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，这些数据以观察到每种病毒的地点和时间为索引。科学家、企业、政府和独立组织希望从这些数据中学习，这样他们就可以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设计连贯的政策。进化传染（Evo-Con）模型试图通过联合模拟病毒传染和进化来识别和预测具有较高传播率的病毒变体。分析病毒传染的空间模式是一个具有巨大科学兴趣的领域，但这项任务需要考虑影响全球经济的交通网络的性质。因此，大数据应用变得计算密集型，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，提高了Evo-Con模型的知识和实用性。​研究者将把理论、方法和计算结合起来，以促进高影响力的数据分析和容易的成功测量。PI将(1)开发一类非线性和多元系统发育Hawkes过程，该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，但实用的方法将与以前的贝叶斯实现不同，以增强可扩展性和预测性；(2)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，将这些随机过程模型应用于复杂的运输模式。当旅行网络明确地呈现自己时，PI将利用经验建立空间模型，通过图形cnn结合非线性来解释网络结构。在系统地理学中更激进的是，PI将在球形cnn的帮助下避免明确的网络表示，球形cnn建立全球空间依赖性的隐式表示，以增加灵活性来模拟病毒传染。通过将(1)和(2)在同一因子图内分层组合，联合数据神经模型将充分整合空间、基因组和时间数据。最后，研究者将(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。计算的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记录发明。该奖项反映了美国国家科学基金会的法定使命，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.