GPU Optimization for All-Pairs Shortest Path (Individual Competitor; CCF-TCARCH 2025 National Champion)

• Designed a memory-aware 3-stage blocked Floyd–Warshall pipeline to reduce bandwidth pressure in dense APSP computation, using shared memory to improve data locality.
• Optimized kernel execution through fusion, dual-stream scheduling, double buffering, and reduced synchronization overhead. Further compressed host-to-device transfer cost from O(V²) to O(E) via custom I/O and device-side graph reconstruction.
• Ranked 1st nationally as an individual competitor, achieving a 52% end-to-end speedup on the official hidden datasets (11.50s → 5.42s) through profiling-driven optimization and systematic performance tuning.

RaceRadar: An End-to-End iOS System for Aggregating and Prioritizing Competition Opportunities (Independent systems engineering project; App Store release in preparation)

• Built a fault-tolerant, multi-source ingestion pipeline that continuously crawls, cleans, deduplicates, and ranks competition announcements, publishing a unified JSON feed through scheduled GitHub Actions workflows.
• Developed a native iOS app in Swift/Xcode with card-based discovery, detailed opportunity pages, deadline-aware prioritization, source/category tagging, and shareable competition posters.
• Designed the system around real student workflows, translating automated data ingestion and ranking into a deployable user-facing information system for discovering higher-quality and time-sensitive opportunities.

SparseTSF-FFT: A Frequency-Domain Framework for Time Series Forecasting

• Reimplemented and extended SparseTSF from PyTorch to MindSpore, including custom API mappings and reconstruction of the training/evaluation pipeline for cross-framework deployment.
• Designed an FFT-based learnable filtering module to replace local sliding-window convolutions, aiming to better capture long-range periodic structure under a lightweight parameter budget.
• Conducted cross-framework profiling and forecasting evaluations, observing improved long-horizon performance (e.g., at 336 and 720 steps on ETTh1/ETTh2) relative to the baseline.

Research on Adaptive Weight Selection for Image Diffusion Models (National 3rd Prize in Challenge Cup, K-LoRA++)

• Extended the training-free object-style fusion framework (CVPR 2025 K-LoRA) by mathematically formulating and evaluating 8 distinct stage-aware scaling schedules across diffusion timesteps.
• Demonstrated through first-order derivative analysis and cross-domain experiments that a constant-derivative linear schedule optimally balances content fidelity and style consistency, effectively preventing structural distortion and visual jitter during the fusion process.
• Facilitated the end-to-end development of the project’s interactive demo and drove its practical deployment for downstream generative applications.