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Published in Applied Soft Computing, 2022
In this paper, for semi-supervised learning, we propose a new framework WFRSTT, which incorporates the merits of Fuzzy Rough Sets, Tri-training, a novel noise-filtering technique bad-points, and the multi-view information. Experiment results demonstrate its effectiveness in Medical Diagnosis.
Published in ICDCS 2026, Under Review, 2025
The paper introduces NetSight, a model that captures global-local spatial-temporal dependencies through a data-driven fusion graph, node normalization, and multi-head attention, significantly improving network traffic forecasting accuracy.
Published in IPCCC 2025, 2025
This paper proposes a DRL based TCP congestion control framework, which caters to handle various objectives (maximize throughput, minimize latency, …) set by various applications.
Published in Working Paper, 2026
Existing LLM-GNN integration relies on static, unidirectional pipelines, causing bidirectional error propagation. We introduce a Dual-View Co-Evolution mechanism where the GNN injects topological context to guide the LLM, and the LLM constructs a dynamic semantic graph.
Published in ICML 2026, Under Review, 2026
Invisible Tokens, Visible Bills: The Urgent Need to Audit Hidden Operations in Opaque LLM Services.
Published:
In this talk, titled “The Fundamental Models of Graph Neural Networks and Their Applications,” I will introduce recent advances in GNN foundation models and discuss their practical applications across domains including network performance optimization and data-driven system intelligence.
Data Structure and Algorithms (Undergraduate), NCSU, CS, 2023
Computer Network (Graduate), NCSU, CS, 2024
Senior Design (Undergraduate), NCSU, CS, 2025