科学研究

暴乐，宾夕法尼亚州立大学统计学终身制（tenured）副教授，数据同化和预测技术中心副主任。其工作主要关注以数据和统计模型为基础，对政策制定提供定量参考。作为项目主要负责人，得到了联合国艾滋病署，世界卫生组织，全球基金，美国国立卫生研究院，以及盖茨基金会的多项基金奖项支持，总计超过500万美金。以解决实际问题为动力，暴乐教授对多种统计学方法做出了贡献，例如贝叶斯数据融合模型，复杂统计模型的诊断和数据价值分析，解决数据资源不均衡问题的小域估计方法；发表SCI论文40多篇，包括统计学和流行病学顶级刊物；担任Annals of Applied Statistics副主编；于宾夕法尼亚州立大学统计系获得Rosenberger优秀教学奖。

Marginalized groups are affected by individual, network, and structural risks, making them vulnerable to poor health and wellbeing. Because of social stigma and discrimination towards marginalized groups, it is difficult to measure or estimate the size and location of marginalized groups at any spatial resolution, especially a fine-scale resolution. In this study, we develop zero inflated models to estimate the marginalized groups population at the grid-cell level across countries of Eastern and Southern Africa which extends the presence-only data analysis literature. We also propose a subsampling procedure to substantially decrease computation time of zero-inflated models.