Dan Yang (杨丹)
Associate Professor at IIM
Associate Director for Institute of Digital Economy and Innovation (IDEI)
Contacts:
Innovation and Information Management (IIM)
Business School
The University of Hong Kong
Room 816, K.K. Leung Building
Phone: (852) 39170015
E-mail: dyanghku at hku dot hk
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.
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 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:
Business analytics in finance and healthcare
High-dimensional statistical inference
Tensor data
Time series analysis
Dimension reduction
Functional data analysis and multivariate data analysis
Analysis of observational studies and causal inference
Professional Services :
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)