Our research is centered around the physics and engineering of human behavior. Our objective is to achieve a comprehensive quantitative characterization of human behavior, focusing on memory processes, emotions, and psychological well-being, and to discern whether and to what extent these dynamics can be quantified, predicted, and controlled. 

We believe that significant breakthroughs in understanding human brain and behavior’s complexity will arise from a shift in focus—from a reductive decomposition of the behavior to an emphasis on the principles governing its operation as an integrated complex system. 

Computational, thus, we primarily employ techniques from complexity science and machine learning, including dynamical systems theory, network control theory, and recurrent neural networks. To train and test our models, we utilize cross-sectional and longitudinal behavioral and psychological data, such as thought trajectories in mind-wandering experiments and symptom trajectories of patients with major depression. Additionally, we incorporate neuroimaging data (fMRI, DTI, T1, EEG) and neural stimulation techniques (ECT, tACS, etc.)