Ms. Qiping Wei | Security Analysis | Women Researcher Award
Ms. Qiping Wei, University of Texas at Arlington, United States
Ms. Wei, a Ph.D. graduate in Computer Science with a focus on testing and security analysis of Solidity smart contracts, has demonstrated remarkable dedication to advancing blockchain technology. Her perseverance, highlighted by the extensive revision process of her first journal paper published in 2024, underscores her commitment to high-quality research. Leading innovative projects that integrate reinforcement learning and large language models to enhance Solidity smart contract security, Ms. Wei is at the forefront of blockchain technology and AI applications. Her impactful work and leadership in the field reflect her significant contributions and align with the values of the Research for Women Researcher Award.
Professional Profile:
Orcid
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Suitability for the Award
Ms. Qiping Wei is a highly suitable candidate for the Research for Women Researcher Award. Her specialization in the testing and security analysis of Solidity smart contracts is timely and critical, given the growing importance of blockchain technology. Her successful publication record, leadership in innovative research projects, and contributions to global technology make her a standout researcher in her field.
Academic Background and Achievements:
Ms. Wei has an impressive academic trajectory, having completed her Ph.D. in Computer Science with a specialization in testing and security analysis of Solidity smart contracts. Her educational journey, marked by persistence and dedication, reflects her commitment to advancing in a challenging field.
Her determination is evident from the multiple rounds of revision her first paper underwent before its eventual acceptance, highlighting her resilience and dedication to high-quality research. This perseverance culminated in the publication of her first journal paper in 2024, showcasing her contributions to the field.
Research Contributions:
Ms. Weiās research focuses on the critical area of blockchain technology, specifically the testing and security analysis of Solidity smart contracts. This work is increasingly important as blockchain systems become more integral to global networks.
She is leading two innovative projects that utilize reinforcement learning and large language models (LLMs) to improve the symbolic execution of Solidity smart contracts. These projects aim to enhance the security and reliability of blockchain systems, demonstrating her leadership in advancing technology at the intersection of AI and blockchain.
Impact and Innovation:
Her work has made a tangible impact, as evidenced by the significant interest in her publications. Her paper on the application of LLMs in engineering has garnered substantial attention, indicating the relevance and significance of her research.
By addressing security challenges in blockchain technology and exploring advanced AI techniques, Ms. Wei is pushing the boundaries of how these technologies can be applied to improve digital infrastructure. This aligns with the values of the Research for Women Researcher Award, which celebrates innovation and excellence in technology.
Professional Experience and Leadership:
Ms. Weiās involvement in part-time academic roles during her undergraduate years and her subsequent research efforts reflect her deep commitment to the field. Her leadership in managing research projects and contributing to advancements in blockchain security further underscores her suitability for this award.
Publication Top Note:
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Title: Mining New Scientific Research Ideas from Quantum Computers and Quantum Communications
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Year: 2019
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Cited by: 6
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Title: SmartExecutor: Coverage-Driven Symbolic Execution Guided by a Function Dependency Graph
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Year: 2023
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Cited by: 2
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Title: MagicMirror: Towards High-Coverage Fuzzing of Smart Contracts
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Year: 2023
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Title: Sligpt: A Large Language Model-Based Approach for Data Dependency Analysis on Solidity Smart Contracts
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Year: 2024
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Title: SmartExecutor: Coverage-Driven Symbolic Execution Guided via State Prioritization and Function Selection
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Year: 2024
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