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Lab Director

Leanne Hirshfield

                                                             Leanne.Hirshfield@colorado.edu

Professor 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 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 avoid overfitting and succeed in ecologically valid environments outside the lab.

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PhD Student

Lucca Eloy

Lucca.Eloy@colorado.edu

Lucca Eloy is a third-year PhD student in computer and cognitive science working in the SHINE Lab. His research explores the neural and physiological correlates of affective states that can be derived from non-invasive brain measurement and various multimodal signals. In particular, he studies emotions that arise during human-machine interaction and teaming. He is interested in using linear and nonlinear statistical methods alongside machine learning to better understand and design systems that adapt to human cognition and affect.

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PhD Student

Trevor Grant

Trevor.Grant@colorado.edu

Trevor Grant is a research assistant in the SHINE lab. His primary focus is on exploring the neurophysiological correlates to different types of mental workload experienced while users interact with virtual environments. Consequently, his work overlaps with various sub-domains of cognitive psychology, primarily by using theories of attention, perception and memory to develop models of mental load. 

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PhD Student

Lucas Hayne

Lucas.Hayne@colorado.edu

Lucas Hayne is a third-year computer science PhD student in the SHINE Lab. He explores the use of deep neural networks to predict mental states from fNIRS data. His research interests lie at the intersection of machine learning and neuroscience, where he hopes recent advances in statistical learning will help answer questions about the brain, cognition, and the mind.

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