The goal of my research is to better characterize, understand and predict psychopathology and related behaviors, particularly suicidal behaviors.
To gain in-depth descriptions of clinical phenomena, I develop thorough interviews and use real-time monitoring methods, as well as examine relationships among phenomena/symptoms using network analysis techniques.
To improve descriptions of key behaviors, such as suicide attempts, and ensure more accurate group classification, I test and refine existing assessment tools.
I seek to understand psychological risk factors and mechanisms involved in psychopathology and suicidal behaviors.
My recent work has focused on using reinforcement learning (RL) approaches and computational models to examine basic decision-making processes.
With my collaborators, I developed a novel RL behavioral paradigm and computational model to examine decisions to escape or avoid aversive contexts.
With computational models, we can infer specific decision-making processes that might be biased or dysfunctional in clinical populations.
I have also examined risk factors for suicidal behaviors within specific populations, such as military personnel.
I assess implicit cognition to improve the prediction of suicidal outcomes, particularly suicide attempts.
Our lab developed the Death/Suicide Implicit Association Test (D-IAT) to assesses associations with death or suicide outside of conscious awareness.
I focus on refining the D-IAT to improve prediction and classification of those at highest risk to translate this tool into clinical settings.