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

全球病毒流行产生了大量的高维数据，这些数据由每种病毒被观察到的位置和时间索引。科学家、企业、政府和独立组织希望从这些数据中学习，以便他们能够了解基本的生物机制，投资资本，分配援助，并在不断变化的世界中制定连贯的政策。进化传染（Evo-Con）模型试图通过联合建模病毒传染和进化来识别和预测具有更高传播率的病毒变体。分析病毒传播的空间模式是一个具有巨大科学意义的领域，但这项任务需要考虑塑造全球经济的交通网络的性质。因此，大数据应用程序变得计算密集型，并受益于高性能计算。该项目在大量复杂，动态和地理分布数据的背景下推进Evo-Con模型的知识和实用性。与这些发展同步，研究人员将开发“统计学习病毒”，一个免费访问的MOOC（大规模开放式在线课程），伴随着免费访问的临时教科书，并进一步扩大他的努力，在加州大学洛杉矶分校和主要的历史黑人学院和大学之间建立桥梁。调查人员将联合收割机理论，方法和计算的方式，促进高影响力的数据分析和容易衡量的成功。PI将（1）开发一类非线性和多变量系统发育Hawkes过程，使用自回归神经网络来保持灵活性和可扩展性。非线性系统发育Hawkes过程开发将利用为联合数据构建高度分层模型的经验，但务实的方法将偏离以前的贝叶斯实现，以增强可扩展性和预测性;（2）通过将非线性降维与卷积神经网络（CNN）相结合，以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运输模式。当旅游网络明确呈现自己时，PI将利用构建空间模型的经验，通过图CNN合并非线性来解释网络结构。在地理学中更激进的是，PI将在球形CNN的帮助下避开显式网络表示，这些CNN构建了全球空间依赖性的隐式表示，以更大的灵活性来模拟病毒传染。通过在同一因子图中分层组合（1）和（2），联合数据神经模型将完全集成空间、基因组和时间数据。最后，研究人员将（3）构建高性能计算技术，利用传统和量子资源来适应复杂，多模态和高维模型几何形状。计算发展将远远超出使用图形处理单元和量子计算机的并行马尔可夫链蒙特卡罗算法的创纪录发明。该奖项反映了NSF的法定使命，并通过使用基金会的知识价值和更广泛的影响审查标准进行评估，被认为值得支持。

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.