Wael Badawy | Data Science | Pioneer Researcher Award

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Prof. Wael Badawy | Data Science | Pioneer Researcher Award

Head of Data science Department | Egyptian Russian University | Egypt

Prof. Wael Badawy, Ph.D., P.Eng., SFAHE, SIEEE, SACM, is a distinguished academic, researcher, engineer, and business leader with over twenty-eight years of international experience spanning academia, research, innovation, and technology commercialization. He has served in key academic and executive positions, including Executive Director of ABM College in Canada, Professor at several universities in Egypt, the United Kingdom, and Canada, and Adviser for Innovation and Entrepreneurship at Umm Al Qura University in Saudi Arabia. His expertise encompasses cybersecurity, artificial intelligence, computer engineering, information technology management, and digital transformation. Prof. Badawy has authored more than four hundred scientific publications, thirty-four patents, and over fifty books and proceedings, and has delivered numerous invited lectures and tutorials worldwide. Recognized with over ninety national and international awards, including distinctions from IEEE, Alberta Venture, the Global Business Leaders Magazine, and the QS Reimagine Education Awards, he has played a pivotal role in establishing and accrediting educational programs, serving on technical and quality assurance committees, and leading initiatives for national research and innovation strategies. As a Senior Fellow of the Advanced Higher Education and a Professional Engineer in Canada, Prof. Badawy continues to advance research excellence, technological innovation, and higher education development through visionary leadership, mentorship, and global collaboration.

Profiles: Scopus | Orcid | Google Scholar

Featured Publications

Badawy, W. (2025). The ethical implications of using children’s photographs in artificial intelligence: Challenges and recommendations. AI and Ethics, 5(2).

Maged, S., Mohamed, A., & Badawy, W. (2025, May 10). Audiogram-based tinnitus detection using deep learning: A comparative study of CNN architectures. In Proceedings of ICMISI 2025. IEEE.

Elnady, N., Adel, A., & Badawy, W. (2025, May 10). Enhancing kidney stone detection using YOLOv9: A deep learning approach. In Proceedings of ICMISI 2025. IEEE.

Elnady, N., Adel, A., & Badawy, W. (2025, April 13). Advancing brain tumor detection with YOLOv9: A comprehensive evaluation. In Proceedings of ICCIT 2025. IEEE.

Soliman, S. S., Abd El-Samie, F. E., Abd El-atty, S. M., Badawy, W., & Eshra, A. (2025). DNA nanotechnology for cell-free DNA marker for tumor detection: A comprehensive overview. Nucleosides, Nucleotides & Nucleic Acids, 44(4), 233–249.

Jicai Liu | Data Science | Women Researcher Award

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Assoc. Prof. Dr. Jicai Liu | Data Science | Women Researcher Award

Associate Professor | Shanghai Lixin University of Accounting and Finance | China

Jicai Liu is an Associate Professor of Statistics at the School of Statistics and Mathematics, Shanghai Lixin University of Accounting and Finance, with a research focus on high-dimensional data, survival analysis, dimension reduction, and quantile regression. His academic journey includes advanced training in statistics and extensive teaching and research experience across leading institutions in China and collaborations abroad. He has contributed to the development of novel methodologies in statistical theory and applications, particularly in areas such as high-dimensional regression, nonparametric tests, hazards models, feature screening, clustering algorithms, and dimension reduction techniques. His publications appear in internationally recognized journals including Bernoulli, Science China Mathematics, Journal of Computational and Graphical Statistics, Journal of Multivariate Analysis, Computational Statistics and Data Analysis, and Statistics and Computing, among others. As corresponding author on multiple works, he has advanced methods for analyzing censored outcomes, martingale difference correlation, projection quantile correlation, and sufficient dimension reduction. His contributions also extend to robust estimation, survival models for multivariate failure time data, additive hazards models, and semi-supervised regression. Through his research, he has established a strong reputation in both theoretical developments and practical applications, providing statistical tools that address complex data structures and real-world problems. With 357 citations by 262 documents across 33 publications and an h-index of 10, he has demonstrated significant scholarly impact. In addition to his academic achievements, he has been engaged in collaborative projects with international partners and short-term academic visits, enriching his global perspective and research impact. His work continues to influence the fields of statistics and applied mathematics, contributing innovative approaches to modern statistical challenges and advancing the understanding of high-dimensional and survival data analysis.

Profile: Scopus | Orcid

Featured Publications

Liu, J. (2022). Estimation under single-index hazards models: A new nonparametric extension of ANOVA via projection mean variance measure. Statistica Sinica.

Liu, J. (2022). K-CDFs: A nonparametric clustering algorithm via cumulative distribution function. Journal of Computational and Graphical Statistics.

Liu, J., Si, Y., Niu, Y., & Zhang, R. (2022). Projection quantile correlation and its use in high-dimensional grouped variable screening. Computational Statistics & Data Analysis, 107369.

Niu, Y., Zhang, R., Liu, J., & Li, H. (2020). Group screening for ultra-high-dimensional feature under linear model. Statistical Theory and Related Fields, 4(2), 120–132.

Zhang, Y., Liu, J., Wu, Y., & Fang, X. (2019). A martingale-difference-divergence-based estimation of central mean subspace. Statistics and Its Interface, 12(4), 571–584.