Lecturer, Department of Foundational Courses Dujiangyan Campus, Sichuan Agricultural University, China
Yan Wu is a dedicated statistician and lecturer at Sichuan Agricultural University, specializing in the intersection of statistics and data science. With a strong academic foundation and international research exposure, Yan focuses on innovative statistical methods applied to environmental science and knowledge base correction.
Publication Profile
Google Scholar
🎓 Education Background
Yan Wu earned a PhD in Statistics from Southwest University in 2023 under Professor Zili Zhang. During the PhD, Yan expanded research expertise as a visiting scholar at the University of Luxembourg’s Big Data Group. Prior to this, Yan completed a Master’s in Probability and Mathematical Statistics at Chengdu University of Technology and a Bachelor’s degree in Mathematics and Applied Mathematics from Southwest University of Science and Technology with high honors.
💼 Professional Experience
Yan Wu currently serves as a lecturer at Sichuan Agricultural University, Department of Foundational Courses. Yan’s research includes guided inductive logic programming and 3D geological modeling, contributing to cross-disciplinary projects in statistics, artificial intelligence, and environmental modeling. Yan has also been involved in several national and international research projects integrating big data and machine learning techniques.
🏆 Awards and Honors
Yan has been recognized multiple times, including the prestigious CSC-funded Visiting PhD Scholar position at the University of Luxembourg in 2018, outstanding trainee awards in Sichuan Higher Education Teacher Qualification Program, and several academic scholarships and innovation awards during graduate studies. Notably, Yan won third prize in a campus-wide mathematical modeling competition, highlighting strong analytical and problem-solving skills.
🔍 Research Focus
Yan Wu’s research focuses on statistical modeling for environmental issues such as carbon emission markets, as well as the application of guided inductive logic programming to clean and correct large knowledge bases. This interdisciplinary work bridges environmental science, data science, and artificial intelligence to create efficient, data-driven solutions.
🔚 Conclusion
With a robust educational background, diverse research experience, and notable academic achievements, Yan Wu is a promising early-career researcher contributing significantly to statistical science and its applications in environmental and knowledge systems.
📚 Publication Top Notes
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Reducing greenhouse gas emissions: a duopoly market pricing competition and cooperation under the carbon emissions cap
M Jian, H He, C Ma, Y Wu, H Yang
Environmental Science and Pollution Research 26, 16847-16854 (2019)
Cited by 31 article
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A green production strategies for carbon-sensitive products with a carbon cap policy
C Ma, X Liu, H Zhang, Y Wu
Advances in Production Engineering & Management 11(3), 216-226 (2016)
Cited by 19 articles
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Guided inductive logic programming: Cleaning knowledge bases with iterative user feedback
Y Wu, J Chen, P Haxhidauti, V Ellampallil Venugopal, M Theobald
6th Global Conference on Artificial Intelligence (GCAI 2020) (2020)
Cited by 2 articles
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Correcting large knowledge bases using guided inductive logic learning rules
Y Wu, Z Zhang, G Wang
PRICAI 2021: Trends in Artificial Intelligence (2021)
Cited by 1 article
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An Inductive Logical Model with Exceptional Information for Error Detection and Correction in Large Knowledge Bases
Z Wu, Yan and Lin, Xiao and Lian, Haojie and Zhang
Mathematics 13(11), Article 187 (2025)