Big Data Predictive Phylogenetics with Bayesian Learning

基于贝叶斯学习的大数据预测系统发育 摘要 安德鲁·霍尔布鲁克博士是一位贝叶斯统计学家，在应用、理论和计算机方面具有广泛的背景。 计算数据科学。他提出的基于贝叶斯学习的大数据预测系统发育研究 贝叶斯系统发育模型与fl可伸缩、自激随机相结合的病毒暴发预测 流程模型。为医院信息系统开发和发布开源的高性能计算软件 模型将在大数据环境中促进快速流行病学fiELD反应。霍尔布鲁克博士将应用他的方法- 2015-2016年美洲寨卡病毒流行的重建，重点是确定关键 传播的地理途径和系统发育分支具有更强的传染性。 候选人：霍尔布鲁克博士是加州大学洛杉矶分校人类遗传学系的博士后学者。他赢得了他的 加州大学欧文分校统计系统计学博士，在此期间他完成了他的论文 几何贝叶斯，对抽象数学空间上的贝叶斯建模和计算的研究，以及 同时参与了加州大学欧文阿尔茨海默病研究中心的Sciencefic合作。 拟议的职业发展计划将使霍尔布鲁克博士成为数据密集型领域的独立领导者 病毒流行病学：1)促进课程作业以建立生物学领域知识；2)为霍尔布鲁克博士提供 有机会在加州大学洛杉矶分校教授马克·苏查德的专家监督下领导自己的项目， 医学博士、博士学位，以及3)允许霍尔布鲁克博士继续致力于定量病毒流行病学研究 转到了教职员工承诺。 导师：在fi颁奖期的前三年，霍尔布鲁克博士将与苏查德教授密切合作， 继续他们目前的每周例会计划。苏查德教授是贝叶斯物理学领域的一流专家。 遗传学和高性能统计计算；以他的医学背景，苏查德教授将建议 霍尔布鲁克博士在病毒流行病学领域知识的扩展。作为二级导师，克里斯蒂安教授 斯克里普斯研究所的安德森博士将就他的统计学的有效应用向霍尔布鲁克博士提供建议 以及2015-2016年寨卡病毒疫情的计算方法。霍布鲁克博士和霍尔布鲁克教授。苏查德和 安徒生将在博士后阶段结束后继续他们的合作。 研究：贝叶斯系统发育学成功重建进化史但未能预测病毒 散开。自我激动点过程缺乏生物学洞察力，也无法解释地理网络 扩散的影响。目标1解决这两种互补的病毒流行病学建模技术中的Defi相关性 通过创新一个组合模型，在该模型中，系统发育和自兴奋成分相互支持。 Aim 2通过发布开源的大规模并行计算软件使广泛采用成为现实 适用于大数据分析。AIM 3重建2015-2016年寨卡疫情，学习关键地理路线 传播和IdentifiEs系统发育分支具有更强的传染性。

Big Data Predictive Phylogenetics with Bayesian Learning Abstract Andrew Holbrook, Ph.D., is a Bayesian statistician with a broad background in applied, theoretical and compu- tational data science. His proposed research Big Data Predictive Phylogenetics with Bayesian Learning tackles viral outbreak forecasting by combining Bayesian phylogenetic modeling with flexible, `self-exciting' stochastic process models. The development and publication of open-source, high-performance computing software for his models will facilitate fast epidemiological field response in a big data setting. Dr. Holbrook will apply his method- ology to the reconstruction of the 2015-2016 Zika virus epidemic in the Americas, focusing on identifying key geographical routes of transmission and phylogenetic clades with enhanced infectiousness. Candidate: Dr. Holbrook is Postdoctoral Scholar at the UCLA Department of Human Genetics. He earned his Ph.D. in Statistics from the Department of Statistics at UC Irvine, during which time he completed his dissertation Geometric Bayes, an investigation into Bayesian modeling and computing on abstract mathematical spaces, and simultaneously participated in scientific collaborations at the UC Irvine Alzheimer's Disease Research Center. The proposed career development plan will establish Dr. Holbrook as an independent leader in data intensive viral epidemiology by 1) facilitating coursework to build biological domain knowledge, 2) affording Dr. Holbrook the opportunity to lead his own project while remaining under the expert oversight of UCLA Prof. Marc Suchard, M.D., Ph.D., and 3) allowing Dr. Holbrook to continue his focus on quantitative viral epidemiology once he has moved to a faculty commitment. Mentors: During the first three years of the award period, Dr. Holbrook will work closely with Prof. Suchard, continuing their current schedule of weekly meetings. Prof. Suchard is a leading expert in both Bayesian phylo- genetics and high-performance statistical computing; and with his medical background, Prof. Suchard will advise Dr. Holbrook in his expansion of domain knowledge in viral epidemiology. As secondary mentor, Prof. Kristian Andersen, Ph.D., of the Scripps Institute will advise Dr. Holbrook in the impactful application of his statistical and computational methodologies to the 2015-2016 Zika virus epidemic. Dr. Holbrook and Profs. Suchard and Andersen will maintain their collaborations after the postdoctoral period. Research: Bayesian phylogenetics successfully reconstructs evolutionary histories but fails to predict viral spread. Self-exciting point processes are devoid of biological insight and fail to account for geographic networks of diffusion. Aim 1 addresses deficiencies in these two complementary viral epidemiological modeling techniques by innovating a combined model where the phylogenetic and self-excitatory components support each other. Aim 2 makes widespread adoption a reality by publishing open-source, massively parallel computing software suitable for big data analysis. Aim 3 reconstructs the 2015-2016 Zika epidemic, learns key geographical routes of transmission and identifies phylogenetic clades with enhanced infectiousness.