Ms. Xinyi Wang | Atmosphere | Best Researcher Award
Ms. Xinyi Wang | Nanjing University of Information Science and Technology | China
Ms. Xinyi Wang is an undergraduate student majoring in Data Science at Nanjing University of Information Science and Technology. She has demonstrated exceptional talent in research, particularly in atmospheric science and radiative transfer modeling, where she contributed to developing an innovative framework that accounts for Earth’s curvature in satellite remote sensing simulations. As a second author, she published an SCI paper focusing on dataset construction and spherical radiative transfer modeling. Xinyi has actively participated in prestigious mathematical modeling competitions, achieving finalist and provincial awards that highlight her analytical and problem-solving skills. She gained practical experience as a project intern in a leading consulting department, optimizing procurement plans and maintaining data operations platforms. Her research expertise includes machine learning applications in atmospheric science, satellite remote sensing, and atmospheric optics. Through her work, she has demonstrated strong interdisciplinary capability, technical proficiency, and a commitment to advancing scientific understanding, establishing herself as a promising young researcher with significant potential.
Publication Profile
Orcid
Academic and Professional Background
Ms. Xinyi Wang is an undergraduate student pursuing a Bachelor of Science in Big Data at Nanjing University of Information Science and Technology. She is an active member of the Communist Youth League of China and has gained valuable professional experience as a project intern in the Technology and Transformation Consulting Department at Deloitte, where she contributed to optimizing procurement plans and maintaining the company’s internal data operations platform. Xinyi has demonstrated exceptional research capability, publishing a peer-reviewed SCI paper in Atmosphere as a second author, which addressed the effect of spherical curvature on the simulation of radiative transfer under diverse atmospheric scenarios. She has also excelled in competitive mathematical modeling challenges, earning finalist recognition in an international contest and a provincial prize in a national competition. Her academic achievements, combined with practical industry exposure and research contributions, reflect her strong analytical skills, technical expertise, and dedication to advancing scientific understanding in data-driven and atmospheric research.
Research Focus
Ms. Xinyi Wang’s research primarily lies at the intersection of atmospheric science, data science, and computational modeling, with a strong focus on radiative transfer processes. Her work addresses the limitations of traditional plane-parallel atmospheric radiative transfer models by incorporating the effect of Earth’s curvature, which is critical for improving the accuracy of satellite remote sensing and atmospheric simulations. She has contributed to the development of datasets and frameworks that enable more precise modeling of radiation under diverse atmospheric scenarios, enhancing the reliability of climate studies and optical path predictions. In addition, she is exploring the integration of machine learning techniques such as advanced algorithms to refine atmospheric radiation simulations, reflecting her interdisciplinary approach that bridges physical modeling and artificial intelligence. Her broader research interests include spherical radiative transfer, atmospheric optics, satellite remote sensing applications, and data-driven modeling, positioning her as a promising researcher advancing the accuracy of environmental and climate-related predictions.
Publication Top Notes
Dataset Construction for Radiative Transfer Modeling: Accounting for Spherical Curvature Effect on the Simulation of Radiative Transfer Under Diverse Atmospheric Scenarios
Conclusion
Ms. Wang shows outstanding potential as a young researcher with significant contributions to atmospheric science and data-driven modeling. While still early in her career, her demonstrated research rigor, awards, and innovative approach make her highly suitable for recognition through the Best Researcher Award.