Dr. Feng Li is an Assistant Professor of Statistics in the School of Statistics and Mathematics at Central University of Finance and Economics in Beijing, China. Feng obtained his Ph.D. degree in Statistics from Stockholm University in 2013. His research interests include Bayesian computation, Econometrics and forecasting, and Distributed learning. His recent research output appeared in statistics and forecasting journals such as the International Journal of Forecasting and Statistical Analysis and Data Mining, AI journals such as Expert Systems with Applications, and medical journals such as BMJ Open.

He also served as the Associate Dean for School of Statistics and Mathematics at Central University of Finance and Economics from 2016.

Email: feng.li@cufe.edu.cn 
Phone: +86-(0)10-6177-6189

Education 🎓

Curriculum Vitae

Research Interests👨‍🔬

  • Bayesian computation, Econometrics and forecasting, Distributed learning

    • Working Papers⏳

    1. Distributed Forecasting for Ultra-long Time Series (with Xiaoqian Wang, Yanfei Kang and Rob J Hyndman)
    2. Déjà vu: forecasting with similarity (with Yanfei Kang, Evangelos Spiliotis, Fotios Petropoulos, Nikolaos Athiniotis, and Vassilios Assimakopoulos)
    3. Least squares approximation for a distributed system (with Xuening Zhu and Hansheng Wang)
    4. FFORMPP: Feature-based forecast model performance prediction (with Thiyanga S. Talagala and Yanfei Kang)
    5. Que será será? The uncertainty estimation of feature-based time series forecasts (with Xiaoqian Wang, Yanfei Kang and Fotios Petropoulos)

    Research Grants

    Selected Publications 🖺

    1. Xixi Li, Yanfei Kang, and Feng Li*. (2020). Forecasting with time series imaging, Expert Systems with Applications. 160(1)
    2. Megan GJaneway, Xiang Zhao, Max Rosenthaler, Yi Zuo, Kumar Balasubramaniyane, Michael Poulson, Miriam Neufeld, Jeffrey J. Siracuse, Courtney E. Takahashif, Lisa, Allee, Tracey Dechert, Peter A Burke, Feng Li, and Bindu Kalesan (2020). Clinical diagnostic phenotypes in hospitalizations due to self-inflicted firearm injury, Journal of Affective Disorders. (In Press).
    3. Bindu Kalesan, Siran Zhao, Michael Poulson, Miriam Neufeld, Tracey Dechert, Jeffrey J Siracuse, Yi Zuo, and Feng Li (2020). Intersections between firearm suicide, drug mortality and economic dependency in rural America, Journal of Surgical Research. 256, pp 96-102. Journal’s Cover Paper.
    4. Chengcheng Hao, Feng Li, and Dietrich von Rosen. (2020). A Bilinear Reduced Rank Model, Book chapter in Contemporary Experimental Design, Multivariate Analysis and Data Mining (Jianqing Fan and Jianxin Pan eds.), Springer.
    5. Yanfei Kang, Rob Hyndman, and Feng Li*. (2020). GRATIS: GeneRAting TIme Series with diverse and controllable characteristics, Statistical Analysis and Data Mining. 13(4), pp. 354-376.
    6. Hannah M Bailey, Yi Zuo, Feng Li, Jae Min, Krishna Vaddiparti, Mattia Prosperi, Jeffrey Fagan, Sandro Galea, and Bindu Kalesan. (2019). Changes in patterns of mortality rates and years of life lost due to firearms in the United States, 1999 to 2016: A joinpoint analysis. PLoS One 14(11): e0225223.
    7. 康雁飞、李丰（译）(2019). 预测：方法与实践（第2版）（Hyndman & Athanasopoulos 著. Forecasting: Principles and Practice）
    8. Feng Li and Zhuojing He. (2019). Credit Risk Clustering in a Business Group: Which Matters More, Systematic or Idiosyncratic Risk? Cogent Economics and Finance, 7(1632528).
    9. Feng Li and Yanfei Kang (2018) Improving forecasting performance using covariate-dependent copula models, International Journal of Forecasting, 34(3), pp. 456-476.
    10. Elizabeth C Pino, Yi Zuo, Camila Maciel De Olivera, Shruthi Mahalingaiah, Olivia Keiser, Lynn L Moore, Feng Li, Ramachandran S Vasan, Barbara E Corkey, and Bindu Kalesan. (2018) Cohort Profile: The MULTI sTUdy Diabetes rEsearch (MULTITUDE) Consortium, BMJ Open, 8(5): e020640.
    11. Feng Li (2016). 大数据分布式计算与案例（Distributed Statistical Computing for Big Data and Case Studies）, ISBN：9787300230276
    12. Feng Li. (2013). Bayesian Modeling of Conditional Densities. Ph.D. thesis, Department of Statistics, Stockholm University. ISBN: 978-91-7447-665-1
    13. Feng Li and Mattias Villani. (2013). Efficient Bayesian Multivariate Surface Regression, Scandinavian Journal of Statistics, 40(4), pp. 706-723
    14. Feng Li, Mattias Villani, and Robert Kohn. (2011). Modeling Conditional Densities using Finite Smooth Mixtures, Book chapter in Mixture Estimation and Applications (Mengersen, K.L., Robert, C. P. and Titterington, D.M. eds), Wiley.
    15. Feng Li, Mattias Villani, and Robert Kohn. (2010). Flexible Modeling of Conditional Distributions using Smooth Mixtures of Asymmetric Student T Densities, Journal of Statistical Planning and Inference, 140(12), pp. 3638-3654

    Awards 🌟