I am an Associate Professor in the School of Economics, Zhongnan University of Economics and Law. My research interests lie at the intersection of efficiency and productivity analysis, machine learning, and nonparametric econometrics and their applications to energy, resources, and environmental fields.

Before joining Zhongnan University of Economics and Law, I was a Postdoctoral Researcher at Turku School of Economics, University of Turku. I received a Ph.D. in Economics and Business Administration from Aalto University School of Business.

Here you’ll find links to my publications and my CV. Feel free to contact me at sheng.dai [at] zuel.edu.cn.

Research highlights

1. Dai, S., Kuosmanen, N., Kuosmanen, T. & Liesiö, J. 2025. Optimal resource allocation: Convex quantile regression approach. European Journal of Operational Research, Forthcoming.

2. Kuosmanen, T. & Dai, S.* 2024. Modeling economies of scope in joint production: Convex regression of input distance function. Journal of Productivity Analysis.

3. Dai, S., Fang, Y.H., Lee, C.Y. & Kuosmanen, T. 2024. pyStoNED: A Python package for convex regression and frontier estimation. Journal of Statistical Software. 111, 1-43.

4. Dai, S., Kuosmanen, T. & Zhou, X. 2023. Generalized quantile and expectile properties for shape constrained nonparametric estimation. European Journal of Operational Research, 310, 914-927.

5. Dai, S. 2023. Variable selection in convex quantile regression: L1-norm or L0-norm regularization? European Journal of Operational Research, 305, 338-355.