Data Visualization

Consider the tasks of detecting risks of residents' falls in nursing homes and the prescription of mitigation actions; monitoring online youth activities to identify potential mental health disorders and suggest procedures to trigger early treatments; or identifying the roots for the differences in cities' quality of life and indicate possible solutions to equate the discrepancies.The key concept underlying these applications is predicting an event, understanding the reasons for the prediction, and potentially prescribing mitigation actions. Although prediction is pervasive, benefiting many application scenarios, from disease prognosis to mortgage application analysis, only recently the need for identifying biases and understanding the reasons for an outcome became a concern, with governmental regulations requiring explanations about automated decisions.Machine learning and visual analytics techniques have been proposed to address this issue. However, existing solutions consider the different aspects of this process, e.g., bias and explanation, in isolation while rarely support the concept of prescribing actions based on a prediction. The proposed research will develop new visual analytics approaches aiming to devise predictive and prescriptive tools and techniques to support the whole pipeline of classifying with confidence, understanding a classification, and prescribing mitigation actions.Appropriately addressing these three pillars imposes several challenges, especially the design of visual interfaces to facilitate user involvement, making the analytical process more reliable by incorporating background knowledge. The proposed research will investigate new visual metaphors and techniques to place users as active players in building high-quality classification models by (i) aiding the labeling process to correct problems and identifying data biases, (ii) helping in understanding complex model inner workings and auditing results, and (iii) developing the novel concept of prescriptive visual analytics, visually recommending potential solutions to problems detected through predictions.With industrial and academic collaborations, the results of this research will be translated into novel applications to benefit different domains, including health monitoring, urban data analysis, among others, helping to promote visual analytics into other knowledge domains and leveraging Dalhousie's expertise in the use, analysis, and interpretation of data.