| Ge Qiyang postdoctoral researcher Research Institute of Intelligent Complex Systems, Fudan UniversityE-mail: qyge@fudan.edu.cn E-mail: qyge@fudan.edu.cn |
Biography
Ge Qiyang is engaged in researches on complex networks and deep learning, including computer vision, natural language processing, computer-aided medicine, data mining analysis, causal inference analysis, etc.
Education
2015.9-2022.6 Ph.D. Major of applied mathematics, School of Mathematical Sciences, Fudan University
2011.9-2015.7 B.Sc. Major of mathematics and applied mathematics, School of Mathematical Sciences, Fudan University
Representative Publications
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.
