在站博士后:葛启阳(Ge, Qiyang)

发布者:孙毅 发布时间:2021-09-01 浏览次数:313

葛启阳


复旦大学智能复杂体系基础理论与关键技术实验室博士后,上海市超博


E-mail: qyge@fudan.edu.cn


简介

从事复杂网络和深度学习的研究工作,包括计算机视觉、自然语言处理、计算机辅助医疗、数据挖掘分析、因果推断分析等方向。

教育经历

2015.09-2022.06 复旦大学数学科学学院 应用数学专业 博士学位 导师:林伟教授

2011.09-2015.07 复旦大学数学科学学院 数学与应用数学专业 学士学位

工作经历

2015.09-2022.06 复旦大学数学科学学院 应用数学专业 博士学位 导师:林伟教授


代表成果

Preprints

1. Hu Z, Ge Q, Luo L, and et al. Population vaccine effectiveness and its implication for control of the spread of COVID-19 in the US. medRxiv, 2021.

2. Ge Q, Hu Z, Zhang K, Li S, Wei L, and et al. Recurrent neural reinforcement learning for counterfactual evaluation of public health interventions on the spread of Covid-19 in the world. medRxiv, 2020: 2020.07.

3. Hu Z, Ge Q, and et al. Artificial intelligence forecasting of Covid-19 in China. arXiv preprint arXiv: 2002.07112, 2020.

4. Hu Z, Ge Q, and et al. Spread of Covid-19 in the United States is controlled. medRxiv, 2020.

Papers in Refereed Journals

1. Ge Q, Hu Z, Li S, Lin W, and et al. A novel intervention recurrent autoencoder for real time forecasting and non-pharmaceutical intervention selection to curb the spread of Covid-19 in the world. Statistics and Its Interface, 2021, 14(1):37-47.

2. Ge Q, Huang X, Fang S, Guo S, Liu Y, Lin W, and et al. Conditional generative adversarial networks for individualized treatment effect estimation and treatment selection. Frontiers in Genetics, 2020, 11.

3. Hu Z, Ge Q, and et al. Evaluating the effect of wearing face masks by the general population on mitigating the spread of COVID-19. Epidemiology International Journal. 2020, 4I2.

4. Hu Z, Ge Q, Li S, and et al. Forecasting and evaluating multiple interventions for COVID-19 worldwide. Frontiers in Artificial Intelligence, 2020, 3:41.

5. Liu Y, Li Z, Ge Q, and et al. Deep feature selection and causal analysis of Alzheimer's disease. Frontiers in Neuroscience, 2019, 13:1198.

Academic Presentations

1. Xiong M, Ge Q, Liu Y and Huang X. (2019). Deep learning for estimation of individualized treatment effects with multiple sources. JSM 2019, July 27-August 1, 2019, Denver, Colorado.

2. Xiong M, Jiao R, Kiu Y, Ge Q, Chen X and Jinying Zhao. (2018). Association or Causation. 4th International Conference on Big Data and Information Analytics, December 17-19, 2018, Houston, Texas.

3. Xiong M, Liu Y, Li Z, Ge Q, Lin N. (2019). Intelligent algorithms for genetic-imaging data analysis. 2019 Annual ROSMAP Investigator Meeting. April 14-16, Chicago.

4. Liu Y, Ge Q, Lin N, Peng WJ, Jiao R, Wu X, and Xiong M. (2018). Image Segmentation via deep learning and causal inference. JSM2018, July 28 - August 2, 2018, Vancouver, Canada.