Bongjun Kim is a PhD candidate in Computer Science. His research interests lie at the interaction of machine learning, audio/music signal processing, and HCI. Specifically, he is interested in designing algorithms and interfaces for users to be able to search for their desired audio contents (e.g. music or sound events) easily and fast with the help of interactive machine learning leveraging both strengths of human and machine. As a Segal Design Cluster Fellow, he has been working on an interface for sound event detection and annotation that speeds up labeling environmental sound events in a long audio file. Outside of grad school, he enjoys creating interactive media art and composing pop music.
MS, Culture Technology, KAIST, Korea
BS/MS, Industrial Engieering, Ajou University, Korea