Collaborative Research: PPoSS: LARGE: Principles and Infrastructure of Extreme Scale Edge Learning for Computational Screening and Surveillance for Health Care

该项目研究了一种全新的跨学科概念“计算筛查和监测”，它利用边缘学习来检测疾病的早期指标，并监测个人和人群的健康变化。CS分析和解释人类受试者的连续和不同种类的生理和生理传感数据流，以产生关于他们健康状况的实时信息、知识和洞察。该项目的新颖性是一种数据驱动的范式，它彻底改变了对急性/传染性、慢性身体和心理疾病的理解、预测、干预、治疗和管理。该项目的影响为个人、组织和医疗保健系统带来了巨大的社会和经济利益：早期发现、先发制人的干预和管理可以极大地提高医疗质量，并为多种疾病节省巨额成本，每种疾病每年花费数千亿美元。研究人员设计、开发和评估由极大规模边缘学习支持的CS的原则和解决方案，涉及四个维度：数据模式、健康状况和数据模式、人工智能/机器学习(AI/ML)算法和模型以及个人/人口。设计遵循四个原则：利用规模和异质性，针对不确定性进行设计，将隐私作为一等公民，将错误和攻击作为规范。调查人员将1)设计AI/ML算法，用于在极端规模上学习数据模式以及个人和群体中不同健康状况的相关性；2)量化安全性、隐私保护和学习准确性之间的理论界限，以防止对边缘和云中的数据和模型的各种攻击；3)开发编程抽象，用于在不确定情况下自动探索竞争的AI/ML方法，以及保护流处理完整性免受敏感数据泄露和错误/恶意分析的系统机制；以及4)设计神经架构和加速器，以提高受约束边缘的计算效率，使用有限的训练集提高数据效率，并利用AutoML提高人类效率。该奖项反映了NSF的法定使命，并通过使用基金会的智力优势和更广泛的影响审查标准进行评估，被认为值得支持。

This project investigates a completely new cross-disciplinary concept of “Computational Screening and Surveillance (CSS)” that utilizes edge learning to detect early indicators of diseases, and monitor health changes in both individuals and populations. CSS analyzes and interprets continuous and heterogeneous physical and physiologic sensing-data streams of human subjects to produce real-time information, knowledge, and insights about their health status. The project’s novelty is a data-driven paradigm that revolutionizes the understanding, prediction, intervention, treatment, and management of acute/infectious, chronic physical and psychological diseases. The project’s impacts are enormous social and economic benefits to individuals, organizations, and the healthcare system: early detection, preemptive intervention and management can lead to greatly improved quality of care, and huge savings for multiple diseases each costing hundreds of billions of dollars every year.The investigators design, develop and evaluate principles and solutions for CSS enabled by extreme-scale edge learning spanning four dimensions: data modalities, health conditions and data patterns, Artificial Intelligence/Machine Learning (AI/ML) algorithms and models, and individuals/populations. The design is guided by four principles: exploit scale and heterogeneity, design for uncertainty, privacy as a first-class citizen, and faults and attacks as a norm. The investigators will 1) design AI/ML algorithms for learning data patterns and correlations for diverse health conditions in both individuals and populations at extreme scales; 2) quantify theoretical bounds on the tradeoffs between security, privacy protection, and learning accuracy in order to protect against various attacks on data and models at both the edge and cloud; 3) develop programming abstractions for automated exploration of competing AI/ML methods under uncertainty, and system mechanisms to protect stream processing integrity against sensitive data disclosure and faulty/malicious analytics; and 4) devise neural architectures and accelerators for computation efficiency at the constrained edge, data efficiency using limited training sets, and human efficiency utilizing AutoML.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.