Changxin Yu | Digital technology | Best Researcher Award

Dr. Changxin Yu | Digital technology
|Best Researcher Award

 

Dr at Beijing Institute of Technology ,China.

Changxin Yu is a Ph.D. candidate in Applied Economics at Beijing Institute of Technology. Her research bridges agricultural economics and digital technology, focusing on their combined impact on productivity, sustainability, and innovation. She has investigated public perceptions of GMOs, the role of R&D in Chinese pesticide firms, and the productivity effects of modern biotechnology. Yu applies empirical models, including machine learning, to analyze how digital technologies—such as industrial robots and digital trade—contribute to green development and economic transformation. Her work is published in leading journals, including Technological Forecasting and Social Change. With interdisciplinary expertise, she continues to explore how digital tools can enhance agricultural and manufacturing sector performance, contributing to China’s sustainable economic growth.


🌍 Professional Profile:

Scopus

🏆 Suitability for the Best Researcher Award

 

Changxin Yu exemplifies the qualities sought in a Best Researcher Award recipient. Her work seamlessly integrates applied economics, digital innovation, and sustainability—a rare and valuable interdisciplinary nexus. She has produced high-impact research on topics such as industrial robots’ role in green growth and the effect of digital trade on agricultural productivity. Her ability to apply cutting-edge empirical and machine learning techniques enhances the credibility and applicability of her findings. With several prestigious publications and international collaborations, her research has advanced understanding of sustainable development and digital adoption in agriculture and manufacturing. Yu’s academic rigor, innovative approach, and commitment to real-world challenges position her as a strong candidate for the award.

🎓 Education 

Changxin Yu has a robust academic background that spans economics, management, and agriculture. She is currently pursuing a Ph.D. in Applied Economics at Beijing Institute of Technology (2019–present), focusing on digital and green economic development. She also holds a Master’s degree in Management Science and Engineering (2017–2019) from the same institution. Her undergraduate education was completed at Beijing Forestry University, where she earned a Bachelor’s degree in Agricultural and Forestry Economic Management (2013–2017). Her multidisciplinary training enables her to address complex challenges across agricultural economics, digital transformation, and environmental sustainability. Through this academic trajectory, Yu has cultivated a deep understanding of the socioeconomic implications of digital tools in agriculture and industry, strengthening her research versatility.

🏢 Work Experience 

Changxin Yu has a diverse range of research experience rooted in interdisciplinary projects. She has worked on USDA-funded studies examining the impact of public and private R&D investment on total factor productivity in China. Her academic and project-based research focuses on digital adoption in agriculture, industrial innovation, and environmental sustainability. She has analyzed the economic effects of GMOs, digital trade, and robotics in manufacturing. Through these experiences, she has developed strong skills in data analysis, policy assessment, and empirical modeling. Yu’s contributions extend beyond academia to inform policy and innovation strategies in agriculture and industry. Her professional journey is marked by her involvement in internationally collaborative projects and publications in well-regarded scientific journals.

🏅 Awards and Honors 

While specific awards are not listed, Changxin Yu has earned academic recognition through her involvement in high-impact research projects and publications in reputable journals such as Technological Forecasting and Social Change. Her selection for a USDA-funded research initiative reflects her capabilities and potential for influencing policy and practice. Additionally, her ongoing doctoral research incorporates advanced econometric and machine learning techniques, distinguishing her in the field of applied economics. Yu’s research contributions have gained attention in academic and policy circles for their relevance to green development, digital transformation, and agricultural innovation. Given the scope and impact of her work, she is likely to be a strong contender for academic and research honors in the near future.

🔬 Research Focus 

Changxin Yu’s research sits at the intersection of applied economics, digital transformation, and sustainable development. She focuses on how digital technologies, such as industrial robots and digital trade platforms, impact agricultural productivity and green growth. Her current doctoral research investigates the effects of modern biotechnology on agricultural total factor productivity (TFP), using robust empirical and machine learning methods. Yu also examines the economic implications of public and private R&D investments, particularly in agriculture and manufacturing. Her work has explored public attitudes toward GMOs and the economic impact of carbon abatement via digitalization. By analyzing how emerging technologies reshape economic systems, her research provides valuable insights for policy makers, academics, and industries working toward sustainable innovation.

📊 Publication Top Notes:

Citation:
Deng, H., Yu, C., Pray, C. E., & Jin, Y. (Forthcoming). How is China Shaping Global Food Supply Chains? Insights from the Seed Industry. European Review of Agricultural Economics.

