Dr. Xinfang Ji | Computation | Best Researcher Award

Dr. Xinfang Ji | North Minzu University | China

Dr. Xinfang Ji is an accomplished academic and researcher specializing in control theory, evolutionary computation, and surrogate-assisted optimization. Currently serving as a lecturer at the School of Mechanical and Electrical Engineering, North Minzu University, Dr. Xinfang Ji has established a strong research portfolio focusing on data-driven optimization, high-dimensional problem-solving, and computational intelligence. With an extensive publication record in leading international journals and contributions to cutting-edge projects funded by national and regional foundations, Dr. Ji has made significant advancements in surrogate-assisted evolutionary optimization and multi-objective decision-making algorithms. Through innovative research, academic leadership, and active project involvement, Dr. Xinfang Ji has demonstrated consistent excellence and impact in the field of computational intelligence and control engineering.

Professional Profile

SCOPUS

Summary of Suitability

Dr. Xinfang Ji is highly suitable for the Best Researcher Award due to her remarkable research achievements, impactful publications, and leadership in the field of computational intelligence. She earned her Ph.D. in Control Theory and Control Engineering from the China University of Mining and Technology (CUMT) and has over a decade of academic and research experience. Dr. Ji’s research primarily focuses on data-driven optimization, surrogate-assisted evolutionary computation, and multi-objective optimization, where she has made innovative contributions to solving complex, high-dimensional, and expensive optimization problems. She has an outstanding publication record with 19 peer-reviewed research papers, including several in top-tier international journals such as IEEE Transactions on Evolutionary Computation, IEEE Transactions on Cybernetics , Expert Systems with Applications , and Swarm and Evolutionary Computation. Her works are widely recognized for their high quality and innovation in the field.

Education

Dr. Xinfang Ji received a Ph.D. in Control Theory and Control Engineering from the China University of Mining and Technology (CUMT), where his research focused on data-driven optimization and surrogate-assisted evolutionary methods. He completed a Master’s degree in Control Theory and Control Engineering from CUMT, concentrating on evolutionary computation and multi-objective optimization. Dr. Xinfang Ji also earned a Bachelor’s degree in Electrical Engineering and Automation, laying the foundation for his expertise in intelligent control systems and computational modeling. This solid academic background has provided him with a strong interdisciplinary approach, integrating control theory, optimization techniques, and machine learning applications.

Experience

Dr. Xinfang Ji has extensive teaching and research experience, currently serving as a lecturer at North Minzu University, where he teaches courses in electrical engineering, control systems, and computational intelligence. Previously, he worked at China University of Mining and Technology Yinchuan College, where he contributed to curriculum development and supervised multiple undergraduate and postgraduate research projects. Beyond his teaching roles, Dr. Xinfang Ji has successfully led several national and regional research projects, including funding from the National Natural Science Foundation of China, the Ningxia Natural Science Foundation, and the Young Talent Cultivation Program at North Minzu University. His project leadership focuses on developing optimization algorithms for complex engineering problems and high-performance computational solutions, demonstrating a strong balance between academic rigor and practical applications.

Research Interests

Dr. Xinfang Ji’s research centers on surrogate-assisted evolutionary optimization, data-driven modeling, multi-objective decision-making, and high-dimensional optimization problems. His work emphasizes developing computationally efficient algorithms for expensive optimization tasks, such as multimodal problem-solving and multi-task optimization frameworks. By integrating machine learning techniques with control theory, Dr. Xinfang Ji designs innovative surrogate models that accelerate computation and improve optimization performance. He is particularly interested in knowledge transfer between optimization tasks, cognitive-based algorithm design, and the practical applications of evolutionary computation in industrial control systems, robotics, and engineering simulations. His interdisciplinary research provides valuable contributions to intelligent system design, decision-support frameworks, and automation technologies.

Awards

Dr. Xinfang Ji has been recognized for his academic excellence and contributions to research with several awards and distinctions. He received the Excellent Bachelor’s Thesis Award during his undergraduate studies and has consistently earned recognition for outstanding research achievements throughout his career. His projects have been supported by prestigious funding agencies, reflecting the significance of his work in advancing computational optimization and control engineering methodologies. Through his publications, collaborative research, and project leadership, Dr. Xinfang Ji has established himself as a prominent young researcher making impactful contributions to his field.

Publication Top Notes

Dual-Surrogate Assisted Cooperative Particle Swarm Optimization for Expensive Multimodal Problems

Multi-Surrogate Assisted Multitasking Particle Swarm Optimization for Expensive Multimodal Problems

Surrogate-Assisted Two-Stage Cooperative Differential Evolution for Expensive Constrained Multimodal Optimization Problems

A Review of Surrogate-Assisted Evolutionary Algorithms for Expensive Optimization Problems

Surrogate and Autoencoder-Assisted Multitask Particle Swarm Optimization for High-Dimensional Expensive Multimodal Problems

Conclusion

Dr. Xinfang Ji is a highly accomplished researcher whose contributions to evolutionary computation, surrogate-assisted optimization, and data-driven modeling have significantly advanced the state of computational intelligence. With a strong combination of theoretical insight and practical application, his research addresses critical challenges in solving expensive, high-dimensional, and constrained optimization problems. Through impactful publications, prestigious project leadership, and innovative algorithm development, Dr. Ji has positioned himself as an emerging leader in control engineering and optimization research. His demonstrated excellence and potential for future advancements make him an outstanding candidate for research awards and academic recognition.

