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Built a Douyin seeding analysis framework for Toread's new outdoor-casual product line, linking competitor landscape, campaign effectiveness, creator tiering, and content pattern analysis across Camel, Under Armour, Kailas, and Beneunder-style outdoor competitors. Using a cost-effectiveness-efficiency KPI system, peer benchmarking, and a creator evaluation model, I identified the highest-leverage combination as mid-tier creators plus visually atmospheric, low-ad-feel content, then translated the findings into reusable creator selection rules, content templates, and stage-based campaign evaluation logic for lower-tier market expansion.

Built a holiday demand forecasting and operations monitoring solution for Spring Festival demand fluctuations. Integrated multi-source data and applied machine learning with historical trends, seasonality, and holiday effects to forecast assurance-period workloads. Established dynamic-threshold monitoring and anomaly alerts for core KPIs, then optimized upgrade rules, priorities, and cross-channel collaboration between outsourced and in-house teams. During the Spring Festival assurance period, demand intake increased by 12% YoY while achieving 0 OCC accountable incidents, 0 complaints, and full compliance on key SLAs; quantified forecast-bias causes and created reusable data cases for future holiday model optimization.

Built an operations dashboard suite for OCC battery monitoring, image recognition, BaaS approvals, the 9903 hotline, and home/public charging services, providing a unified view of workload, processing efficiency, and handling quality across multiple service scenarios. I connected previously manual workflow, ticketing, and hotline data into Datasight, then designed core metrics such as response rate, resolution rate, dispatch accuracy, manual intervention rate, approval workload, and repeat-call rate, supported by trend views, drill-down tables, and reason-distribution analysis to improve staffing, emergency management, strategy review, and agent performance coaching.

A mixed-methods study of adults aged 60-69 in Hangzhou, combining surveys, employer interviews, policy text mining, and case analysis to evaluate re-employment intentions and the effectiveness of existing policy support. Drawing on 61 policy documents and 982 survey and fieldwork records, I used factor analysis and logistic regression to identify education support, employment services, and market mechanisms as key drivers, then translated the findings into targeted policy recommendations for highly educated older adults and older migrants living with their children.

Developed a Napari-based biomedical image annotation plugin for Charles Perkins Centre that combines Micro-SAM with interactive correction for multi-channel microscopy workflows. Implemented folder-level batch loading, per-channel embedding cache, auto segmentation, point/box/brush/eraser refinement, committed mask management, and TIFF/ZARR export to support reproducible downstream analysis. Validated the tool through repeated functional, usability, and performance testing, delivering stable segmentation results across nucleus, lipid-droplet, and other cell-structure annotation tasks.

Built a real-time fuel price monitoring platform on top of the NSW FuelCheck API, MQTT, and a Streamlit/Folium dashboard, covering the full workflow from data retrieval and cleaning to stream publishing and map-based visualization. The back end fetches live price data every 60 seconds, performs type conversion, deduplication, and station-level merging, then publishes cleaned records to MQTT at 0.1-second intervals; the front end subscribes to the stream in real time and supports filtering by fuel type, brand, and price range, along with live map markers, summary statistics, clustering, CSV export, and stable high-frequency updates through queue-based consumption, debounced rerenders, and preserved map state.

Integrated 25M+ exposure/ad/audience rows via SQL/Tableau Prep to a clean wide table. Built GMV/CTR/CVR dashboards with cross-filtering & alerts. Used k-means and chi-square tests to surface price-sensitive segments and optimize targeting & budget allocation.