Characterize human decision-making via behavioral task data
My research employs advanced statistical modeling to investigate human decision‐making in choice‐ and reward‑based behavioral tasks, with the aim of identifying and quantifying the distinctive decision patterns exhibited by individuals with mental health conditions.
Bian, Y., Guo, X., Wang, Y. (2025+). Joint Modeling for Learning Decision-Making Dynamics in Behavioral Experiments. [Arxiv]
Guo, X., Zeng, D., Wang, Y. (2025). HMM for Discovering Decision-Making Dynamics Using Reinforcement Learning Experiments. Biostatistics. [Paper] [Arxiv] [Code]
Guo, X., Zeng, D., Wang, Y. (2024). A Semiparametric Inverse Reinforcement Learning Approach to Characterize Decision Making for Mental Disorders. Journal of the American Statistical Association. [Paper] [Code]
Statistical and machine learning methods for modeling complex data structure
A core component of my research is to develop advanced statistical and machine learning methodologies for complex data structures—including high‑dimensional functional data (e.g., task EEG), multivariate time series (e.g., resting‑state EEG), and imaging (e.g., fMRI)—to construct predictive and causal models that uncover latent neural dynamics and identify biomarkers in cognitive neuroscience. Recently, I am working on joint modeling of neurophysiological (EEG/fMRI) and behavioral data to elucidate brain–behavior association.