统计学博士，副教授，硕士研究生导师
办公地址：北京市昌平区沙河高教园正规买球站（沙河校区）1号楼，邮编102206
个人主页：https://feng.li
实验室：https://kllab.org
电子邮件：feng.li@cufe.edu.cn
李丰，内蒙古鄂尔多斯人，现任正规买球站(中国)有限公司副教授、硕士研究生导师。李丰老师博士毕业于瑞典斯德哥尔摩大学，博士论文曾获瑞典2013年统计学最佳论文奖（Cramér Prize），在经济统计、预测方法、大数据分布式学习等方面取得较好的学术积累，主持和参与多项国家级基金项目。李丰老师和他的团队开发具有自主产权、适用于海量数据的开源统计和大数据分析软件。
李丰老师近3年研究成果发表在统计学期刊Journal of Computational and Graphical Statistics，Journal of Business and Economic Statistics,经济与预测期European Journal of Operational Research, International Journal of Forecasting, Journal of Business Research, Journal of the Operational Research Society,人工智能与数据挖掘期刊Expert Systems with Applications, Statistical Analysis and Data Mining和医学期刊BMJ Open, Journal of Surgical Research, Journal of Affective Disorders等。他同时著有Bayesian Modeling of Conditional Densities和《大数据分布式计算与案例》《统计计算》，译著《预测：方法与实践》一书。
【研究方向】
统计学习·计量经济学·预测方法·大数据分布式学习
*李丰老师热忱欢迎优秀校内本科生（①本校拟录取应用统计学硕专硕和数量经济学硕保研员工②大三以上学有余力同学）加入研究团队。研究团队为优秀本科生提供每周一对一指导、参与KLLAB核心科研项目的机会、与国内外知名大学合作研究的机会。
【教育背景】
●2008—2013 统计学 博士，瑞典斯德哥尔摩大学统计学系
博士论文：贝叶斯条件密度建模（获瑞典统计学最佳博士论文，Cramér prize）
●2007—2008 统计学 硕士，瑞典达拉那大学统计学系
●2003—2007 统计学 本科，中国人民大学正规买球站（获北京市优秀毕业生）
【工作经历】
●2013年9月至今——正规买球站(中国)有限公司 讲师、副教授
●2016年6月至2023年3月——正规买球站(中国)有限公司副经理、数学教学部副主任
【科研课题】
●国家社会科学基金一般项目（22BTJ028）：全局模型视角下的复杂分层经济预测研究。2022/09-今，项目负责人，在研。
●阿里巴巴创新研究计划：电商场景下的复杂时间序列预测问题研究。2021/09-今、项目负责人，在研。
●国家自然科学基金面上项目（82074282）：中医药临床疗效评价中基于目标值法的单臂临床研究方法体系的构建。2021/01-2023/12、主要参与人。
●国家自然科学基金青年项目（11501587）：贝叶斯柔性密度方法及其在高维金融数据中的应用。2016/01-2018/12、项目负责人。
●教育部基金项目：贝叶斯弹性高维密度方法在复杂数据的研究。2014/01-2016/12、项目负责人。
●国家自然科学基金青年项目（11401603）：复发事件的均值模型和纵向数据的分位数回归的统计与推断。2015/01-2017/12、参加。
●国家自然科学基金青年项目（71401192）：公司财务困境预警模型研究:基于财务波动信息的区间数据刻画方法、2015/01-2017/12、参加。
●国家自然科学基金面上项目（71473279）：货币总量转向信用总量:全球虚拟经济与实体经济背离机理与宏观政策应对、2015/01-2017/12、参加。
【学术论文与专著】
[30]. Xiaoqian Wang, Rob J. Hyndman,Feng Li and Yanfei Kang (2023),"Forecast combinations: an over 50-year review",International Journal of Forecasting. (In Press)
[29]. Li Li, Yanfei Kang, Fotios Petropoulos and Feng Li(2023),"Feature-based intermittent demand forecast combinations: accuracy and inventory implications",International Journal of Production Research. (In Press)
[28]. Bohan Zhang, Yanfei Kang, Anastasios Panagiotelis and Feng Li(2023),"Optimal reconciliation with immutable forecasts",European Journal of Operational Research. (In Press)
[27]. Rui Pan, Tunan Ren, Baishan Guo,Feng Li, Guodong Li and Hansheng Wang (2022),"A Note on Distributed Quantile Regression by Pilot Sampling and One-Step Updating",Journal of Business and Economic Statistics. Vol. 40(4), pp. 1691-1700.
