Ye Tao | Machine Learning | Best Researcher Award

Dr. Ye Tao | Machine Learning | Best Researcher Award

PhD Student | China University of Petroleum, Beijing| China

Dr Ye Tao is a dedicated researcher focusing on sedimentology, unconventional oil and gas exploration, and the integration of artificial intelligence into geological studies. His work emphasizes fine characterization and sweet spot evaluation of shale gas reservoirs, tectonic evolution, sedimentary system reconstruction, and deepwater hydrocarbon accumulation models. Ye Tao has served as principal investigator and key researcher on multiple funded projects, including studies on shale reservoir heterogeneity in the Wufeng–Longmaxi Formations, tectonic evolution of the North Uscult Basin, and migration and accumulation mechanisms in the Guyana Basin. His expertise spans seismic data interpretation, fracture classification, mechanical modeling, and stress field simulation, contributing to accurate prediction of reservoir sweet spots and caprock sealing capacity. Ye Tao has actively published in peer-reviewed journals, presenting significant contributions such as deep learning-aided shale reservoir analysis, isotope-based sea-level reconstructions, and machine learning-based carbonate fossil recognition. His interdisciplinary approach bridges geology with computer vision and artificial intelligence, providing innovative methodologies for improving exploration accuracy. Ye Tao has been awarded multiple national and institutional prizes, including first prizes at China University of Petroleum’s Graduate Academic Forum and the National Doctoral Student Academic Forum, showcasing his academic excellence and leadership. His skillset includes seismic processing, petrographic thin section analysis, carbon and oxygen isotope testing, and restoration of paleoenvironments, enabling comprehensive understanding of sedimentary processes. By applying deep learning techniques to geological data, Ye Tao is contributing to next-generation exploration strategies that enhance prediction of hydrocarbon distribution and optimize resource development. His work demonstrates strong potential for advancing both theoretical sedimentology and applied petroleum exploration, making significant impact on energy resource evaluation and development strategies in complex geological settings.

Profile:  ORCID
Featured Publication

Tao, Y., Bao, Z., & Ma, F. (2025). Analyzing key controlling factors of shale reservoir heterogeneity in “thin” stratigraphic settings: A deep learning-aided case study of the Wufeng-Longmaxi Formations, Fuyan Syncline, Northern Guizhou. Applied Computing and Geosciences, 100293.

Tao, Y., Bao, Z., Yu, J., & Li, Y. (2025). The petrophysical characteristics and controlling factors of the Wufeng Formation–Longmaxi Formation shale reservoirs in the Fuyan Syncline, Northern Guizhou. Geological Journal.

Tao, Y., Gao, D., He, Y., Ngia, N. R., Wang, M., Sun, C., Huang, X., & Wu, J. (2023). Carbon and oxygen isotopes of the Lianglitage Formation in the Tazhong area, Tarim Basin: Implications for sea-level changes and palaeomarine conditions. Geological Journal, 58, 967–980.

Tao, Y., He, Y., Zhao, Z., Wu, D., & Deng, Q. (2023). Sealing of oil-gas reservoir caprock: Destruction of shale caprock by micro-fractures. Frontiers in Earth Science, 10, 1065875.

Khaista Rahman | Artificial Intelligence| Best Paper Award

Dr. Khaista Rahman | Artificial Intelligence| Best Paper Award

Assistant Professor | Shaheed Benazir Bhutto University Sheringal | Pakistan 

Dr. Khaista Rahman is a distinguished researcher specializing in fuzzy set theory, fuzzy logic, aggregation operators, and artificial intelligence-based decision support systems, with a strong focus on solving decision-making problems under uncertainty. His work explores advanced mathematical structures like Pythagorean fuzzy numbers, interval-valued fuzzy models, and complex fuzzy systems to create robust solutions for multi-attribute group decision-making processes. Dr. Rahman has published extensively on generalized and induced aggregation operators, developing new models that enhance decision accuracy and reliability in diverse applications such as plant location selection, hospital siting during COVID-19, vaccine selection, and railway optimization problems. His research integrates t-norm and t-conorm-based approaches, Einstein hybrid operators, and logarithmic intuitionistic fuzzy techniques to handle complex decision environments. He has also supervised several M.Phil., M.Sc., and BS scholars, contributing significantly to academic mentorship and knowledge dissemination. Recognized among the top 2% scientists worldwide by Stanford University from 2022 to 2025, he has made substantial contributions to granular computing, soft computing, and intelligent systems literature. His work during the COVID-19 pandemic stands out for developing emergency response models using complex fuzzy information to predict and manage disease spread in Pakistan. As Principal Investigator of a funded project on complex intelligent decision support models, Dr. Rahman has bridged theoretical advancements with practical implementations, making his research highly impactful. With an H-index of 26 and over 1900 citations, his scholarly influence spans mathematics, operations research, and computational intelligence, providing frameworks that empower policymakers and industries to make optimal decisions in uncertain and dynamic scenarios. Dr. Khaista Rahman has achieved 776 citations across 532 documents with an impressive h-index of 16.

Profile:  Scopus | ORCID
Featured Publication
  1. Rahman, K., & Khishe, M. (2024). Confidence level based complex polytopic fuzzy Einstein aggregation operators and their application to decision-making process [Retracted]. Scientific Reports, 14(1), 15253.

  2. Rahman, K., & Khishe, M. (2024). Retraction Note: Confidence level based complex polytopic fuzzy Einstein aggregation operators and their application to decision-making process. Scientific Reports, 14(1).

  3. Rahman, K., et al. (2025). Unraveling vegetation diversity and environmental influences in the Sultan Kha Valley, Dir Upper, Pakistan: An advanced multivariate analysis approach. Polish Journal of Environmental Studies.

  4. Rahman, K. (2024). Some new types induced complex intuitionistic fuzzy Einstein geometric aggregation operators and their application to decision-making problem. Neural Computing and Applications.