We conduct research that seeks to clarify the emergence of risk behaviors in adolescence. We focus on the developmental sequalae of childhood adversity, on a range of outcomes such as impulsivity, delay discounting, substance use problems, and suicidality.

We investigate youth developmental outcomes, including sleep, cognitive functioning, depressive symptoms, and emotion regulation, leveraging Team Science to use multilevel (from neuron to neighborhood) and multimethod (observations, fMRI, fNIRS, psychophysiology, surveys) approaches.

We research the effects of psychosocial stress (e.g., maltreatment, discrimination, and socioeconomic status affect mental health outcomes, coping mechanisms, and risk behaviors) on youth development, especially among marginalized populations. We are interested in identifying protective factors and mechanisms (hormesis) like future orientation, social support, and positive youth development.

Informed by developmental systems theories centering on self-organization, process dynamics, bi-directional interactions and transactions, we employ a developmental psychopathology approach to studying adolescent resilience versus risk behaviors and their association with chronic stress (e.g., family conflict, child maltreatment, poverty). In particular, in my program of research, we have been studying the developmental pathways and trajectories that underlie the link between early childhood adversity on the development of child and adolescent health risk including psychopathology and adjustment problems. We are especially interested in using multi-level research (from the neighborhood through the family and to the neural level) to understand the developmental ecology in which adolescent risk behaviors probabilistically emerge during development, spanning from childhood to emerging adulthood.

We examine how parenting style and approaches including parental support, harsh parenting, coparenting, and parental history of maltreatment influence child psychosocial and neurocognitive and neuroaffective development (e.g., emotional regulation, aggression, and delinquency).

We strive to use data-informed developmental science. We aim to push the boundaries of how data is collected, analyzed, and interpreted to gain new insights and solve complex problems. This is done by using cutting-edge techniques, algorithms, and technologies to extract meaningful information from vast and diverse datasets. This can include advancements in machine learning, artificial intelligence, data visualization, statistical modeling, and computational methods.

We utilized methods and theories that aim to shed light on cognitive development in children and adolescents with the aim of unraveling the underlying development of risk behaviors and resilience.