[26]. Xiaoqian Wang, Yanfei Kang, Fotios Petropoulos and Feng Li(2022),"The uncertainty estimation of feature-based forecast combinations",Journal of the Operational Research Society. Vol. 73(5), pp. 979-993.
[25]. Zhiru Wang, Yu Pang, Mingxin Gan, Martin Skitmore and Feng Li(2022),"Escalator accident mechanism analysis and injury prediction approaches in heavy capacity metro rail transit stations",Safety Science. Vol. 154, pp. 105850.
[24]. Li Li, Yanfei Kang and Feng Li(2022),"Bayesian forecast combination using time-varying features",International Journal of Forecasting. (In Press)
[23]. Xiaoqian Wang, Yanfei Kang, Rob J. Hyndman and Feng Li(2022),"Distributed ARIMA models for ultra-long time series",International Journal of Forecasting. (In Press)
[22]. Matthias Anderer and Feng Li(2022),"Hierarchical forecasting with a top-down alignment of independent level forecasts",International Journal of Forecasting. Vol. 38(4), pp. 1405-1414.
[21]. Fotios Petropoulos, ...,Feng Li, et al (2022),"Forecasting: theory and practice",International Journal of Forecasting. Vol. 38(3), pp. 705-871.
[20]. Thiyanga S. Talagala,Feng Li and Yanfei Kang (2022),"FFORMPP: Feature-based forecast model performance prediction",International Journal of Forecasting. Vol. 38(3), pp. 920-943.
[19]. Yanfei Kang, Wei Cao, Fotios Petropoulos and Feng Li(2022),"Forecast with Forecasts: Diversity Matters",European Journal of Operational Research. Vol. 31(1), pp. 180-190.
[18]. Xuening Zhu,Feng Li and Hansheng Wang (2021),"Least-Square Approximation for a Distributed System",Journal of Computational and Graphical Statistics. Vol. 30(4), pp. 1004-1018.
[17]. Yanfei Kang, Evangelos Spiliotis, Fotios Petropoulos, Nikolaos Athiniotis,Feng Li and Vassilios Assimakopoulos (2021),"Déjà vu: A data-centric forecasting approach through time series cross-similarity",Journal of Business Research. Vol. 132(2021), pp. 719-731.
[16]. Megan G. Janeway, Xiang Zhao, Max Rosenthaler, Yi Zuo, Kumar Balasubramaniyan, Michael Poulson, Miriam Neufeld, Jeffrey J. Siracuse, Courtney E. Takahashi, Lisa Allee, Tracey Dechert, Peter A. Burke,Feng Li and Bindu Kalesan (2021),"Clinical diagnostic phenotypes in hospitalizations due to self-inflicted firearm injury",Journal of Affective Disorders. Vol. 278, pp. 172-180.
[15].康雁飞and李丰(2020),"预测：方法与实践"在线出版.
[14].康雁飞and李丰(2020),"统计计算"在线出版.
[13]. Bindu Kalesan, Siran Zhao, Michael Poulson, Miriam Neufeld, Tracey Dechert, Jeffrey J. Siracuse, Yi Zuo and Feng Li(2020),"Intersections of firearm suicide, drug-related mortality, and economic dependency in rural America",Journal of Surgical Research. Vol. 256, pp. 96-102.
[12]. Xixi Li, Yanfei Kang and Feng Li(2020),"Forecasting with time series imaging",Expert Systems with Applications. Vol. 160, pp. 113680.
[11]. Chengcheng Hao,Feng Li and Dietrich von Rosen (2020),"A Bilinear Reduced Rank Model", InContemporary Experimental Design, Multivariate Analysis and Data Mining. Springer Nature.
