A key challenge in understanding mental (dys)functions is their etiological and functional heterogeneity, and several multi-dimensional assessments have been proposed for their comprehensive characterization. However, such assessments require lengthy testing, which may hinder reliable and efficient characterization of individual differences due to increased fatigue and distraction especially in clinical populations. Computational modeling may address the challenge as it often provides more reliable measures of latent neurocognitive processes underlying observed behaviors and captures individual differences better than traditional assessments. However, even with a state-of-art hierarchical modeling approach, reliable estimation of model parameters still requires a large number of trials. Recent works suggest that Bayesian adaptive design optimization (ADO) is a promising way to address the challenges. With ADO, experimental design is optimized adaptively from trial to trial to extract the maximum amount of information about an individual’s characteristics. In this review, we first describe the ADO methodology and then summarize recent works demonstrating that ADO increases reliability and efficiency of latent neurocognitive measures. We conclude by discussing the challenges and future directions of ADO and propose we develop ADO-based computational fingerprints to reliably and efficiently characterize the heterogeneous profiles of psychiatric disorders.