This will be a two-part talk that presents two applications of human-in-the-loop analytics. The first part takes a traditional approach in eliciting judgement from experts to specify subjective priors in the context of uncertainty quantification of simulators. In particular, the approach applies and verifies a method called Reification (Goldstein and Rougier, 2008), where experts initiate uncertainty analyses by specifying a hypothetical, high-fidelity computer model. With this hypothetical model, we can decompose potential discrepancy between a given simulator and reality. The second part of the talk places experts in the middle of analyses via Bayesian Visual Analytics (House et al., 2015) so that experts may explore data and offer feedback continuously. For BaVA, we use reduced-dimensional visualizations within an interactive software so that experts may communicate their judgements by interacting with data. Based on interactions, we parameterize and specify feedback distributions'', rather than prior distributions, for analyses. We exemplify BaVA using a dataset about animals. To conclude, I hope to engage in an open discussion for how we can use BaVA in Uncertainty Quantification of Computer Models.