Raquel Gaspar | Financial Data Analysis | Best Researcher Award

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Prof. Raquel Gaspar | Financial Data Analysis | Best Researcher Award

ISEG, Universidade de Lisboa | Portugal

Prof. Raquel M. Gaspar is an Associate Professor of Finance with Habilitation at ISEG – Universidade de Lisboa, where she coordinates the Scientific Area of Finance and the Master in Finance program. She earned her PhD in Finance from the Stockholm School of Economics in 2006, focusing on credit risk and forward price models under the supervision of Prof. Tomas Björk, following an MSc in Mathematics Applied to Economics and Management from ISEG. Her research interests include mathematical and computational finance, credit risk modeling, asset pricing, stochastic processes, portfolio management, and risk analysis. Prof. Gaspar has extensive teaching experience at ISEG and internationally, including Stockholm Business School and Universidad Complutense de Madrid. She has supervised multiple PhD and MSc theses in finance and actuarial science, with an emphasis on quantitative methods and applied finance. Her scientific contributions are reflected in 329 citations, an h-index of 10, and 13 i10-index publications, according to Google Scholar. She has authored several pedagogical materials and textbooks and participates actively in research projects under CEMAPRE/REM. Recognized for her pedagogical innovation and leadership, Prof. Gaspar continues to contribute to the development of financial education and quantitative finance research in Portugal and abroad.

Profiles : Scopus | Orcid

Featured Publications

Gaspar, R. M., & Silva, P. M. (2023). Investors’ perspective on portfolio insurance: Expected utility vs prospect theories. Portuguese Economic Journal.

França, R., & Gaspar, R. M. (2023). On the bias of the unbiased expectation theory. Mathematics.

Gaspar, R. M., & Khapko, M. (2023, October). In memoriam: Tomas Björk (1947–2021), on his career and beyond. Finance and Stochastics.

Almeida, J., & Gaspar, R. M. (2023, July 25). Portfolio performance of European target prices. Journal of Risk and Financial Management.

Céu, M. S., & Gaspar, R. M. (2022, December 21). Vegetative cycle and bankruptcy predictors of agricultural firms. Agricultural Economics (Zemědělská ekonomika), 0139-570X / 1805-9295.

Ying Jin | Big Data Analytics | Best Researcher Awards

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Ying Jin | Big Data Analytics | Best Researcher Award

Professor, Nanjing University, China

Profile

 Scopus 

Prof. Jin Ying is a Professor at the Department of Computer Science and Technology, Nanjing University, widely recognized for her leadership and contributions in computer education, data mining, citation analysis, big data, and virtual reality applications. Her career bridges teaching, research, and academic service, reflecting a strong commitment to advancing both theoretical knowledge and practical innovations in the field of computer science. She has served in multiple influential academic and professional roles, helping shape the direction of education and technology integration in China.

Education

Prof. Jin began her academic journey in geochemistry and later transitioned into information management and computer science, bringing an interdisciplinary perspective to her work. She earned her Ph.D. in Management with a specialization in data mining and CSSCI research, which laid the foundation for her impactful contributions in citation analysis, big data, and education technology. Her diverse educational background allows her to integrate cross-disciplinary insights into computer science education and applied research.

Experience

Throughout her career at Nanjing University, Prof. Jin has advanced from teaching assistant to lecturer, associate professor, and eventually full professor. She teaches a wide range of courses including computer applications, Visual Basic, Python, and C programming, with her pedagogy focusing on innovation and practical engagement. Beyond the classroom, she serves as Group Lead of Visual Basic and Secretary of the Center of College Computer Test in Jiangsu Province. She is also an active member of the Computer Education Supervisory Committee (MOE, Liberal Arts), Jiangsu Computer Foundation Education Committee, and the Council on East China University Computer Foundation Education. Additionally, she contributes as a judge for the National College Computer Design Competition and as a peer reviewer for CSSCI-indexed journals.

Research Interests

Prof. Jin’s research integrates education and technology, addressing critical issues such as AI-driven talent training models, programming pedagogy reform in the AI era, flipped classroom strategies, big data course design for non-computer majors, and innovations in youth IT education. She has published widely in international journals and conferences, making significant contributions to the fields of data mining, education reform, and computational thinking. Her authored teaching resources, including College Basic Computer Applications and New Visual Basic Programming, are used across universities and contribute to improving computer education nationwide.

Awards

Prof. Jin’s achievements have been recognized through numerous awards and honors. She received the prestigious First Tan Haoqiang Computer Education Fund Outstanding Teacher Award and Nanjing University’s Shilin Teaching Award, reflecting her excellence in teaching and mentorship. She has also earned multiple best paper awards and nominations at international conferences, alongside prizes for guiding student projects in national-level computer design competitions. These recognitions highlight her dual impact as both a researcher and educator.

Featured Publications

  • Jin, Y. (2024). Exploration of K12 Multi-level Information and AI Talent Training Model.

  • Jin, Y. (2024). Exploration on Teaching Content Reform of Programming Course in the Era of AI.

  • Jin, Y. (2024). A survey of research on several problems in the RoboCup3D simulation environment.

  • Jin, Y. (2023). The Role of Science and Technology Innovation Competition in Talent Cultivation and Development.

  • Jin, Y. (2022). An approach for evaluating course acceptance based on Bayesian network. Int. J. of Intelligent Internet of Things Computing.

  • Jin, Y. (2022). Design and Implementation of a Cloud-Native Platform for Financial Big Data Processing Course.

  • Jin, Y. (2022). Hierarchical and Diverse Cultivation of Data Thinking Capability in College Based on New High School Curriculum Standards.

Conclusion

By combining rigorous scholarship, innovative teaching, and impactful leadership, Prof. Jin Ying has significantly advanced computer education reform and promoted the integration of technology with pedagogy. Her interdisciplinary vision and ability to merge data-driven methods with educational practice have influenced generations of students, equipping them with computational skills essential for the digital age. Through her roles in research, teaching, academic committees, and national projects, she continues to shape the landscape of computer science education in China and beyond, fostering a forward-looking environment that bridges research, practice, and innovation.