Data Visualization

考虑养老院居民跌倒风险的检测任务和缓解措施的处方；监测在线青年活动，以确定潜在的精神健康障碍，并建议启动早期治疗的程序；或者找出城市生活质量差异的根源，并提出消除差异的可能解决方案。这些应用背后的关键概念是预测事件，了解预测的原因，并可能规定缓解措施。虽然预测是普遍的，有利于许多应用场景，从疾病预测到抵押贷款申请分析，直到最近，需要识别偏差和理解一个结果的原因成为一个关注，政府法规要求对自动化决策的解释。机器学习和视觉分析技术已经被提出来解决这个问题。然而，现有的解决办法孤立地考虑了这一过程的不同方面，例如偏见和解释，而很少支持根据预测规定行动的概念。拟议的研究将开发新的可视化分析方法，旨在设计预测和规范的工具和技术，以支持整个管道的分类信心，理解分类，并规定缓解措施。适当地解决这三个支柱带来了一些挑战，特别是设计可视化界面以促进用户参与，通过结合背景知识使分析过程更加可靠。拟议的研究将调查新的视觉隐喻和技术，通过(i)帮助标记过程纠正问题和识别数据偏差，（ii）帮助理解复杂的模型内部工作和审计结果，以及（iii）发展规范性视觉分析的新概念，直观地推荐通过预测发现的问题的潜在解决方案，将用户作为构建高质量分类模型的积极参与者。通过工业和学术合作，这项研究的结果将转化为新的应用，造福于不同的领域，包括健康监测、城市数据分析等，有助于将视觉分析推广到其他知识领域，并利用Dalhousie在数据使用、分析和解释方面的专业知识。

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.