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Lu Ping is a dedicated researcher in interdisciplinary fields including artificial intelligence (AI), digital economy, technological innovation, and industrial economics. Currently serving as an Associate Research Fellow at the China Academy of Information and Communications Technology (CAICT), Lu Ping focuses on examining the impacts of digital technologies (e.g., AI, big data, and IoT) on economic growth, industrial ecosystems, policy formulation, and societal ethics through multidimensional data modeling and empirical research.
Representative Academic Contributions:
1. AI Development and Societal Implications
A Brief History of Artificial Intelligence Development in China (2017): Explored the technological evolution and policy-driven pathways of China’s AI industry.
Ethical Dilemmas Faced by AI Algorithms (2018): Analyzed ethical challenges such as algorithmic bias and data privacy, proposing governance frameworks.
A Brief History of the Evolution of Smart Hardware in China (2018): Systematically reviewed the technological iterations and market dynamics of China’s smart hardware sector.
2.Technological Innovation and Industrial Economics
An Empirical Analysis of Technological Innovation Driving Growth in Internet Companies: Evidence from A-Share Listed Internet Firms in Shanghai and Shenzhen (2019).
Research on Competitiveness Measurement of Frontier Emerging Industries Based on Data Envelopment Analysis (DEA) Models (2019).
3.Digital Economy and Market Behavior
Correlation Analysis of Crowdfunding Behavior and Funding Performance for Internet Products: A Bayesian Approach Based on JD.com Crowdfunding Data (2018): Uncovered nonlinear relationships between user participation and project success rates using crowdfunding platform data.
Analyzing the Effects of Developer and User Behavior on Mobile App Downloads (2019): Built predictive models for app market performance based on user behavior data.
4.Policy Simulation
General Equilibrium Analysis of Beijing’s Water Supply and Consumption Policies: A Computable General Equilibrium (CGE) Model-Based Approach (2015).
Impact Analysis of EU Food Safety Standards on China’s Food Industry: A Dynamic Global Trade Analysis Project (GTAP) Model-Based Study (2015).
Academic Contributions:
Pioneered interdisciplinary paradigms in industrial economics research by integrating perspectives from econometrics, data science, and sociology. Published high-impact research in AI ethics, digital economy policies, and resource-environmental economics, providing decision-making references for academia and policymakers.
My research focuses on the interdisciplinary nexus of artificial intelligence (AI), digital economy, technological innovation, and industrial economics, with an emphasis on understanding how digital technologies reshape economic structures, policy frameworks, and societal norms. Key areas of interest include:
Agent-based simulation of social processes related to food and agricultural supply chains
I am an Associate Professor of Industrial Engineering with over two decades of experience in teaching, research, and supervision in data-driven decision making, operations research, and computational modeling. My research integrates Multi-Criteria Decision Analysis (MCDA), Agent-Based Modeling (ABM), and Reinforcement Learning (RL) to support strategic decision systems in sustainability, investment, and industrial operations. My recent work explores human-centric and multi-actor systems, leveraging simulation-based optimization and AI-driven analytics to enhance resilience, efficiency, and sustainability in complex socio-technical environments. I have published extensively in international journals, reviewed over 75 manuscripts, and am an active member of INFORMS and the System Dynamics Society. My long-term goal is to bridge industrial systems modeling with intelligent decision support, aligning academic research with real-world sustainability and innovation challenges.
🔹 Experience
Associate Professor — Industrial Engineering, University of Engineering and Technology, Taxila (2018 – Present)• Teach graduate and undergraduate courses in Operations Research, Data Mining, Advanced Statistics, System Simulation, and Soft Computing.• Conduct funded research in agent-based and reinforcement learning models for sustainable and data-driven decision systems.• Supervise doctoral students in decision analytics, multi-agent modeling, and MCDM applications.• Reviewer for international journals including Neural Computing and Applications, the Journal of Cleaner Production, Annals of Operations Research, Environment, Development and Sustainability, Energy for Sustainable Development, Scientific Reports, IEEE Access, Cleaner Energy Systems, Utilities Policy, and Sustainable Futures
🔹 Research Interests•
Data-Driven Decision Making• Agent-Based Modeling (ABM)• Reinforcement Learning (RL)• Multi-Criteria Decision Analysis (MCDA / MAMCA)• Sustainable Supply Chains• System Dynamics; Simulation• E-Health and Humanitarian Systems
🔹 Selected Achievements•
30+ peer-reviewed publications; ~360+ citations• Reviewer for 75+ international journal papers• Completed Coursera Specializations in Machine Learning, Deep Learning, and Reinforcement Learning• 20+ years of experience integrating data science with sustainability modeling
Data-Driven Decision Making | Agent-Based & Reinforcement Learning Models | Multi-Criteria Decision Analysis | Sustainable Systems | Operations Research | Netlogo | R