Interpretable Time Series Representation Learning via Disentanglement and Domain Priors

世界卫生组织预计，到2030年，医疗服务提供者的缺口将达到990万。考虑到65岁以上的世界人口同时增加，这种短缺尤其令人担忧。据估计，到2050年，加拿大老年人口将占全国人口的23%。气候变化的冲击加剧了极端天气事件、干旱、洪水和火灾季节的增加，导致全球范围内生命和生计的损失，医疗保健和气候变化构成了人类在地球上所面临的一些最大挑战。虽然表面上不同，但医疗保健数据（例如患者病史）和气候变化数据（例如温室气体排放数据）都包含随时间变化的信息（数据样本），正式名称为“时间序列数据”。因此，分析时间序列数据中的模式对于检测、监测和预测事件以改善医疗保健结果和制定缓解气候变化的战略至关重要。我的研究项目将建立具有时间推理能力的自动化人工智能（AI）模型，以解决这些应用领域的关键挑战，推进时间序列分析的最新技术。我的研究项目将通过开发用于培训、监测、诊断和决策的基于人工智能的自动化辅助医疗解决方案来帮助扩展医疗保健服务，促进向偏远和边缘化社区提供高质量的医疗保健。在气候变化的背景下，我的项目将通过开发能够检测和分析温室气体排放（气候变化的主要原因之一）的自动化监测和预测技术，帮助工业实现排放目标。这些解决方案还将用于模拟和预测极端天气事件和森林火灾蔓延的动态，从而最大限度地减少对生物多样性的破坏和生命损失。该项目将通过开发生物和物理启发的模型来解决这些紧迫的现实问题，为从时间序列中提取模式提供有意义的方法。作为该研究计划的一部分，对时间序列数据的概念性理解将取得根本性进展，通过增加可解释性（以及最终的可信度）和公平性，使人工智能在这些关键领域的可靠应用成为可能。此外，我的项目将培养学生在机器学习方面的专业知识，以应用于现实世界，培养具有高要求的人工智能和时间序列分析技能的毕业生。这些学生将深入了解人工智能的理论基础和实际意义，通过研究、创业和工程，推动加拿大下一波负责任的创新。

The World Health Organization projects a shortage of 9.9 million medical providers by 2030. This shortage is especially alarming considering the simultaneous increase in the world population above the age of 65, with elderly Canadians estimated to comprise 23% of the national population by 2050. Compounded by the onslaught of climate change with increased extreme weather events, droughts, flooding, and fire seasons, leading to the loss of lives and livelihoods across the globe, healthcare and climate change pose some of the greatest challenges that humans have ever faced during their time on the planet. Although different at the surface, both healthcare data (e.g., patient medical history) and climate change data (e.g., greenhouse gas emission data) comprise information (data samples) evolving over time, formally known as "time-series data". Consequently, the analysis of patterns in time-series data is critical to the detection, monitoring and prediction of events to improve healthcare outcomes and develop strategies to mitigate climate change. My research program will build automated artificial intelligence (AI) models with temporal reasoning capabilities to tackle critical challenges in these application areas, advancing the state-of-the-art in time-series analysis. My research program will help scale healthcare services by developing AI-based automated assistive healthcare solutions for training, monitoring, diagnostics, and decision-making, facilitating the provision of quality healthcare to remote and marginalized communities. In the context of climate change, my program will help industries meet emission goals by developing automated monitoring and forecasting techniques that can detect and analyze greenhouse gas emissions, one of the primary causes of climate change. These solutions will also be used to model and forecast the dynamics of extreme weather events and forest fire spread that can be used to minimize damage to biodiversity and loss of life. The program will address these pressing real-world problems by developing bio- and physics-inspired models to provide meaningful ways to extract patterns from time-series. The conceptual understanding of time-series data developed as part of this research program will make fundamental advances to enable reliable application of AI in such critical areas via added interpretability (and ultimately trustworthiness) and fairness. Additionally, my program will train students to develop expertise in machine learning for real-world applications, producing graduates with high-demand skills in AI and time-series analysis. These students will develop in-depth understandings of both the theoretical underpinnings and the practical implications of AI to drive the next wave of responsible innovation in Canada via research, entrepreneurship, and engineering.