Aizhen Ren | Machine learning | Excellence in Innovation Award

Published on by Engineering Scientist

Prof. Aizhen Ren | Machine learning
| Excellence in Innovation Award

College of Science, Inner Mongolia Agricultural University | China

Prof. Aizhen Ren research work centers on machine learning, deep learning, statistical inference, and their applications in economics, finance, and computational biology. A significant portion of the research contributes to the development and mathematical validation of advanced bootstrap techniques, including the speedy double bootstrap method, which enhances the statistical reliability of phylogenetic tree estimation and provides third-order accurate unbiased p-values. These methods have been applied to evolutionary analyses of horse breeds, supporting biological and genomic investigations with high-precision statistical tools. In the financial domain, the research explores machine-learning-based trend prediction models, such as multiscale bootstrap-corrected random forest voting systems used to forecast stock index movement with improved accuracy and inference reliability. Additional work includes the construction of financial risk early-warning models for listed companies using multiple machine learning approaches, reflecting an interdisciplinary blend of statistics, computing, and economics. Contributions also extend to consumption behavior analysis employing regression-based models, as well as deep learning ensemble frameworks integrating empirical mode decomposition and temporal convolutional networks for time-series prediction tasks. The released R package SDBP operationalizes the novel bootstrap methodology, enabling researchers to compute unbiased p-values efficiently. Overall, the research advances methodological innovation and practical applications across data-intensive scientific domains.

 Profile: Orcid

Featured Publications

Ren, A., Duan, Y., & Liu, J. (2025). Multiscale bootstrap correction for random forest voting: A statistical inference approach to stock index trend prediction. Mathematics, 13(22), 3601. https://doi.org/10.3390/math13223601

Ren, A., Ishida, T., & Akiyama, Y. (2020). Mathematical proof of the third order accuracy of the speedy double bootstrap method. Communications in Statistics – Theory and Methods, 49(16), 3950–3964. https://doi.org/10.1080/03610926.2019.1594295

Ren, A., Ishida, T., & Akiyama, Y. (2013). Assessing statistical reliability of phylogenetic trees via a speedy double bootstrap method. Molecular Phylogenetics and Evolution, 67(2), 429–435. https://doi.org/10.1016/j.ympev.2013.02.011

Zhiying Mu| Neural Networks | Best Researcher Award

Published on by Engineering Scientist

Dr. Zhiying Mu| Neural Networks
|Best Researcher Award

Dr . Zhiying Mu  Northwestern Polytechnical University, China .

Zhiying Mu, a Ph.D. candidate in Cyberspace Security at Northwestern Polytechnical University, is a distinguished young scholar dedicated to AI safety and intelligent system security. With a rigorous academic foundation and cross-disciplinary insight from her mathematics background at the University of Connecticut and the University of Nebraska–Lincoln, she has contributed to multiple national-level research projects, including the “New Generation Artificial Intelligence” initiative. Her publications in top-tier journals like IEEE IoT Journal and Neural Processing Letters demonstrate high academic impact. She leads multiple research and data-driven modeling projects with outstanding results. Her innovative mindset, strong leadership, and publication record make her an exceptional candidate for the Best Researcher Award.



🌍 Professional Profile:


Scopus


🏆 Suitability for the Best Researcher Award


 




















Zhiying Mu, a Ph.D. candidate in Cyberspace Security at Northwestern Polytechnical University, is a distinguished young scholar dedicated to AI safety and intelligent system security. With a rigorous academic foundation and cross-disciplinary insight from her mathematics background at the University of Connecticut and the University of Nebraska–Lincoln, she has contributed to multiple national-level research projects, including the “New Generation Artificial Intelligence” initiative. Her publications in top-tier journals like IEEE IoT Journal and Neural Processing Letters demonstrate high academic impact. She leads multiple research and data-driven modeling projects with outstanding results. Her innovative mindset, strong leadership, and publication record make her an exceptional candidate for the Best Researcher Award.




















🎓 Education 


Zhiying Mu earned her Ph.D. in Cyberspace Security (2021–2025) from Northwestern Polytechnical University, under the supervision of Academician He Dequan. Her curriculum includes machine learning, optimization, complex networks, and academic ethics. She actively contributed to major national and industrial research projects related to AI safety and power systems. She previously earned a Master’s degree in Mathematics from the University of Connecticut (2017–2019), and a Bachelor’s degree in Mathematics from the University of Nebraska–Lincoln (2013–2017). Her academic performance has been consistently excellent, with a GPA of 3.8/4.0 during her Ph.D. Her multidisciplinary training bridges cybersecurity and data science, laying a robust foundation for her research excellence and interdisciplinary innovation.


🏢 Work Experience 


Zhiying Mu has led and participated in various high-impact research projects involving AI safety, network attack modeling, and climate risk forecasting. Notable projects include storm damage prediction using regression models, ACI index modeling via time-series analysis, and optimal strategy evaluation in Tic-Tac-Toe using generalized linear models. She has also organized institutional reading programs, promoting interdisciplinary knowledge sharing. Her responsibilities typically involve end-to-end project management: problem formulation, data collection and preprocessing, statistical modeling, visualization, and outcome documentation. She is proficient in R and Python and applies advanced analytics and machine learning techniques. Her blend of theoretical depth and practical implementation reflects a versatile and impactful research profile in both academic and applied contexts.


🏅 Awards and Honors 


Zhiying Mu has received significant recognition for her research and academic contributions. She was the top borrower of the year at her university library, reflecting her deep engagement with academic literature. She has been entrusted with leadership roles in national projects funded under China’s “New Generation Artificial Intelligence” program and major horizontal projects with the State Grid Corporation of China. Her peer-reviewed publications in top SCI-indexed journals such as IEEE IoT Journal, Neurocomputing, and Neural Processing Letters highlight her academic excellence. She has also served as a project lead in multiple interdisciplinary modeling initiatives. Her academic and extracurricular leadership underscores her status as an emerging thought leader in AI security and intelligent systems.


🔬 Research Focus 
































































Zhiying Mu’s research centers on artificial intelligence security, multi-task learning, risk modeling, and network attack analysis. She investigates adversarial learning techniques, identity-preserving dialogue generation, and neural machine translation enhancement using syntactic features. Her work integrates mathematical modeling, machine learning, and cybersecurity to address challenges in intelligent power systems, social network robustness, and data-driven decision-making. She has explored both black-box and white-box vulnerabilities in AI systems and proposed defense mechanisms with theoretical grounding. Her interdisciplinary focus also includes time-series forecasting for insurance risk and strategic modeling in game theory. She actively contributes to national AI safety platforms and is committed to advancing secure, interpretable, and reliable AI technologies for critical infrastructures.




























































































📊 Publication Top Notes:


Prompt-enhanced Neural Machine Translation with POS Tags


Authors:
Mu, Zhiying; Lin, Shengchuan; Guo, Sensen; Yu, Shanqing; Gao, Dehong


Journal:
Neurocomputing, 2025


Citations:
0 (as of now)