生物统计专业
Jiacheng Wu
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可服务区域/国家: 美国
已助力 18 人实现梦想 准时率:98.9%
综合评分:
简介
我叫吴嘉成,目前在华盛顿大学读生物统计博士,之前在耶鲁大学拿到了生物统计硕士。 我对统计学科充满热情,因为它的研究方法可以应用到很多领:生物,传染病学,金融,经济,社会科学,等等。我曾经在Journal of American Statistical Association, Statistics in Medicine, Journal of graphical and computational statistics 上发表论文,对机器学习和统计方法有着深刻的了解。 对统计专业的申请和就业非常熟悉。我也很想帮助对统计专业感兴趣的同学申请学校,实现自己的梦想。
教育背景
华盛顿大学
/博士
/生物统计
耶鲁大学
/硕士
/生物统计
同济大学
/本科
/应用数学
专长
个性化

2015年6月到2016年8月

名称:Exposure, hazard, and survival analysis of diffusion on social networks

https://onlinelibrary.wiley.com/doi/abs/10.1002/sim.7658

published in statistics in medicine.

职位:第一作者,主要研究人

描述:Social network is important for the diffusion of ideas and products through human societies. People will tend to influence their friends on their decision to use certain products. We are interested in using the network information to maximize the adoption of products on social networks. Due to the resource constraints, the questions lie in which individuals should we target initially so that more people can use the products. My collaborators conducted randomized clinical trials in Honduras to answer this question for the diffusion of multivitamin tablets. We use a survival analysis framework to analyze this type of data. We find that targeting the most influential people in the network will lead to the maximum adoption of the multivitamin tablets. This conclusion will help future researchers to better allocate resources for the adoption of products to help people in developing countries    

 

名称:Flexible and interpretable models for survival data
职位:第一作者,主要研究人
描述:As data sets continue to increase in size, there is growing interest in methods for prediction that are both flexible and interpretable. A flurry of recent work on this topic has focused on additive modeling in the regression setting, and in particular, on the use of data-adaptive non-linear functions that can be used to flexibly model each covariate's effect, conditional on the other features in the model. In this paper, we extend this recent line of work to the survival setting. We develop an additive Cox proportional hazards model, in which each additive function is obtained by trend filtering, so that the fitted functions are piece-wise polynomial with adaptively-chosen knots. An efficient proximal gradient descent algorithm is used to fit the model. We demonstrate its performance in simulations and in application to a primary biliary cirrhosis (PBC) dataset.
研究任务:

  1. Develop penalized regression (machine learning) models to fit piecewise polynomial in Cox proportional hazard model.
  2. Model covariate effects flexibly using trend filtering penalty.
  3. Knots are chosen adaptively based on the data.

 

获奖:

名称:最佳论文奖

颁奖机构:Yale University

实习经历
2014年二月到六月 公司:通用电气金融(GE capital) 职位:数据分析员 内容:用SAS和SQL 获取数据,分析数据,使用logistic regression模型,预测信用卡分数。
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