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WASP: An R package for complex system modelling and prediction

15 minute read

Published:

The wavelet-based variance transformation method is used for system modelling and prediction. It refines predictor spectral representation using Wavelet Theory, which leads to improved model specifications and prediction accuracy. A supporting open-source software, Wavelet System Prediction (WASP), can be found under page of Software.

GitHub Pages: A site to host your personal homepage

3 minute read

Published:

GitHub Pages is a static site hosting service that takes HTML, CSS, and JavaScript files straight from a repository on GitHub, optionally runs the files through a build process, and publishes a website.

Word to Latex: Writing papers the right way

1 minute read

Published:

LaTeX is a document preparation system for high-quality typesetting. It is most often used for medium-to-large technical or scientific documents, but it can be used for almost any form of publishing.

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Meihao Fan

Ph.D. student at Renmin University of China
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Cost-Effective In-Context Learning for Entity Resolution: A Design Space Exploration

Published in IEEE 40th International Conference on Data Engineering (ICDE), 2024

This paper is about LLM for Entity Resolution.

Recommended citation: Fan, Meihao, Xiaoyue Han, Ju Fan, Chengliang Chai, Nan Tang, Guoliang Li, and Xiaoyong Du. "Cost-effective in-context learning for entity resolution: A design space exploration." In 2024 IEEE 40th International Conference on Data Engineering (ICDE), pp. 3696-3709. IEEE, 2024. https://ieeexplore.ieee.org/abstract/document/10597751

DeepPrep: An LLM-Powered Agentic System for Autonomous Data Preparation

Published in VLDB 2026 (Under Review), 2026

This paper proposes DeepPrep, an LLM-powered agentic system for autonomous data preparation.

Recommended citation: Meihao Fan, Ju Fan, Yuxin Zhang, Shaolei Zhang, Xiaoyong Du, Jie Song, Peng Li, Fuxin Jiang, Tieying Zhang, Jianjun Chen. "DeepPrep: An LLM-Powered Agentic System for Autonomous Data Preparation." VLDB 2026 (Under Review).

TACO: A Benchmark for Open-Domain Text-to-SQL with Ambiguous and Cross-Database Queries

Published in VLDB 2026 (Accepted), 2026

This paper proposes TACO, a benchmark for Open-Domain Text-to-SQL.

Recommended citation: Chao Deng, Ju Fan, Yuyu Luo, Qinliang Xue, Meihao Fan, Yuxin Zhang, Min Zhang, Xiaofeng Jia, Jing Zhang, Xiaoyong Du. "TACO: A Benchmark for Open-Domain Text-to-SQL with Ambiguous and Cross-Database Queries." VLDB 2026 (Accepted).

DeepAnalyze: Agentic Large Language Models for Autonomous Data Science

Published in ICML 2026 (Under Review), 2026

This paper explores agentic large language models for autonomous data science.

Recommended citation: Anonymous Authors (incl. **Meihao Fan**). "DeepAnalyze: Agentic Large Language Models for Autonomous Data Science." ICML 2026 (Under Review).

CODA-BENCH: Can Code Agents Handle Data-Intensive Tasks?

Published in ICML 2026 (Under Review), 2026

This paper introduces CODA-BENCH to evaluate code agents on data-intensive tasks.

Recommended citation: Anonymous Authors (incl. **Meihao Fan**). "CODA-BENCH: Can Code Agents Handle Data-Intensive Tasks?" ICML 2026 (Under Review).

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