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In this independent research, I applied the Hilbert-Schmidt Independence Criterion (HSIC) to assess the goodness-of-fit for complex time-series models. I demonstrated its capability to capture complex, non-linear dependencies in model residuals where traditional linear methods like Pearson correlation fail.
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This project was my undergraduate thesis. I designed and implemented a data pipeline using the GPT API to perform automated semantic entity extraction from over 1,000 scientific papers, constructing a structured knowledge graph for reaction path analysis. This system improved search efficiency by 20% over traditional keyword-based baselines.
Published in Proceedings of the 31st ACM SIGKDD Conference on Knowledge Discovery and Data mining (KDD '25), 2025
Published in KDD 2025 (CCF-A). A novel framework for multi-modal recommendation.
Recommended citation: Maolin Wang*, Yutian Xiao*, Binhao Wang*, Sheng Zhang, et al. (2025). "FindRec: Stein-Guided Entropic Flow for Multi-Modal Sequential Recommendation." KDD '25.
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Published in ACM International Conference on Information and Knowledge Management (CIKM '25), 2025
Accepted to CIKM 2025 (CCF-B). A novel hybrid geometric GNN for knowledge-aware recommendation.
Recommended citation: Binhao Wang, Yutian Xiao, Maolin Wang, et al. (2025). "SPARK: Adaptive Low-Rank Knowledge Graph Modeling in Hybrid Geometric Spaces for Recommendation." CIKM '25.
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Published:
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Published:
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Undergraduate course, University 1, Department, 2014
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Workshop, University 1, Department, 2015
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