Prof. Hugo Terashima Marín | Computer Science | Best Researcher Award

Dr. Hugo Terashima-Marín is a distinguished Professor of Computer Science and Intelligent Systems at Tecnológico de Monterrey, Mexico, widely recognized for his pioneering work in computational intelligence and heuristic optimization. His academic foundation spans prestigious institutions in Mexico, the United States, and the United Kingdom, reflecting a strong interdisciplinary background in informatics, artificial intelligence, and knowledge-based systems. As a leading researcher in evolutionary computation, constraint satisfaction problems, and hyper-heuristics, Dr. Terashima-Marín has developed innovative methodologies that bridge artificial intelligence and practical problem-solving across domains such as logistics, medicine, and smart cities. His extensive publication record in high-impact journals demonstrates his global influence in advancing algorithmic design, machine learning integration, and automated reasoning systems. Beyond research, he has mentored numerous doctoral and master’s students, fostering new generations of scientists in computational intelligence. His leadership roles at Tecnológico de Monterrey—directing graduate and doctoral programs and leading research groups in intelligent systems—underscore his commitment to academic excellence and institutional innovation. Recognized by the Mexican National System of Researchers and honored by the Mexican Academy of Sciences and the IEEE, Dr. Terashima-Marín’s contributions have elevated the standards of AI research in Latin America. His current projects explore multi-objective optimization, digital twins for smart city applications, and AI-driven decision support systems, continuing to push the boundaries of how computation can model, predict, and enhance complex human and industrial processes. Through decades of scholarship and collaboration, he remains an influential figure shaping the global discourse on intelligent systems and applied artificial intelligence.

Profiles: Scopus | ORCID

Featured Publications

Ali, F., Ahmed, A., Alipour, M. A., & Terashima-Marin, H. (2025, October 25). Adoption of AI-coding assistants in programming education: Exploring trust and learning motivation through an extended technology acceptance model.

Morales-Paredes, A., Juárez, J., Falcón-Cardona, J., Terashima-Marin, H., & Coello Coello, C. (2025, July 14). Automatic design of specialized variation operators for the multi-objective quadratic assignment problem.

Morales-Paredes, A. I., Falcón-Cardona, J. G., Juárez, J., Terashima-Marín, H., & Coello Coello, C. A. (2025, July 14). Reference point specification in greedy inclusion hypervolume-based subset selection: A study on two objectives. In Proceedings of the Genetic and Evolutionary Computation Conference (GECCO 2025).

Pirzado, F. A., Ahmed, A., Hussain, S., Ibarra-Vázquez, G., & Terashima-Marin, H. (2025, March 11). Assessing computational thinking in engineering and computer science students: A multi-method approach.

Garza-Santisteban, F., Cruz-Duarte, J. M., Amaya, I., Ortiz-Bayliss, J. C., Conant-Pablos, S. E., & Terashima-Marín, H. (2025, February). Selection hyper-heuristics and job shop scheduling problems: How does instance size influence performance

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.

Profiles: Scopus | ORCID

Featured Publications