I am an interdisciplinary data scientist who specializes in human behavior analysis, including experimental design, emotion research, machine learning, and decision science. I also have expertise in the audio domain, including music cognition & perception and the analysis of vocal biomarkers in speech and NLP.

A curious researcher

My work draws on a number of fields, including data science & machine learning, market research, psychology, cognitive science, neuroscience, and music theory. I use my knowledge of these diverse subjects to fuel my passion for interdisciplinary research.

Research Interests

Data Science
Behavioral Intelligence
Social Psychology
Emotion & Mental Health

Music Perception & Cognition
Music Information Retrieval
Music Theory
Vocal Biomarkers

reading

My music research centers broadly on how (and why) people relate to music. I utilize a wide range of data analytic techniques in order to do this, including traditional music theoretic analysis, corpus studies, computational methods, behavioral studies, pharmacological procedures, and psychophysical methods. My aim is to employ different kinds of data analysis to tell stories about why we love music.

Uncovering stories with data

16

publications and proceedings articles

40

invited lectures and conference presentations & posters

8

classes taught at the college level