Dr. Hirshfield’s research explores the use of non-invasive brain measurement to passively classify users’ social, cognitive, and affective states in order to enhance usability testing and adaptive system design. She focuses her research on individual and team-level performance. Hirshfield works primarily with functional near-infrared spectroscopy (fNIRS), a relatively new non-invasive brain imaging device that is safe, portable, robust to noise, which can be implemented wirelessly; making it ideal for research in human-computer interaction. The high density fNIRS equipment in Hirshfield’s lab provides rich spatio-temporal data that is well suited as input into deep neural networks and other advanced machine learning algorithms. A primary tenet of Hirshfield’s machine learning research involves building and labeling large cross-participant, cross-task fNIRS training datasets in order to build robust and generalizable models that can generalize to ecologically valid settings.
keywords
functional near-infrared spectroscopy, brain-computer interaction, human-computer interaction, trust, affect, human information processing, adaptive systems, artificial intelligence, team science, collaborative problem solving, machine learning, brain-computer interfaces
CSCI 6940 - Master's Degree Candidacy
Primary Instructor
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Fall 2023
For students who need to be registered for the purpose of taking the master's comprehensive exam and who are not otherwise registered. Credit does not count toward degree requirements.
