๐ป
Zihao Viston Wang is currently pursuing a Ph.D. in Agricultural and Forestry Economics Management under the supervision of Prof. Tianjun Liu. His research focuses on agricultural supply chains, with particular expertise in empirical analysis and artificial intelligence algorithms. He is committed to integrating research paradigms from agricultural and forestry economics with management science.
PhD Candidate in A&F Economics Management
College of Economics & Management, Northwest A&F University
MEng. in Management Science & Engineering
School of Management, Xi'an University of Architecture and Technology
BMgmt. in Engineering Management
College of Civil Engineering, Dalian Minzu University
My research focuses on the supply chains within the agri-industry. Currently, I am investigating the efficiency-enhancing effects of digital intelligence on agricultural leading enterprises and the agricultural supply chain. I specialize in utilizing AI algorithms, including reinforcement learning and deep learning, to address operational decision-making challenges faced by stakeholders in the agricultural supply chain. For instance, my research published in Omega designed an smart inventory system for fresh produce wholesalers, effectively overcoming the dynamic replenishment in presence of perishable nature of produce.
I am also studying empirical research methods in economics and am committed to deeply integrating agricultural economics and management science research paradigms. My objective is to provide new perspectives on issues related to the agricultural supply chain through an interdisciplinary approach.
My research interests encompass the following areas:
I welcome collaboration and discussions with colleagues who share similar interests. ๐ค
A VS Code extension designed specifically for Stata users. An enhanced version of โStata Outline,โ featuring syntax highlighting, code completion, smart outlines, code execution, and productivity tools. Almost everything you need for Stata in VS Code is integrated into a single extension.
One VS Code extension tailored for Stata users. An enhanced version of Stata Outline with advanced Syntax Highlighting + Code Hints + Smart Outline + Code Execution + Quick Editing. All your Stata needs in one place.
This is a VS Code extension I wrote that adds outline support to Stata .do files.
This is a real-time, flexible web application for event scoring statistics that I developed.
This website is dedicated to sharing my academic research and life updates. Welcome to explore!
This study, published in the journal China Rural Economy, employs various machine learning methods, including multiple linear regression, penalized regression, ensemble learning, and deep learning, to examine the factors influencing the urbanization level of the agricultural migrant population. The article offers new insights and methodologies for analyzing these influencing factors through machine learning techniques.
This study, authored by Professor Guo Feng from Shanghai University of Finance and Economics, is published in the China Economic Review. It primarily employs machine learning and empirical methods for causal inference, utilizing data from Ant Financial to assess the conditions of offline small and micro operators in the informal economy during the COVID-19 pandemic.
Please feel free to communicate directly with me! Leave your emoji reactions or text comments, and I will respond promptly.
