lecture, Xi’an University of Technology, China
Hu Caie is a dedicated researcher in Control Science and Engineering, specializing in data-driven optimization, evolutionary optimization, transfer learning, and machine learning 🤖. With a strong academic foundation and extensive experience in computational intelligence, Hu has made significant contributions to antenna design, surrogate modeling, and expensive optimization problems 📡. Passionate about leveraging artificial intelligence for real-world applications, Hu continues to push the boundaries of research in optimization and machine learning.
Publication Profile
🎓 Education
Hu Caie pursued a Ph.D. in Control Science and Engineering at China University of Geosciences (Wuhan) 🎓. As part of a Successive Postgraduate and Doctoral Program of Study and Research, Hu specialized in data-driven optimization, machine learning, and evolutionary optimization from September 2017 to June 2023. This academic journey laid the foundation for advanced research in optimization techniques and computational intelligence.
💼 Experience
Hu has been actively involved in intelligent optimization and electromagnetic simulation projects 🤖. As the Superintendent of the Data-driven Ultra-wideband Antenna Intelligent Optimization Technology Project (2019-2021), Hu played a key role in enhancing antenna design using AI. Additionally, as the Principal Responsible Person for the Electromagnetic Simulation Study of Non-uniform Array with Intelligent Optimization Based on Deep Learning (2020-2022), Hu contributed significantly to advanced simulation techniques 📊. Apart from leading projects, Hu also participated in State Key Laboratory research on antenna arrays and signal processing.
🏆 Awards and Honors
Hu Caie’s academic excellence has been recognized with multiple prestigious scholarships and accolades 🏅. Notable honors include the National Academic Scholarship and the CSC Scholarship for outstanding research contributions. Additionally, Hu received an Academic Report Award, highlighting expertise in presenting complex optimization techniques effectively.
🔎 Research Focus
Hu Caie’s research revolves around data-driven optimization, evolutionary optimization, transfer learning, and machine learning applications in antenna design 🤖. The work primarily focuses on uncertainty modeling, surrogate-assisted optimization, and scalable Gaussian processes for solving expensive optimization problems. By integrating AI and computational intelligence, Hu aims to develop more efficient and adaptive optimization techniques for complex engineering challenges.
🔚 Conclusion
Hu Caie is a passionate researcher at the intersection of AI-driven optimization and computational intelligence 💡. With a strong background in machine learning, evolutionary algorithms, and antenna design, Hu’s contributions continue to shape advancements in data-driven engineering solutions. Through groundbreaking publications and leadership in research projects, Hu remains committed to developing more efficient, scalable, and intelligent optimization techniques 🚀.
📚 Publications
A Robust Technique without Additional Computational Cost in Evolutionary Antenna Optimization – IEEE Transactions on Antennas & Propagation (2019) [Cited by: X articles] 🔗 Read here
On Nonstationary Gaussian Process Model for Solving Data-driven Optimization Problems – IEEE Transactions on Cybernetics (2021) [Cited by: X articles] 🔗 Read here
An Uncertainty Measure for Prediction of Non-Kriging Surrogates – Evolutionary Computation (2021, under revision) 🔗 Read here
A Framework of Global Exploration and Local Exploitation using Surrogates for Expensive Optimization – Knowledge-Based Systems (2023) [Cited by: X articles] 🔗 Read here
Scalable GP with Hyperparameters Sharing Based on Transfer Learning for Solving Expensive Optimization Problems – Applied Soft Computing (2023) [Cited by: X articles] 🔗 Read here
Hyperparameters Adaptive Sharing Based on Transfer Learning for Scalable GPs – IEEE Congress on Evolutionary Computation (2022) 🔗 Read here
An Adaptive Model Management Strategy: Balancing Exploration and Exploitation – IEEE Symposium Series on Computational Intelligence (2021) 🔗 Read here
Elliptical Wide Slot Microstrip Patch Antenna Design Using Dynamic Constrained Multiobjective Optimization Evolutionary Algorithm – Artificial Intelligence Algorithms and Applications (2020) 🔗 Read here