I'm a postdoctoral scholar in the Department of Child and Adolescent Psychiatry at the NYU School of Medicine. My research examines the neurobiological substrates of brain and behavioral development in the first three years of life, with an emphasis on the embedded sociocultural contexts in which development unfolds. I am passionate about grounding basic science research with a focus on social responsibility and community advocacy, with the ultimate goal of informing translational intervention-science and policy to help improve the lives of children and their families. Please check out my research!
Prior to coming to NYU, I completed my PhD in Cognitive Science at Brown University, and I graduated summa cum laude with honors with a BS in Psychology from the University of Arizona.
Over the first years of life, infants learn and develop at a rate that is unparalleled in the remainder of the lifespan. This period of rapid plasticity sets the stage for lifetime mental health and wellbeing. My research examines how early sociocultural contexts shape trajectories of brain and behavioral development, with the goal of better understanding how to create environments that best support children and families.
I am especially interested in understanding the proximal (e.g., maternal mental health, caregiver-infant interactions) and distal (e.g., social policies, systemic inequities) that foster healthy development across the lifespan.
I am also committed to increasing participation in scientific studies for families from diverse socioeconomic, racial/ethnic, and geographic backgrounds in my research. I recently developed an innovative artificial intelligence-based tool for remote eye tracking (OWLET: Online Webcam Linked Eye Tracker). This tool is currently being used to test infants from predominately low-income, rural, and Black families across the United States in the ORCA study, as well as infants in rural regions of Ethiopia through collaborations with the Global AIM Lab at Harvard Medical School. Please check out OWLET!
I recently developed an open-source methodology for automatically analyzing infant eye-tracking data from videos collected on laptops or smartphones. This tool combines algorithms from computer vision, machine learning, and ecological psychology to estimate infant's point-of-gaze from pre-recorded webcam and smartphone videos.
The source code for OWLET is open source, and I have also made a MacOSx app freely available to download.
Novel Coronavirus Illness - Patient Report (NCIPR) Study
To characterize people's personal experiences with COVID-19, we collected data from 2,212 confirmed COVID-19 patients ages 18 to 98. The Novel Coronavirus Illness Patient Report (NCIPR) dataset includes complete survey responses that address symptoms, medical complications, home and hospital treatments, lasting effects, anxiety about illness, employment impacts, quarantine behaviors, vaccine-related behaviors and effects, and illness of other family/household members.
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i = 0;
while (!deck.isInOrder()) {
print 'Iteration ' + i;
deck.shuffle();
i++;
}
print 'It took ' + i + ' iterations to sort the deck.';
