation-inhibition balance in neocortical circuits and biological motor control in human handwriting.  Previously, I worked on expressive speech synthesis, i.e., how to synthesize expressive speech by means of deep neural networks at KAIST (Korea Advanced Institute of Science Technology) for the projects led by Soo-Young Lee. Before joining KAIST, I was a research assistant in Biointelligence Lab. (PI: Byoung-Tak Zhang) at Seoul National University, worked for several machine learning projects, and received M.Sc. in Cognitive Science for connectionist cognitive modeling research on comparison between human’s and neural net’s arithmetic computation. During my undergraduate at Kwangwoon University, I was especially interested in database to efficiently manage data, numerical methods to solve mathematical problems, and computer graphics to simulate physical phenomena in the virtual environments; then, I earned B.Eng. in computer science and B.Sc. in mathematics.