Xu Liu | Statistics | Best Researcher Award

Prof. Xu Liu | Statistics | Best Researcher Award

Prof. Xu Liu, Shanghai University of Finance and Economics, China

Prof. Xu Liu, a tenured Full Professor at the School of Statistics and Management, Shanghai University of Finance and Economics (SUFE), is a distinguished expert in high-dimensional data analysis, statistical genetics, and AI-enabled decision-making. With a Ph.D. in Statistics from Yunnan University and postdoctoral training from Northwestern University and Michigan State University, he brings extensive international research experience. His work focuses on machine learning, transfer learning, and deep generative models, with numerous publications in top-tier journals. Prof. Liu actively contributes to academic leadership as an editor and conference organizer, making him a key figure in the field of modern statistics and data science.

Author Profile

Scopus

🎓 Early Academic Pursuits

Prof. Xu Liu’s academic journey is rooted in a deep passion for mathematics and statistics. His formal education began with a Bachelor of Science in Mathematics from Hengyang Normal University (2000–2004). Demonstrating early excellence and commitment to the field, he pursued a Master of Science in Mathematics at Yunnan University, graduating in 2007. His intellectual drive and mathematical acumen culminated in a Ph.D. in Statistics from the same institution in 2011. These formative years laid a strong theoretical foundation and cultivated his research interests in complex data structures, machine learning, and statistical inference.

During his graduate studies, Prof. Liu delved into challenging problems in mathematical modeling, statistical theory, and early explorations in computational statistics, developing an academic rigor that continues to define his work today.

👨‍🏫 Professional Endeavors

Following the completion of his doctoral studies, Prof. Liu embarked on a postdoctoral research path that would span internationally recognized institutions. From 2011 to 2013, he was a postdoctoral researcher in the Department of Statistics at Northwestern University, followed by another impactful postdoc position in the Department of Statistics and Probability at Michigan State University (2013–2016). These roles enabled him to collaborate with prominent statisticians and immerse himself in cutting-edge research on high-dimensional inference and statistical learning.

In 2016, Prof. Liu joined the School of Statistics and Management at Shanghai University of Finance and Economics (SUFE) as an Assistant Professor. Over the years, his academic progression has been steady and well-earned—rising to Associate Professor (2019), achieving tenure in 2022, and recently being promoted to Full Professor in 2024.

His professional tenure at SUFE reflects both his teaching excellence and research productivity, positioning him as a pillar of the department and a leader in statistical education.

📊 Contributions and Research Focus

Prof. Xu Liu’s research contributions span a diverse range of statistical and data science disciplines:

  • High-dimensional data analysis

  • Machine learning and deep generative models

  • Representation and transfer learning

  • Statistical genetics and gene-environment interactions (G×E and G×G)

  • Advanced variable selection methods

  • Uncertainty quantification and statistical inference

His recent publications appear in prestigious journals like Statistics in Medicine, Journal of Multivariate Analysis, Bioinformatics, Journal of Computational and Graphical Statistics, and Statistics in Biosciences. These works include the development of tools such as the Python-based REGS sampler and the R-package qfabs, demonstrating a strong commitment to open-source statistical computing and reproducible science.

He is also contributing significantly to academic literature with three upcoming books focused on AI decision-making and high-dimensional statistical inference, all scheduled for release in 2025.

🏅 Accolades and Recognition

Prof. Liu’s excellence in research and academic contribution has earned him significant accolades:

  • Outstanding Achievement Award of Philosophy and Social Science, Shanghai (2023)

  • Third Prize, 17th “Challenge Cup” Shanghai Science and Technology Competition (2022)

These awards underscore his relevance not only in theoretical statistics but also in its impactful applications across economics, social science, and public policy.

Moreover, his professional recognition extends to editorial roles. He serves as:

  • Associate Editor, Journal of Statistical Theory and Applications

  • Associate Editor, International Journal of Organizational and Collective Intelligence

  • Guest Editor, Special Issue in Axioms on Mathematical and Statistical Finance

🌐 Impact and Influence

Prof. Liu’s influence transcends national boundaries through his international collaborations, open-source software contributions, and thought leadership in machine learning applications in health, finance, and genomics. His work on gene-environment interactions is particularly impactful in the area of statistical genetics, with real-world implications for personalized medicine and epidemiological modeling.

As an educator, Prof. Liu has taught a wide spectrum of courses—from foundational subjects like Mathematical Statistics to advanced topics such as Empirical Process Theory and Computer Programming in C/C++. His mentoring of students and junior researchers helps foster the next generation of statisticians and data scientists.

🌟 Legacy and Future Contributions

With a growing legacy built on innovation, scholarship, and mentorship, Prof. Xu Liu stands at the forefront of modern statistical science in China. His upcoming books will serve as valuable references in both academic and applied settings. As a conference organizer and invited speaker, he continues to shape conversations around statistical learning, AI in economics, and computational statistics.

Looking ahead, his work promises deeper integration of AI-enabled modeling, data-driven decision-making, and ethical data science, especially in public health, policy, and business analytics.

✍️Publication Top Notes


📘Subgroup testing in the change-plane Cox model.

Author: Zhang, X., Ren, P., Shi, X. Ma, S. and Liu, X

Journal: Statistics in Medicine

Year: 2025


📘Random projection-based response best-subset selector for ultra-high dimensional multivariate data

Author: Hu, J., Li, T., Liu, X. and Liu, X

Journal: Multivariate Analysis

Year: 2025


📘Uncertainty quantification in high-dimensional linear models incorporating graphical structures with applications to gene set analysis.

