Big Data Predictive Phylogenetics with Bayesian Learning

大数据预测系统发生学与贝叶斯学习 摘要 安德鲁·霍尔布鲁克博士，是一个贝叶斯统计学家，在应用，理论和计算机方面有着广泛的背景， 实验数据科学他提出的研究大数据预测系统发生学与贝叶斯学习解决 结合贝叶斯系统发育模型和灵活的“自激”随机 过程模型开发和出版开源的高性能计算软件， 模型将有助于在大数据环境中快速应对流行病学领域。霍尔布鲁克医生会用他的方法 2015-2016年美洲寨卡病毒疫情的重建，重点是确定关键 传播的地理途径和具有增强传染性的系统发育分支。 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 博士在加州大学欧文分校统计系的统计学，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， 同时参加了加州大学欧文分校阿尔茨海默病研究中心的科学合作。 拟议的职业发展计划将使Holbrook博士成为数据密集型行业的独立领导者。 病毒流行病学通过1）促进课程，以建立生物领域的知识，2）提供博士霍尔布鲁克 有机会领导自己的项目，同时在加州大学洛杉矶分校教授马克苏查德的专家监督下， 医学博士，哲学博士、3）允许霍尔布鲁克博士继续他的重点定量病毒流行病学，一旦他 转为教职员工 导师：在奖励期的前三年，Holbrook博士将与Suchard教授密切合作， 继续他们目前的每周会议日程。Suchard教授是Bayesian phylo和 遗传学和高性能统计计算;凭借他的医学背景，Suchard教授将建议 博士霍尔布鲁克在他的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 安德森博士，斯克里普斯研究所将建议霍尔布鲁克博士在他的统计的影响力应用 2015-2016年寨卡病毒流行的计算方法。Holbrook博士和教授Suchard和 安德森将在博士后期间后保持他们的合作。 研究：贝叶斯遗传学成功地重建了进化历史，但未能预测病毒 传播.自激点过程缺乏生物学的洞察力，无法解释地理网络 扩散。目标1解决了这两种互补的病毒流行病学建模技术的缺陷 通过创新一种组合模型，其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， 适合大数据分析。目标3重建2015-2016年寨卡疫情，了解关键地理路线 传播和识别系统发育分支具有增强的传染性。

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.