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A computer science graduate student with an interdisciplinary background and strong self-learning capabilities, researching Graph Neural Networks for financial fraud detection. Proficient in C/C++ and Python, with hands-on experience in embedded operating systems, kernel-level scheduling, resource management, emerging storage architectures, and system design.
Skills
Featured projects
RTOS Kernel-Level Scheduling and Resource Management Development
C · μC/OS-II · Real-Time Operating Systems · Rate Monotonic Scheduling · Earliest Deadline First Scheduling · Non-Preemptive Critical Sections · Ceiling Priority Protocol
Implemented Rate Monotonic (RM) and Earliest-Deadline-First (EDF) dynamic scheduling algorithms at the kernel level, successfully managing preemption and deadline logic for multiple periodic tasks. Addressed multi-threading race conditions by manually implementing Non-Preemptive Critical Section (NPCS) and Ceiling Priority Protocol (CPP), effectively resolving priority inversion issues common in embedded systems. Directly manipulated Task Control Block (TCB) linked lists to build a custom context switch recorder, accurately tracking task switching behaviors and system latency. The work involved μC/OS-II kernel-level implementation.
Insurance Fraud Detection based on Graph Neural Networks (GNN)
Python · Graph Neural Networks · PyTorch
Modeled complex relationships between insurance agents and policyholders to research and extract feature representations of anomalous behaviors. Built Graph Neural Network models using Python to process large-scale graph data, enhancing the robustness of the anomaly detection model.
Education
National Taiwan University of Science and Technology — Master, Computer Science and Information Engineering
National Central University — Bachelor, Atmospheric Science
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