External ventricular drain (EVD) is a common, yet challenging neurosurgical procedure of placing a catheter into the brain ventricular system that requires prolonged training for surgeons to improve the catheter placement accuracy. In this article, we introduce NeuroLens, an Augmented Reality (AR) system that provides neurosurgeons with guidance that aids them in completing an EVD catheter placement. NeuroLens builds on prior work in AR-assisted EVD to present a registered hologram of a patient's ventricles to the surgeons, and uniquely incorporates guidance on the EVD catheter's trajectory, angle of insertion, and distance to the target. The guidance is enabled by tracking the EVD catheter. We evaluate NeuroLens via a study with 33 medical students and 9 neurosurgeons, in which we analyzed participants' EVD catheter insertion accuracy and completion time, eye gaze patterns, and qualitative responses. Our study, in which NeuroLens was used to aid students and surgeons in inserting an EVD catheter into a realistic phantom model of a human head, demonstrated the potential of NeuroLens as a tool that will aid and educate novice neurosurgeons. On average, the use of NeuroLens improved the EVD placement accuracy of the year 1 students by 39.4%, of the year 2$-$-4 students by 45.7%, and of the neurosurgeons by 16.7%. Furthermore, students who focused more on NeuroLens-provided contextual guidance achieved better results, and novice surgeons improved more than the expert surgeons with NeuroLens's assistance.

We develop a novel measurement framework based on Gaussian process coregionalization model to address a long-lasting debate in psychometrics: whether psychological features like personality share a common structure across the population or vary uniquely for individuals. We propose idiographic personality Gaussian process (IPGP), an intermediate model that accommodates both shared trait structure across individuals and "idiographic" deviations. IPGP leverages the Gaussian process coregionalization model to conceptualize responses of grouped survey batteries but adjusted to non-Gaussian ordinal data, and exploits stochastic variational inference for latent factor estimation. Using both synthetic data and a novel survey, we show that IPGP improves both prediction of actual responses and estimation of intrapersonal response patterns compared to existing benchmarks. In the survey study, IPGP also identifies unique clusters of personality taxonomies, displaying great potential in advancing individualized approaches to psychological diagnosis.