CAREER: Data-Centric Evolutionary Contagion Models with Parallel and Quantum Parallel Computing

职业：具有并行和量子并行计算的以数据为中心的进化传染模型

• 批准号: 2236854
• 负责人: Andrew Holbrook
• 金额: $ 54.92万
• 依托单位: University of California-Los Angeles
• 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: 2023-07-01 至 2028-06-30
• 项目状态: 未结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2236854&HistoricalAwards=false
• 关键词: CAREER; Data; Centric; Evolutionary; Contagion

Global viral epidemics produce vast amounts of high-dimensional data indexed by the location and time each virus is observed. Scientists, businesses, governments and independent organizations want to learn from this data so they can understand basic biological mechanisms, invest capital, allocate aid and design coherent policy in a changing world. Evolutionary contagion (Evo-Con) models seek to identify and predict viral variants with heightened rates of spread by jointly modeling viral contagion and evolution. Analyzing spatial patterns of viral contagion is an area of immense scientific interest, but the task requires accounting for the nature of transportation networks that shape the global economy. As a result, big data applications become computationally intensive and benefit from high-performance computing. The project advances knowledge and utility of Evo-Con models in the context of massive amounts of complex, dynamic and geographically distributed data. In lockstep with these developments, the investigator will develop "Statistical Learning Goes Viral", a free-access MOOC (Massive Open Online Course), with concomitant free-access expository textbook and further expand his efforts building bridges between UCLA and key historically black colleges and universities. The investigator will combine theory, methods and computing in a way that facilitates high-impact data analysis and easy measurement of success. The PI will (1) develop a class of nonlinear and multivariate phylogenetic Hawkes processes that use autoregressive neural networks to maintain both flexibility and scalability. Nonlinear phylogenetic Hawkes process development will capitalize on experience building heavily hierarchical models for joint data, but a pragmatic approach will depart from previous Bayesian implementations to enhance scalability and prediction; and (2) adapt these stochastic process models to complex transportation patterns in a way that responds to spatial data precision by combining nonlinear dimension reduction with convolutional neural networks (CNN). When travel networks explicitly present themselves, the PI will leverage experience building spatial models that account for network structures by incorporating nonlinearities through graph CNNs. More radical within phylogeography, the PI will eschew explicit network representations with the help of spherical CNNs that build implicit representations of global spatial dependencies to model viral contagion with increased flexibility. By hierarchically combining (1) and (2) within the same factor graph, the joint data neural model will fully integrate spatial, genomic, and temporal data. Finally, the investigator will (3) construct high-performance computing techniques that leverage conventional and quantum resources to fit complex, multimodal and high-dimensional model geometries. Computational developments will go well beyond track-record inventions of parallelized Markov chain Monte Carlo algorithms that use graphics processing units and quantum computers.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.

全局病毒发作产生了大量的高维数据，并观察到每个病毒的位置和时间。科学家，企业，政府和独立组织希望从这些数据中学习，以便在不断变化的世界中了解基本的生物学机制，投资资本，分配援助和设计连贯的政策。进化传染（EVO-CON）模型旨在通过共同建模病毒传染和进化来识别和预测具有增长速率的病毒变异。分析病毒传染的空间模式是一个极大的科学兴趣领域，但任务需要考虑塑造全球经济的运输网络的性质。结果，大数据应用程序在计算密集程度上变得很密集，并受益于高性能计算。该项目在大量复杂，动态和地理分布的数据的背景下提高了EVO-CON模型的知识和实用性。在这些事态发展中，调查人员将开发“统计学习变得病毒”，这是一个自由访问的MOOC（大规模开放的在线课程），并带有随之而来的自由访问的说明性教科书，并进一步扩大了他在UCLA与主要历史上主要是黑人大学和大学之间建立桥梁的努力。研究者将以促进高影响数据分析并易于测量成功的方式将理论，方法和计算结合在一起。 PI（1）将开发一类非线性和多元系统发育鹰派工艺，这些过程使用自回归神经网络来维持灵活性和可扩展性。非线性系统发育霍克斯工艺发展将利用建立大量分层模型的联合数据的经验，但是务实的方法将从以前的贝叶斯实施中分开，以增强可扩展性和预测。 （2）通过将缩小非线性维度与卷积神经网络（CNN）相结合（CNN）来响应空间数据精度的方式，使这些随机过程模型适应复杂的传输模式。当旅行网络明确表现出来时，PI将利用通过图形CNN合并非线性来解释网络结构的空间模型的体验。在植物地理学中，PI更加激进，将在球形CNN的帮助下避开明确的网络表示，这些网络构建了全局空间依赖性的隐式表示，以模拟病毒传播并提高灵活性。通过层次结合（1）和（2）在同一因素图中，联合数据神经元模型将完全集成空间，基因组和临时数据。最后，研究人员将（3）构建高性能计算技术，这些计算技术利用常规和量子资源来适应复杂，多模式和高维模型几何形状。计算开发将远远超出使用图形处理单元和量子计算机的并行化马尔特·卡洛算法的轨道记录邀请。该奖项反映了NSF的法定任务，并被认为是通过基金会的知识分子优点和更广泛影响的审查标准通过评估来通过评估来获得的支持。