Dan Yang (杨丹)

I am an Associate Professor at Innovation and Information Management, Business School, The University of Hong Kong. I received my PhD from Department of Statistics, The Wharton School, University of Pennsylvania in 2012 and my Bachelor's degrees in Statistics and Economics from Peking University in 2007. Before joining HKU, I was an Assistant Professor at Department of Statistics, Rutgers University.

Tenure-Track Professor/Associate Professor/Assistant Professor in Business Analytics (Statistics) 

The Stat Group at HKU Business School

Prospective PhD students/Postdocs/RAs: 

Our statistics (Business Analystics) group has multiple positions for PhD students, Research Assistants, and Postdocs. We have a focus on business analytics via machine learning with applications ranging from asset pricing to healthcare analytics. If you are interested, self-motivated, and have background in statistics or mathematics, please send me an email with your CV, transcripts, and a short description of your research interests. 

Academic Appointments:

• Associate Director, Institute of Digital Economy and Innovation, The University of Hong Kong , 2022-

• Associate Professor, Innovation and Information Management, Faculty of Business and Economics, The University of Hong Kong , 2018-

• Assistant Professor, Department of Statistics, Rutgers University,  2013-2019

• Postdoc, Statistical and Applied Mathematical Sciences Institute,  2012-2013

Education:

• PhD in Statistics (2012), The Wharton School of Business, University of Pennsylvania

• BS in Statistics (2007) , School of Mathematical Sciences, Peking University

• BS in Economics (2007), Center for Economic Research, Peking University

Research Interests:

• Tensor data

• High-dimensional statistical inference

• Time series analysis

• Dimension reduction

• Network analysis

• Functional data analysis

• Portfolio management

• Analysis of observational studies and causal inference

• Business analytics in finance, economics, and healthcare

Editorial Board :

Associate Editor, Statistica Sinica , 2020 - 2023

"When you can measure what you are speaking about, and express it in numbers, you know something about it; but when you cannot measure it, when you cannot express it in numbers, your knowledge is of a meager and unsatisfactory kind: it may be the beginning of knowledge, but you have scarcely, in your thoughts, advanced to the stage of science". Kelvin (1981)