[10]. Yanfei Kang, Rob J. Hyndman and Feng Li(2020),"GRATIS: GeneRAting TIme Series with diverse and controllable characteristics",Statistical Analysis and Data Mining. Vol. 13(4), pp. 354-376.
[9]. 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. Vol. 14(11)
[8].Feng Li and Zhuojing He (2019),"Credit risk clustering in a business group: which matters more, systematic or idiosyncratic risk?",Cogent Economics & Finance. , pp. 1632528.
[7]. 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. Vol. 8(5), pp. e020640.
[6].Feng Li and Yanfei Kang (2018),"Improving forecasting performance using covariate-dependent copula models",International Journal of Forecasting. Vol. 34(3), pp. 456-476.
[5].李丰(2016),"大数据分布式计算与案例"中国人民大学出版社.
[4].Feng Li(2013),"Bayesian Modeling of Conditional Densities". Thesis at:Department of Statistics, Stockholm University.
[3].Feng Li and Mattias Villani (2013),"Efficient Bayesian Multivariate Surface Regression",Scandinavian Journal of Statistics. Vol. 40(4), pp. 706-723.
[2].Feng Li, Mattias Villani and Robert Kohn (2011),"Modeling Conditional Densities Using Finite Smooth Mixtures", InMixtures: estimation and applications. , pp. 123-144. John Wiley & Sons Inc, Chichester.
[1].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. Vol. 140(12), pp. 3638-3654.
【主要讲授课程】
●统计计算（正规买球站精品实验课）
●大数据分布式计算（应用统计大数据专业、数据科学与大数据专业核心课程）
●Python程序设计与数据挖掘（MBA、金融、会计核心课程）
●数据科学工具（正规买球站核心通识课）
【学术兼职】
●2022—今北京统计学会理事
●2018—今全国工业统计学教学研究会常务理事、青年统计学家论坛副秘书长
●2014—今 中国统计教学学会高等教育分会副秘书长
【评审兼职】
●国家外国专家项目评审专家
●联合国大数据竞赛导师与评审专家
●教育部学位论文评审专家
●全国应用统计专业学位研究生案例大赛评审专家。
●担任Journal of Business and Economics Statistics,International Journal of Forecasting,Omega - The International Journal of Management Science,Pattern Recognition,Computational Statistics and Data Analysis, Neurocomputing,Information Sciences,Journal of Statistical Computation and Simulation,Australian & New Zealand Journal of Statistics,Journal of Official Statistics,Quantitative FinanceStudies in Nonlinear Dynamics & Econometrics, The European Journal of Finance等期刊审稿人
【部分受邀学术报告】
●The41stInternational Symposium on Forecasting,27-28June, 2021.
●The 2021World Meeting of the International Society for Bayesian Analysis, Jun28—July 2, 2021.
●The40thInternational Symposium on Forecasting,26-28Oct, 2020.
●Twelfth International Conference on Monte Carlo Methods and Application (MCM 2019), Sydney, Australia, July 8-12, 2019.
●The39thInternational Symposium on Forecasting, 16-19 June in Thessaloniki, Greece.
●IMS-China International Conference on Statistics and Probability, June 28 – July 1, 2017, Nanning, China.
●The 1stInternational Conference on Econometrics and Statistics, Hong Kong, 15-17 June 2017.
●The 2016 World Meeting of the International Society for Bayesian Analysis, Jun 13—17, 2016, Sardinia, Italy.
【组织学术会议】
●The 2017 Beijing Workshop on Forecasting
●中国数量经济学会2016年年会
●2014年金融工程与风险管理国际研讨会
【学术访问】
●2014年8月加拿大多伦多大学
●2013年10月瑞典斯德哥尔摩大学
●2011年9月—2012年3月瑞典林雪平大学
●20116月 英国南安普顿大学
●2009年5月 荷兰伊拉斯姆斯大学
【荣誉与奖励】
● 第二届全国高校经管类实验教学案例大赛二等奖，2017年12月
● 瑞典皇家统计学会Cramér奖(最佳博士奖)，2014年3月。
● 国际贝叶斯学会青年奖励基金，2012年6月。
● 瑞典Knut & Alice Wallenberg基金奖励，2011年8月。
● 北京市级优秀毕业生，2007年7月。
