We use visualization as a petri dish to study intelligence augmentation, or how can computational representations and software systems help amplify our cognition and creativity, while respecting our agency?
Our goal is to enable scalable and accurate Bayesian inference for rich probabilistic models by applying optimization techniques. We also aim to understand the connections between the two approaches of statistical inference: Bayesian and frequentist.
Doctoral student and recent MAD Design Fellow Jonathan Zong SM ’20 discusses a proposed framework to map how individuals can say “no” to technology misuses.
Open-source software by MIT MAD Fellow Jonathan Zong and others in the MIT Visualization Group reveals online graphics’ embedded data in the user’s preferred degree of granularity.
Selecting the right method gives users a more accurate picture of how their model is behaving, so they are better equipped to correctly interpret its predictions.
Researchers have created prototypes that enable screen-reader users to quickly and easily navigate through multiple levels of information in an online chart.