Dr. Daemin Shin | Computer Science | Academic Luminary Achievement Award

Manager at Financial Security Institute (FSI), South Korea

Daemin Shin is a Manager at the Financial Security Institute, where he has been actively involved in advancing financial security measures since April 2015. With expertise in cloud security, Zero Trust security models, and data security, he has played a significant role in shaping secure financial infrastructures. Before his current role, he was a Senior Researcher at the Financial Security Research Institute from July 2012 to April 2015. His contributions to financial cybersecurity research have been instrumental in addressing security threats and enhancing the resilience of financial institutions. Shin continues to lead innovative research and development in financial security.

Profile

Scopus

Education

Daemin Shin earned his Master of Science in Engineering from the Graduate School of Information Security at Korea University, South Korea, in February 2009. He further pursued his Ph.D. in Engineering at the Department of Information Security, Soonchunhyang University, South Korea, which he successfully completed in February 2020. His academic journey reflects a strong foundation in cybersecurity, particularly focusing on financial security, cloud computing, and data protection. Throughout his education, he has been deeply engaged in research on securing financial transactions and developing security frameworks for modern digital finance ecosystems.

Experience

Shin has over a decade of experience in the field of financial security, with a strong emphasis on cloud security, data protection, and Zero Trust architectures. He started his career as a Senior Researcher at the Financial Security Research Institute, where he contributed to innovative research projects on financial cybersecurity from 2012 to 2015. Since April 2015, he has been serving as a Manager at the Financial Security Institute, where he continues to work on financial security infrastructure, cybersecurity policies, and security compliance strategies. His professional experience has significantly contributed to the development of robust security measures for the financial sector.

Research Interests

Shin’s research interests primarily focus on cloud security, financial security, and Zero Trust security models. He has conducted extensive research on securing cloud-based financial infrastructures, ensuring compliance with regulatory requirements, and mitigating security threats in digital finance. His recent works include studies on security considerations for DevSecOps software supply chains and Zero Trust evaluation frameworks tailored for financial institutions. His expertise in these domains has positioned him as a thought leader in enhancing cybersecurity resilience in the financial industry.

Awards and Recognitions

Shin has been recognized for his outstanding contributions to financial security and cybersecurity research. He has been nominated for the Best Researcher Award in recognition of his groundbreaking research on cloud security and financial security frameworks. His efforts in improving security compliance policies and implementing Zero Trust methodologies in financial institutions have gained widespread recognition. Shin’s work has had a substantial impact on the cybersecurity domain, making financial transactions and data storage more secure against emerging threats.

Publications

D. Shin, V. Sharma, J. Kim, S. Kwon, and I. You (2017). “Secure and Efficient Protocol for Route Optimization in PMIPv6-Based Smart Home IoT Networks,” IEEE Access, vol. 5, pp. 11100-11117, DOI: 10.1109/ACCESS.2017.2710379. Cited by 200+ articles.

D. Shin, K. Yun, J. Kim, P. V. Astillo, J.-N. Kim, and I. You (2019). “A Security Protocol for Route Optimization in DMM-Based Smart Home IoT Networks,” IEEE Access, vol. 7, pp. 142531-142550, DOI: 10.1109/ACCESS.2019.2943929. Cited by 150+ articles.

Shin, Daemin, Kim, Jiyoon, & You, Ilsun (2023). “국내 금융구득 클라우드 전환 동형 및 보안,” REVIEW OF KIISC, 33(5), 57-68. Cited by 50+ articles.

Shin, Daemin, You, Ilsun, and Kim, Jiyoon (2024). “국내 금융구득 클라우드 보안 위험 및 보안 요구사항에 관한 연구,” Journal of Next-Generation Computing, 20(4), 77-96, DOI: 10.23019/kingpc.20.4.202408.007. Cited by 30+ articles.

Daemin Shin, Jiyoon Kim, I Wayan Adi Juliawan Pawana, Ilsun You (2025). “Enhancing Cloud-Native DevSecOps: A Zero Trust Approach for the Financial Sector,” Computer Standards & Interfaces, DOI: 10.1016/j.csi.2025.103975. Cited by 20+ articles.

Conclusion

Daemin Shin’s dedication to advancing financial security and cybersecurity has been instrumental in shaping modern security frameworks for financial institutions. His research on cloud security, Zero Trust models, and DevSecOps methodologies continues to drive innovation in securing financial infrastructures. With a strong academic and professional background, he remains committed to developing secure financial ecosystems and mitigating cybersecurity risks in an ever-evolving digital landscape. His contributions have earned him significant recognition, making him a leading figure in financial security research.