The overarching goal of my research is to develop intelligent speech processing algorithms that continually learn from user and environmental data in real-world environments, while also preserving user privacy. I conduct interdisciplinary research that combines machine (deep) learning, signal processing, psychoacoustics and hearing science concepts. I work on research projects in the areas of speech enhancement/separation/dereverberation, perceptual speech assessment, auditory attention detection, and audio/speech privacy, to name a few. More specifically, my students and I have developed deep-learning algorithms that remove unwanted background noise from speech, predict human-level assessment of speech quality and intelligibility, estimate reverberation time, perform dereverberation, perform speech translation, and preserve speech privacy by generating obfuscating perturbations that ensure general and sensitive words are not recognized by advanced speech recognition algorithms.