Data Scientist Yue Hao

Data Scientist Yue Hao: Deepening Data Intelligence to Strengthen Supply Chain Defense

By Wenyue Li

Guarding industrial security with intelligent technologies and solving development challenges through scientific innovation is the mission and pursuit of scientific and technological talents in the new era. Data scientist Yue Hao has dedicated herself to the interdisciplinary field of AI and supply chain management. With a series of research achievements featuring both theoretical depth and practical value, as well as impactful industrial projects, she has delivered intelligent solutions to mitigate supply disruption risks. Her work has become a critical pillar empowering supply chain security in key sectors, demonstrating the innovative capabilities of contemporary researchers.

Hao holds a Master of Science in Information Systems from Johns Hopkins University and a Bachelor of Arts in Economics from the University of California, Irvine. Her interdisciplinary educational background enables her to not only accurately grasp trends in AI development but also deeply understand the commercial logic underlying supply chain operations. During her years of professional practice at iSoftStone Inc., she has evolved from a specialized data scientist into a core technical leader driving business enablement, accumulating extensive experience in delivering large-scale projects. This solid industry experience has laid a robust foundation for her academic research while ensuring all studies remain closely aligned with real industrial needs, achieving deep integration of technology and business.

Her technical expertise and innovative mindset have been fully demonstrated through industrial project implementations. She built automated ETL pipelines and Power BI dashboards, and by optimizing SQL scripts and DAX code, significantly improved data query efficiency and interactive visualization experience, removing barriers to data-driven decision-making across departments. Notably, the real-time data visualization platform (Power BI) she developed for over 200 retail stores nationwide directly boosted cross-departmental operational efficiency by 30%, achieving cost reduction and efficiency improvement in supply chain operations through practical technology deployment. In the Microsoft Security Copilot project, she led GPT conjoint analysis research, deeply exploring core patterns among user roles, permission controls, and product utility, providing critical insights for product iteration and strategic decision-making. These project outcomes not only validate Hao’s ability to translate AI technologies into commercial value but also allow her to precisely identify core pain points in supply chain management—inaccurate demand forecasting, delayed risk perception, and insufficient cross-domain collaboration—clearly directing her subsequent targeted academic research.

Addressing practical industry challenges, Hao has continued to deepen her academic research and published several high-impact papers focusing on core issues in supply chain risk prevention and control. Among her major innovations are the CLASNet (Cognitive Load-Aware Supply Chain Demand Forecasting Framework) and RAPS-Net (Cross-Domain Risk Prediction Framework for Cloud Payment Supply Chains). With high-precision forecasting and risk perception capabilities, these frameworks help enterprises identify supply gaps earlier, allocate production capacity more rationally, optimize inventory layout scientifically, and formulate emergency response plans more rapidly, effectively avoiding supply disruptions caused by demand misjudgment, risk blind spots, and poor coordination. Their research value and industrial significance are highly prominent.

The CLASNet framework innovatively integrates cognitive load theory with the CNN-LSTM-Attention deep learning model, constructing an end-to-end architecture of demand feature extraction-cognitive state modeling-adaptive interaction. Breaking through the limitation of traditional demand forecasting that focuses solely on data itself, the model uses CNN to extract short-term local fluctuation features of supply chain demand, LSTM to capture long-term time-series trends, and a cognitive load encoder to perceive decision-maker interaction behaviors and dynamically adjust attention weights and information presentation. In tests on a real retail supply chain dataset covering 1,200 SKUs and 50 warehouses, CLASNet achieved an RMSE of just 14.55, reducing error by 9.6% compared to the traditional CNN-LSTM-Attention model. With its high-precision forecasting capability, the framework assists semiconductor, pharmaceutical, and other enterprises in anticipating market demand in advance, optimizing inventory and production capacity layout, and preventing risks such as raw material shortages and product deficits stemming from demand misjudgment at the source.

Over the years, Yue Hao has continuously advanced the development of AI-driven supply chain risk management through solid project experience and cutting-edge academic research. Starting from data and building bridges with models, she has transformed the “perceptual capability” of artificial intelligence into the “immunity” of industrial chains. In an era intertwined with globalization and uncertainty, her research not only provides strong support for enterprises to enhance supply chain resilience but also sets an exemplary model for how data scientists can empower the real economy.