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

合作研究：PPoSS：大型：用于医疗保健计算筛查和监视的超大规模边缘学习的原理和基础设施

• 批准号: 2406572
• 负责人: Ting Wang
• 金额: $ 94.27万
• 依托单位: SUNY at Stony Brook
• 依托单位国家: 美国
• 项目类别: Continuing Grant
• 财政年份: 2023
• 资助国家: 美国
• 起止时间: 2023-11-01 至 2026-09-30
• 项目状态: 未结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2406572&HistoricalAwards=false
• 关键词: Collaborative; Research; PPoSS; LARGE; Principles

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, 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.

该项目研究了“计算筛查和监测（CSS）”这一全新的跨学科概念，利用边缘学习来检测疾病的早期指标，并监测人类受试者的个人和群体的健康变化。该项目的新颖之处在于它是一种数据驱动的范例，它彻底改变了对健康状况的理解、预测、干预、治疗和管理。该项目对个人、组织和医疗保健系统具有巨大的社会和经济效益：早期发现、先发制人的干预和管理可以大大提高护理质量，并为多个群体节省大量费用。每种疾病每年花费数千亿美元。研究人员设计、开发和评估 CSS 的原理和解决方案，这些原理和解决方案通过跨越四个维度的超大规模边缘学习实现：数据模式、健康状况和数据模式、人工智能/机器学习 (AI) /ML）算法设计遵循四个原则：利用规模和异质性、不确定性设计、作为一等公民的隐私以及作为规范的故障和攻击。研究人员将 1) 设计 AI/ML。用于学习极端规模的个人和人群的不同健康状况的数据模式和相关性的算法2）量化安全性、隐私保护和学习准确性之间权衡的理论界限，以防止对数据和模型的各种攻击；边缘和云； 3) 开发用于在不确定性下自动探索竞争性 AI/ML 方法的编程抽象，以及保护流处理完整性免遭敏感数据泄露和错误/恶意分析的系统机制；4) 设计神经架构和加速器以提高受限边缘的计算效率； 、使用有限训练集的数据效率以及利用 AutoML 的人员效率。该奖项反映了 NSF 的法定使命，并通过使用基金会的智力价值和更广泛的影响审查标准进行评估，被认为值得支持。