I have a broad interest on the research projects related to auditory neuroscience and have strong backgrounds and experience on many areas, including signal processing (machine learning), auditory perception (psychophysics and speech), cochlear implant, computer programming, cortical recording, perceptual learning, neural plasticity, and computer-based auditory rehabilitation. My primary research interests are to understand the mechanisms underlying speech pattern recognition in normal auditory system and electrically stimulated auditory system, learning-associated brain plasticity, and to optimize signal processing for neural prostheses. My approaches toward optimizing cochlear implant patient performance have been to maximize both the transmission (signal processing) and reception (auditory perception) of acoustic patterns and to develop efficient and effective training protocols and materials (auditory plasticity) for patients to use at home. I am also interested at understanding interactions between extensive music training (experience) and peripheral hearing loss, and further, developing effective music training approaches to benefit people with hearing loss as music is part of every known human culture, and seemingly affects every part of the human experience, from development to rehabilitation of sensory, motor, cognitive, and emotional processes. I am also interested at using machine learning (ML) technology to help hearing impaired people perceive auditory information via ML-based sound recognition and multi-sensory delivery and maximize cochlear implant or hearing aid users’ performance using advanced signal processing algorithms.