Authors:

  • Haiyan Deng

  • Changxin Yu

  • Carl E. Pray

  • Yanhong Jin* (Corresponding author)

Year:
Forthcoming (Accepted, not yet published)

Citation:
Deng, H., Huang, Z., Wu, J., Güneri, F., Shen, Z., & Yu, C.* (2025). Harnessing the power of industrial robots for green development: Evidence from China’s manufacturing industry. Technological Forecasting and Social Change, 215, 124099. https://doi.org/10.1016/j.techfore.2025.124099

Authors:

  • Haiyan Deng

  • Zhonghua Huang

  • Jian Wu

  • Fatma Güneri

  • Zhiyang Shen

  • Changxin Yu* (Corresponding author)

Year:
2025

Citation:
Hu, R., Yu, C., Jin, Y., Pray, C., & Deng, H. (2022). Impact of government policies on research and development (R&D) investment, innovation, and productivity: Evidence from pesticide firms in China. Agriculture, 12(5), 709. https://doi.org/10.3390/agriculture12050709

Authors:

  • Ruifa Hu

  • Changxin Yu

  • Yanhong Jin

  • Carl Pray

  • Haiyan Deng

Year:
2022

Elahe Karampour | Artificial Intelligence | Best Researcher Award

Ms. Elahe Karampour | Artificial Intelligence| Best Researcher Award

 

Ms. Elahe  Karampour  K.N.Toosi University Of Technology, Iran

Elahe Karampour is a dedicated researcher specializing in Geodesy and Geomatics Engineering with a focus on spatial-temporal data analysis and network science. She is currently pursuing her Master of Science at K.N. Toosi University of Technology, Tehran, Iran, with a thesis on analyzing location-based social networks using geometric curves. She has served as a research assistant, contributing to advancements in community detection and link prediction in spatial networks. Elahe is also an experienced educator, having taught GIS, spatial databases, and social network analysis. She has received national recognition for her academic excellence and was awarded a fully funded research visit to ScaDS.AI in Germany. Her expertise spans programming, spatial modeling, and AI-driven geospatial analytics.

🌍 Professional Profile:

Orcid

🏆 Suitability for the Best Researcher Award

 

Elahe Karampour is an outstanding researcher with significant contributions to geospatial data analysis, particularly in network science and location-based social networks. Her pioneering research integrates Ricci curvature and hyperbolic geometry for community detection and link prediction, leading to novel advancements in spatial-temporal modeling. She has published influential research in high-impact journals, demonstrating her expertise and innovation. Elahe’s research excellence is further recognized through a prestigious fully funded research visit to ScaDS.AI in Germany. With strong technical proficiency in R, Python, PostgreSQL, and GIS software, she bridges theory and application, developing intelligent recommender systems for urban planning. Her exceptional academic record, teaching excellence, and innovative research make her a prime candidate for the Best Researcher Award.

🎓 Education 

Elahe Karampour holds a Master of Science in Geodesy and Geomatics Engineering from K.N. Toosi University of Technology, Tehran, Iran (2021–2024). Her research focuses on spatial-temporal data analysis and social network modeling, with a thesis titled “Analysis of Location-Based Social Networks with Geometric Curves,” receiving a perfect grade of 20/20. She completed her Bachelor of Science in Geodesy and Geomatics Engineering at the University of Zanjan, Iran (2015–2020). Elahe ranked 23rd nationwide in the Iranian Master’s University Entrance Exam, showcasing her academic excellence. Her strong analytical and technical skills, coupled with expertise in GIS, AI, and network analysis, enable her to make significant contributions to the field of geospatial research and urban data science.

🏢 Work Experience 

Elahe Karampour has extensive research and teaching experience in geospatial data analysis and network science. As a Research Assistant (2022–2024) at K.N. Toosi University of Technology, she developed advanced models for community detection and link prediction in location-based social networks using geometric methods. She also worked as a Lecturer (2024) at Babol Noshirvani University of Technology, teaching spatial analysis and visualization to undergraduate students. Additionally, she served as a Teaching Assistant (2023–2024), guiding master’s and PhD students in GIS, social network analysis, and spatial databases. Her technical expertise in R, Python, PostgreSQL, and QGIS, combined with her ability to integrate AI with geospatial analysis, has positioned her as a leader in her research domain.

🏅 Awards and Honors 

Elahe Karampour has received multiple accolades for her academic and research excellence. She ranked 23rd nationwide in the Iranian Master’s University Entrance Exam (2021), demonstrating her strong academic foundation. She was awarded a fully funded research grant for a short-term visit to ScaDS.AI Center for Scalable Data Analytics and Artificial Intelligence in Germany (2023), recognizing her contributions to AI-driven geospatial analysis. Additionally, she was listed among the top-ranked teachers by her students for her exceptional teaching performance. Her work in network science and geospatial modeling has led to publications in high-impact journals, further cementing her status as a leading researcher. These achievements underscore her dedication and outstanding contributions to the field of geospatial and network science.

🔬 Research Focus 

Elahe Karampour’s research centers on spatial-temporal data analysis, network science, and AI-driven geospatial modeling. She specializes in analyzing location-based social networks using advanced mathematical frameworks such as Ricci curvature and hyperbolic geometry for community detection and link prediction. Her work integrates graph-based modeling with GIS technologies to enhance urban planning, mobility analysis, and personalized recommender systems. Elahe has developed innovative approaches to analyzing complex data structures, utilizing machine learning and AI techniques for geospatial applications. She is particularly interested in the intersection of mathematics, computer science, and geospatial technologies, aiming to create data-driven solutions for urban analytics and smart city development. Her research has been recognized internationally, reinforcing her expertise in geospatial data science.

📊 Publication Top Notes:

  • Karampour, E., Malek, M.R., & Eidi, M. (2025). Discrete Ricci flow: A powerful method for community detection in location-based social networks. Computers and Electrical Engineering, 123, 110302.