Summary
Daniel Guest is a software engineer and researcher who blends psychophysics and computational modeling to study pitch perception and auditory stream segregation. With nine years of experience spanning academic research and industry, he has applied deep learning interpretability techniques to low-latency speech segregation during an internship at Oticon and continues postdoctoral work in auditory neuroscience at the University of Rochester. He holds a PhD in Psychology from the University of Minnesota and a BS from UT Dallas, grounding his engineering skills in rigorous experimental design and human perception. Comfortable moving between code, models, and behavioral data, Daniel brings a rare combination of signal-processing intuition and psychophysical rigor to problems in auditory scene analysis.
10 years of coding experience
2 years of employment as a software developer
Vista Ridge High School
University of Minnesota Twin Cities
Westwood High School
Bachelor of Science (BS), Psychology, Bachelor of Science (BS), Psychology at University of Texas at Dallas
English, Spanish, Portuguese, Chinese