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Biography

I am currently an Assistant Professor at Northeastern University in the Applied Psychology Department, with an affiliation in the Psychology Department. I am also a Research Associate in the Psychiatry Department of Massachusetts General Hospital with the Depression Clinical and Research Program. My research interests pertain to leveraging state-of-the-art statistical approaches to address questions relating to the nosology and treatment of emotional disorders. Specifically, my research embraces statistical procedures that foster idiographic and precision medicine approaches to clinical psychology, such as intensive time-series research designs and machine learning approaches. These activities are complemented by my interests in philosophy of science. I completed my postdoctoral fellowship in the Psychiatry Department at Harvard Medical School / Massachusetts General Hospital. I received my Ph.D. in Clinical Psychology from Boston University under the mentorship of Dr. Stefan Hofmann, and I completed my pre-doctoral internship in the CBT track at MGH/Harvard Medical School. Prior to pursuing my doctoral degree, I was a statistician and researcher in the clinical Psychology Department at Yale University for several years. I received my undergraduate degrees from Rutgers University in cognitive science, philosophy, and psychology.

Interests

Dynamic Time-Series Approaches to Emotional Psychopathology using Network Science and Dynamical Systems Theory

Most of my research investigates dynamic time-series frameworks for understanding emotional disorders. Substantive interests include the application of network science, dynamical systems, complex causal systems, and time-series machine learning to characterize how emotional disorders unfold over time. A better understanding of idiographic temporal processes underlying psychopathology affords greater insight into both disorder nosology and treatment processes.

Precision Medicine and Predictive Modelling for Emotional Disorders

A second and complementary area of research relates to furthering precision medicine approaches to emotional disorders. Much of my recent and ongoing research has examined the utility of machine learning models in predicting important clinical outcomes (e.g., response to treatment, changes in disorder state etc.). Time-seies extensions of machine learning provide opportunities for forecasting changes in symptom dynamics at the individual level. The ultimate objective is to leverage predictive modeling procedures to inform just-in-time adaptive interventions that are personalized at the individual level.

Emotion Regulation and Mindfulness

Another principal domain of inquiry is how deficits in emotion regulation and mindfulness relate to the maintenance of emotional disorders. This work culminated in the Conditional Process Model (CPM), which endeavors to unify mindfulness and emotion regulation in a combined model that specifies how mindfulness and emotion regulation deficits interact in such a manner as to contribute to psychopathology. Furthermore, I am interested in emotion regulation mechanisms underlying empirically supported treatments.

Statistics and Computational Modeling of Psychopathology

Broad interests include the potential application of a wide range of statistical and computational strategies for modeling emotional disorders and delineating treatment processes underlying empirically supported interventions. Examples include the application of finite mixture models, latent variable modeling, time-series and forecasting, longitudinal mediation and growth curve analyses, etc.

Philosophy of Science and Psychopathology

My scientific research is complemented by my abiding passion for metaphysics, epistemology, and philosophy of science. Such work primarily explores the ontology of mental disorders vis-à-vis realist and anti-realist frameworks as explanatory accounts.