معرفی
Professor Wolfgang Haerdle is the Ladislaus von Bortkiewicz Professor of Statistics at Humboldt University of Berlin's School of Business and Economics, where he has served as Professor of Statistics since 1992. He maintains an active international presence with visiting appointments at institutions including Xiamen University in China and National Jiaotong University in Taiwan.
His research spans smoothing methods, discrete choice models, statistical modeling of financial markets, and computer-aided statistics, with recent work focusing on high-dimensional nonstationary time series, cryptocurrency analytics, and machine learning applications in finance. Professor Haerdle has authored numerous influential textbooks including 'Applied Multivariate Statistical Analysis' and 'Statistics of Financial Markets', which have become standard references in their fields.
His publication record shows a clear evolution toward contemporary challenges in data science and finance, with recent work heavily focused on cryptocurrency markets, machine learning applications, and high-dimensional statistical methods. The trend indicates increasing interdisciplinary work bridging statistics, finance, and computer science, particularly in analyzing complex financial data streams and developing robust risk management frameworks.
- 2019-2023: YuShan Scholar, Taiwan
- 2017: Charter Fellow, INDI Institute Nonlinear Dynamics, RUDN University
- 2015: Foreign Expert Professor, Xiamen University, China
- 2013: Honorary Member, Romanian Academy of Science
- 2012: Multa Scripsit Award 'Econometric Theory'
- 2003: 'Highly cited scientist' by ISI
- 1997: Fellow, International Statistical Institute
- 1992: Fellow, Institute of Mathematical Statistics
Professor Haerdle has supervised over 50 PhD students whose work spans financial statistics, risk management, machine learning, and econometrics. His research has been supported by significant grants including the IRTG 1792 'High Dimensional Nonstationary Time Series' funded by the DFG. He founded and maintains the Quantlet platform for reproducible research and the Quantinar ecosystem for data analytics.