Author: Tan, X., Zhang, X., Cui, Y. and Liu, X.

Journal: Bioinformatics

Year: 2024


 

Edmund Agyemang | Applied Statistics | Best Researcher Award | 13385

Mr. Edmund Agyemang | Applied Statistics | Best Researcher Award 

Mr. Edmund Agyemang, The University of Texas Rio Grande Valley, Ghana

Mr. Edmund Fosu Agyemang is a passionate and award-winning researcher in Applied Statistics and Data Science at The University of Texas Rio Grande Valley, USA. With a strong background in biostatistics, machine learning in health, experimental design, and time series analysis, his work bridges interdisciplinary gaps in health and data science. He holds an MPhil in Statistics from the University of Ghana and has earned multiple honors including UTRGV’s Outstanding Masters Research Performance Award (2024). His commitment to academic excellence and community impact is reflected in his teaching roles across Ghana and the U.S., as well as his editorial contributions to international journals.

Profile

Orcid

Google Scholar

📚 Early Academic Pursuits

Mr. Edmund Fosu Agyemang’s academic journey began with a strong foundation in the sciences at Aggrey Memorial A.M.E. Zion Senior High School in Cape Coast, Ghana, where he earned his WASSCE in General Science. Driven by an innate curiosity for numbers and patterns, he pursued a Bachelor of Science in Statistics at the University of Ghana, graduating in 2018 as the Best BSc. Statistics (Major) Student. His early academic brilliance was evident in his consistent academic record, earning multiple departmental awards for both academic and extracurricular excellence.

Following his undergraduate success, Edmund advanced to earn a Master of Philosophy (MPhil) in Statistics from the same university in 2021. His strong academic performance, coupled with his commitment to service, research, and teaching, laid the groundwork for his acceptance into the M.S. in Applied Statistics and Data Science program at The University of Texas Rio Grande Valley (UTRGV), USA, where he is currently enrolled.

🧑‍🏫 Professional Endeavors

Edmund’s academic progression runs parallel to his teaching and research contributions. His early roles as a Teaching Assistant and later as a Graduate Assistant at the University of Ghana showcased his skill in making complex statistical concepts accessible to students. He supported undergraduate and graduate courses including Regression Analysis, Experimental Designs, and Hypothesis Testing.

He expanded his instructional portfolio as a Lead Tutor for the University of Ghana’s School of Distance Education and later served as an Adjunct Faculty at Ashesi University, teaching courses such as Precalculus, Applied Calculus, and Statistics.

Currently, at UTRGV, he serves as a Graduate Research Assistant, working on interdisciplinary applications of data science and machine learning. His role involves drafting research papers, analyzing data sets, and contributing to ongoing scholarly efforts in the department.

🔬 Contributions and Research Focus

Edmund’s primary research focus is Applied Biostatistics, with wide-ranging interdisciplinary applications. His areas of interest include:

  • Machine learning in health applications 🤖

  • Time series analysis of health and medical data 📈

  • Computational statistics and experimental design 🧪

  • Multivariate analysis and advanced sampling techniques

He is particularly passionate about using statistical tools to address real-world problems, especially in the healthcare and epidemiological domains. His ongoing graduate research at UTRGV integrates machine learning methods into biostatistical analysis, contributing valuable insights to modern data-driven science.

🏆 Accolades and Recognition

Mr. Agyemang’s excellence in academia and research has not gone unnoticed. His awards include:

  • 🥇 Outstanding Masters Research Performance Award, UTRGV (2024)

  • 🎓 Honor Roll for Academic Excellence, UTRGV (Fall 2023)

  • 💰 I Am Buddy Scholarship, UTRGV (2024-2025)

  • 🏅 Presidential Research Fellowship Award, UTRGV (2023)

  • 📘 Best MPhil Statistics Student, University of Ghana (2021)

  • 🏆 Best Graduate Research Assistant, University of Ghana (2021)

  • 🥇 Best Undergraduate Teaching Assistant, University of Ghana (2019)

  • 🏅 Most Influential Student & Sports Personality, UGASS Awards (2018)

These accolades reflect his strong commitment to scholarship, mentorship, and leadership.

🌍 Impact and Influence

Edmund’s impact spans both academic and professional spheres. As a dedicated educator, he has taught and mentored hundreds of undergraduate and graduate students across Ghana and the United States. His experience in curriculum development and personalized instruction makes him a transformative figure in academic spaces.

As a researcher, he contributes to multidisciplinary projects and publications, tackling pressing issues in public health through statistical modeling. His editorial board roles with academic journals like the Journal of HIV/AIDS Research and Journal Multidisciplinar (Montevideo) underscore his influence in shaping global academic discourse.

🌱 Legacy and Future Contributions

Looking ahead, Edmund Fosu Agyemang envisions a career that balances academic instruction, innovative research, and community outreach. His long-term goals include:

  • Establishing a center for applied data science in West Africa to train students and professionals.

  • Expanding his research into predictive modeling for global health challenges, including pandemic forecasting.

  • Mentoring a new generation of statisticians with a focus on ethics, social responsibility, and interdisciplinary impact.

His legacy is already being written in the lives of students he has mentored, the papers he has helped write, and the bridges he has built between data science and healthcare.

📘Publication Top Notes

Journal: International Journal of Economic Policy Studies
Year: 2025
Journal: Scientific African
Year: 2024
Contributors: Agyemang, E.F.; Nortey, E.N.N.; Minkah, R.; Asah-Asante, K.
Journal: Model Assisted Statistics and Applications
Year: 2023