Vice Dean, Beijing University of Posts and Telecommunications, China
Prof. Xinchao Zhao is a distinguished academic and researcher in the fields of swarm intelligence, evolutionary algorithms, and optimization, currently serving as a Professor at the School of Science, Beijing University of Posts and Telecommunications (BUPT), China. With extensive teaching and research experience, he has made significant contributions to data-driven optimization, cloud scheduling, and machine learning. His prolific academic output and international collaborations have earned him recognition in the global scientific community. 📘🔬
Publication Profile
Scopus
🎓Education Background
While specific degree details are not listed, Prof. Zhao’s academic journey is rooted in his longstanding association with Beijing University of Posts and Telecommunications, progressing from lecturer to full professor. His multidisciplinary focus reflects a strong foundation in computer science, mathematics, and artificial intelligence. 🎓📚
💼Professional Experience
Prof. Zhao began his academic career in 2005 as a Lecturer at BUPT. He advanced to Associate Professor by 2009 and was promoted to full Professor in 2014. His international experience includes visiting professorships at the University of Birmingham and the University of Essex, UK, during 2013–2014. Since then, he has continued his professorial duties at BUPT, mentoring Ph.D. candidates and leading cutting-edge research. 🌍🏫
🏆Awards and Honors
While specific awards are not mentioned, Prof. Zhao has successfully secured and led numerous prestigious national and provincial projects, including several from the National Natural Science Foundation of China and the Beijing Natural Science Foundation. His research leadership and contributions to innovation in optimization algorithms underscore his recognition and reputation. 🏅💡
🔬Research Focus
Prof. Zhao’s research primarily centers around swarm intelligence, evolutionary computation, multi-objective optimization, and their applications in fields such as cloud computing, medical image analysis, and big data. He is particularly interested in data-driven optimization algorithms, fuzzy clustering, neural architecture search, and multi-modal objective problems. His interdisciplinary approach integrates theory and application to solve complex real-world problems. 🧠🧮
🔚Conclusion
Prof. Xinchao Zhao stands as a visionary scholar whose contributions continue to shape the evolution of computational optimization and artificial intelligence. His commitment to academic excellence and innovation reflects in his impactful research, prolific publications, and active mentorship. 🌟📈
📚Top Publications
A Budget-Constrained Workflow Scheduling Approach With Priority Adjustment and Critical Task Optimizing in Clouds
IEEE Transactions on Automation Science and Engineering, 2025
Cited by: 17 articles
Focus: Cloud workflow scheduling under budget constraints with optimized task prioritization.
Fuzzy clustering-based large-scale multimodal multiobjective differential evolution algorithm
Swarm and Evolutionary Computation, 2025
Cited by: 11 articles
Focus: Combines fuzzy clustering and differential evolution to tackle complex multiobjective problems.
An enhanced tree-seed algorithm for global optimization and neural architecture search optimization in medical image segmentation
Biomedical Signal Processing and Control, 2025
Cited by: 8 articles
Focus: Tree-seed algorithm enhancement for image segmentation and neural architecture search.
A bidirectional workflow scheduling approach with feedback mechanism in clouds
Expert Systems with Applications, 2024
Cited by: 22 articles
Focus: Integrates feedback mechanism into cloud scheduling strategies.
Hybrid Response Dynamic Multi-objective Optimization Algorithm Based on Multi-Arm Bandit Model
Information Sciences, 2024
Cited by: 15 articles
Focus: Merges dynamic response strategies with the multi-arm bandit framework for MOO.
An enhanced Runge Kutta boosted machine learning framework for medical diagnosis
Computers in Biology and Medicine, 2023
Cited by: 34 articles
Focus: Advanced ML framework integrating Runge Kutta method for enhanced diagnostics.
A novel MOPSO-SODE algorithm for solving three-objective SR-ES-TR portfolio optimization problem
Expert Systems with Applications, 2023
Cited by: 19 articles