Gulnaz Zakariya | Data Science and Analytics | Research Excellence Award

Published on by Forensic Scientist

Mrs. Gulnaz Zakariya | Data Science and Analytics | Research Excellence Award

Satbayev University | Kazakhstan

Mrs. Gulnaz Zakariya is a researcher specializing in Machine Learning, Data Science, and Geospatial Analytics, with strong interdisciplinary contributions spanning FinTech and remote sensing. Her research focuses on the development of intelligent data-driven models for credit risk assessment, antifraud systems, and decision-support frameworks using machine learning and deep learning techniques. She has also made notable contributions to object-based image analysis and spatial change detection, particularly in the study of informal settlements using satellite imagery. Her work bridges applied artificial intelligence, geoinformatics, and financial technology, emphasizing model robustness, interpretability, and real-world deployment. She has published in peer-reviewed international journals and conferences indexed in major scholarly databases, contributing to innovation at the intersection of data science, spatial analytics, and applied AI-driven solutions.

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Featured Publications


Innovative Credit Scoring and Sales Accounting Solutions for SMEs in Kazakhstan

– Gulnaz Zakariya, Olzhas Akylbekov, Aiman Moldagulova,· FinTech, 2025


Object-based change detection of informal settlements

– P. Hofmann, G. Bekkarnayeva · JURSE 2017 (IEEE)

Adimias Wendimagegn | Data Science | Best Researcher Award

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Mr. Adimias Wendimagegn | Data Science | Best Researcher Award

Kotebe University of Education | Ethiopia

Mr. Adimias Wendimagegn is a data scientist and biostatistician specializing in statistical modeling, distribution theory, and applications in public health. His research primarily focuses on advanced regression frameworks, including classical and Bayesian modeling approaches under GAMLSS and BAMLSS structures. He has introduced novel models such as the Alpha Power Transformed Beta (APTBeta) regression for analyzing antenatal care utilization and developed methodological frameworks for Gamma and Beta-type distributions. He has published multiple peer-reviewed journal articles indexed in Scopus and Web of Science, contributing significantly to statistical theory and biostatistical applications. His works include studies on antenatal care utilization, maternal age at first birth, and advanced distributional models for proportion data. He is also the author of two academic books published by LAP Lambert Academic Publishing, covering joint modeling of longitudinal and survival data and determinants of teenage fertility in Ethiopia. His scholarly contributions extend to the development of R packages for regression modeling and diagnostics, enhancing reproducibility and accessibility in statistical analysis. Mr. Adimias Wendimagegnserves as a reviewer for leading international journals such as PLOS ONE and Value in Health, and his publications have received multiple citations reflecting his growing impact in the field. Through his research, he continues to advance innovative statistical methodologies and their applications in health and demographic studies.

Profile: ResearchGate

Featured Publications

  • Wendimagegn, A., & Arero, B. G. (2025, October). Alpha power transformed beta regression with application on antenatal care visit proportions among Ethiopian women. Journal of Statistical Theory and Applications. https://doi.org/10.1007/s44199-025-00135-w

  • Wendimagegn, A., Goshu, A. T., & Arero, B. G. (2024, September). New alpha power transformed beta distribution with its properties and applications. Frontiers in Applied Mathematics and Statistics, 10. https://doi.org/10.3389/fams.2024.1433767

  • Wendimagegn, A. (2019, March). Determinant of solid-waste management in Debre Birhan Town. American Journal of Theoretical and Applied Statistics, 8(1), 26–33. https://doi.org/10.11648/j.ajtas.20190801.14

Divyanee Garg | Economics, Econometrics and Finance | Best Researcher Award

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Ms. Divyanee Garg | Economics, Econometrics and Finance | Best Researcher Award

Indian Institute of Technology, Delhi | India

Ms. Divyanee Garg is a mathematician specializing in Portfolio Optimization, Behavioural Finance, Robust Portfolio Optimization, and Data-Driven Financial Techniques. Her research focuses on developing innovative mathematical and computational frameworks for decision-making under uncertainty, combining advanced risk measures such as Expectile Value at Risk (EVaR) and Conditional Value at Risk (CVaR) with behavioral finance models. She has contributed significant work on robust portfolio optimization using deep neural networks, integrating data-driven approaches to enhance traditional financial modeling. Ms. Divyanee Garg has published in high-impact journals, including Computational and Applied Mathematics and Omega, addressing deviation measures, robust allocation strategies, and cumulative prospect theory-based indexing, reflecting her strong influence in the field. She has presented her research at international conferences, including the 34th European Conference on Operational Research (EURO 2025, UK) and the ORSI Annual Convention 2024 (IIT Bombay), as well as symposia at IIT Roorkee and ISI Delhi, demonstrating active engagement with the global research community. Her notable projects include the development of optimality and duality conditions for semi-infinite programming problems and numerical improvements to Gauss-Chebyshev quadrature rules, both highlighting her analytical rigor and methodological innovation. Recognized for her academic excellence, she is a recipient of the Prime Minister’s Research Fellowship (PMRF) and the INSPIRE Scholarship from the DST, India. Ms. Divyanee Garg actively contributes to workshops and summer schools on large-scale optimization and machine learning applications in finance, combining theoretical depth with practical relevance. Her research exemplifies the integration of mathematical rigor, computational innovation, and applied financial insight, positioning her as a leading young researcher in quantitative finance and optimization.

Profiles: Google Scholar | ORCID | ResearchGate | LinkedIn | Staff Profile

Featured Publications