Professor at Minnan Normal University, China
Dr. Shujiao Liao is a full professor at the School of Mathematics and Statistics, Minnan Normal University, Zhangzhou, Fujian, China. With a strong academic background in applied mathematics and software engineering, she has dedicated her career to advancing the fields of granular computing, data mining, and machine learning. Her work bridges theoretical mathematics and computational methodologies, enabling novel approaches to intelligent data analysis. Over the years, Dr. Liao has played a pivotal role in both academic teaching and research leadership, contributing significantly to her institution’s development and scholarly output. She has guided numerous students and collaborated across interdisciplinary research groups. Her commitment to innovation and academic excellence makes her a respected figure in her field. As a scholar deeply engaged in cutting-edge technologies and data science trends, she continues to contribute impactful research and strives to address complex problems with analytical precision and computational insight.
Professional ProfileĀ
Educationš
Dr. Shujiao Liao holds a strong interdisciplinary educational background that underpins her academic career. She earned her Master of Science degree in Applied Mathematics from Shantou University, Guangdong, China, in 2006, where she built a solid foundation in mathematical modeling and analytical reasoning. Her pursuit of advanced studies led her to obtain a Ph.D. degree in Software Engineering from the University of Electronic Science and Technology of China, Chengdu, Sichuan, in 2018. This advanced degree enabled her to integrate mathematical theory with practical software systems, contributing to her versatility in computational research. Her doctoral studies focused on bridging data-centric algorithms with intelligent systems, which now form the core of her research interests. This rich educational trajectory has allowed her to approach complex scientific questions from both a mathematical and engineering perspective, making her academic contributions particularly robust in the fields of data mining and machine learning.
Professional Experienceš
Dr. Shujiao Liao is currently a full professor at the School of Mathematics and Statistics, Minnan Normal University, Zhangzhou, Fujian, China. With an academic career that spans over a decade, she has demonstrated excellence in teaching, research, and academic leadership. In her current role, she teaches advanced mathematics and computational theory courses, supervises postgraduate research projects, and actively engages in departmental development. She has led several internal and collaborative research initiatives in granular computing and machine learning, working closely with both academic and industrial partners. Her experience also includes conference presentations, curriculum development, and cross-disciplinary project coordination. She is recognized for her effective mentorship, contributing to the growth of young researchers and promoting high standards in academic inquiry. Through her consistent professional contributions, Dr. Liao has helped elevate her institutionās research standing and continues to serve as a vital resource for the academic community in mathematics and software research.
Research Interestš
Dr. Shujiao Liaoās research interests span several pivotal domains in computer science and applied mathematics, with a particular focus on granular computing, data mining, and machine learning. Her work in granular computing explores how knowledge can be structured and processed using information granules, improving the interpretability and efficiency of decision-making systems. In the area of data mining, she investigates algorithms for pattern discovery, classification, and clustering, contributing to improved data-driven strategies in scientific and industrial applications. Her interests in machine learning include developing intelligent models capable of adaptive learning and robust performance across complex datasets. Dr. Liaoās research bridges theory and application, aiming to solve real-world problems such as intelligent diagnostics, automated reasoning, and big data analysis. Her interdisciplinary focus allows her to work on innovative projects that combine mathematical rigor with computational techniques, positioning her as a contributor to the evolving field of intelligent systems and artificial intelligence.
Award and Honorš
While specific awards and honors for Dr. Shujiao Liao were not provided in the given information, her appointment as a full professor reflects recognition of her academic contributions and research leadership. Attaining such a role typically involves competitive peer-reviewed evaluations, consistent scholarly output, and excellence in teaching and mentorship. It is likely that she has received internal university-level commendations, research project funding awards, or participation in prestigious academic panels, common among professors of her standing. If available, details such as Best Paper Awards, Research Excellence Awards, or National Science Grants would further highlight her academic acclaim. Her long-standing role in the academic community and sustained focus on impactful research suggest she is a strong candidate for further honors at national or international levels. Formal acknowledgment through such accolades would complement her already impressive academic and research credentials, reinforcing her eligibility for broader recognitions such as the Best Researcher Award.
Research Skillš¬
Dr. Shujiao Liao possesses a robust set of research skills grounded in both theoretical understanding and practical application. She demonstrates strong expertise in mathematical modeling, algorithm development, and data analysis, which are essential for her work in granular computing and data mining. Her proficiency in applying machine learning techniques to complex datasets enables her to design predictive models with real-world relevance. She is adept at academic writing, literature review, and hypothesis-driven exploration, essential for high-quality publications and grant writing. Additionally, Dr. Liao has strong collaborative and project management skills, allowing her to lead interdisciplinary research teams and coordinate joint research initiatives. Her experience in supervising graduate theses further reflects her ability to guide rigorous research methodologies. She is also likely skilled in programming languages and tools used in data science, such as Python, MATLAB, or R, further supporting her contributions to computational research domains.
Conclusionš”
Dr. Shujiao Liao is a strong candidate for the Best Researcher Award, particularly within fields like granular computing and machine learning. Her academic background and full professorship position suggest a high level of expertise and leadership. To solidify her candidacy for top-tier recognition, showcasing quantifiable research outcomes, international influence, and broader impact will be important.
Publications Top Notedā
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Title: WrdaGAN: A text-to-image synthesis pipeline based on Wavelet Representation and Adaptive Sample Domain Constraint strategy
Authors: Yongchao Qiao, Yaānan Guan, Shujiao Liao, Wenyuan Yang, Weiping Ding, Lin Ouyang
Year: 2025
Citation: DOI: 10.1016/j.engappai.2025.111305
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Title: Semisupervised Feature Selection With Multiscale Fuzzy Information Fusion: From Both Global and Local Perspectives
Authors: Nan Zhou, Shujiao Liao, Hongmei Chen, Weiping Ding, Yaqian Lu
Year: 2025
Citation: DOI: 10.1109/TFUZZ.2025.3540884
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Title: S-approximation spaces extension model based on item-polytomous perspective
Authors: Xiaojie Xie, Shujiao Liao, Jinjin Li
Year: 2024
Citation: DOI: 10.21203/rs.3.rs-4447331/v1
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Title: Multi-Target Rough Sets and Their Approximation Computation with Dynamic Target Sets
Authors: Wenbin Zheng, Jinjin Li, Shujiao Liao
Year: 2022
Citation: DOI: 10.3390/info13080385
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Title: Multi-Label Attribute Reduction Based on Neighborhood Multi-Target Rough Sets
Authors: Wenbin Zheng, Jinjin Li, Shujiao Liao, Yidong Lin
Year: 2022
Citation: DOI: 10.3390/sym14081652
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Title: Attributeāscale selection for hybrid data with test cost constraint: The approach and uncertainty measures
Authors: Shujiao Liao, Yidong Lin, Jinjin Li, Huiling Li, Yuhua Qian
Year: 2022
Citation: DOI: 10.1002/int.22678
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Title: Featureāgranularity selection with variable costs for hybrid data
Authors: Shujiao Liao, Qingxin Zhu, Yuhua Qian
Year: 2019
Citation: DOI: 10.1007/s00500-019-03854-2