Crew biographies, Photos and Mission patch – March 17th

Leanne Hirshfield
Dr. Leanne 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.

Marta Čeko
Dr. Marta Čeko’s research explores brain mechanisms of pain and negative affect in health and disease. She combines computational modeling with neuroimaging, behavioral data and multiple types of physiological data to develop predictive and generalizable brain and physiology-based models of aversive processing and regulation.

James Crum
James is a postdoctoral research fellow at the Institute of Cognitive Science. More specifically, he is a cognitive neuroscientist at SHINE Lab. He uses multimodal methods (e.g., fMRI, fNIRS, deep-learning, etc.) in ‘real-world’ and lab-based paradigms to investigate the neurocognitive mechanisms supporting cognitive security (i.e., how the brain defends against information-based threats). This research is supported by the Department of Defense’s Multidisciplinary University Research Initiatives (MURI) Program.

Emily Doherty
Emily is a third-year PhD student in computer and cognitive science working in the SHINE Lab. Her research explores human-AI teaming using multimodal methods (non-invasive neuroimaging, natural language processing, machine learning) in varied contexts spanning from education to extreme environments. She is particularly interested in the design of equitable AI that not only enhances cognitive capabilities but also broadly serves society.