Yu Chiao Huang

Research under Dr. Xiao Liu • Utilized Python (Beautiful Soup) for web scraping and Google Cloud Natural Language API and Google News API to collect and process over 100K news headlines, including macroeconomic indicators and company-specific news. • Implemented AI-driven entity recognition to filter and remove irrelevant news, analyzing over 100K+ data points to ensure a focused and accurate analysis on company-specific headlines. • Applied sentiment analysis using Python to extract emotional tones from news articles, resulting in an 8% improvement in model accuracy and an 11% improvement in precision for predicting stock price movements, providing more reliable insights into market trends.

• Engineered a robust data pipeline using Python to process over 6 million records, efficiently extracting actionable insights critical for warehouse optimization. • Conducted in-depth simulations of historical warehouse operations, leveraging six months of historical data to model activities under both current and proposed shipping methods. Discovered that the proposed method would require up to 50% more storage space in worst-case scenarios. • Validated model reliability by achieving a 0.92 correlation between FlexSim simulation outputs and Python-based predictions, demonstrating the accuracy and robustness of our forecasting methods.

• Boosted operational efficiency by analyzing software usage data from over 1 million transactions using advanced SQL techniques for data mapping, querying, profiling, and validation. Extracted actionable insights that led to a 20% optimization in resource allocation and a 15% improvement in team productivity. • Developed and delivered impactful Tableau visualizations, clarifying key metrics like inventory turnover and sales performance for non-technical management, driving enhanced data-driven decision-making. • Led an inventory demand prediction project utilizing machine learning (LSTM) in PyTorch, precisely forecasting future demand patterns, which cut inventory shortages by 37%, elevated service levels, and streamlined supply stability for key automotive components. • Led cross-functional teams, coordinating with stakeholders across procurement, logistics, and finance to evaluate and secure competitive suppliers. Successfully negotiated multi-year, multi-million-dollar contracts with 5 domestic and 3 foreign suppliers, achieving a 14% reduction in procurement costs and significantly improving supply chain operational performance.

• Directed the creation of a dynamic Tableau dashboard, revealing critical insights into the influence of new energy vehicles on traditional vehicles. Enhanced reporting processes and empowered stakeholders with data-driven decisions for industry analysis. • Streamlined customer data management in the CRM system, ensuring the accuracy of key requirements. Created an Excel dashboard to monitor interactions and feedback, leading to a 12% increase in customer retention by enabling effective sales follow-ups and improving project implementation with data-driven insights.