معرفی
Illya Bakurov is a Researcher in the Department of Computer Science and Engineering at Michigan State University's College of Engineering. His work focuses on advancing genetic programming (GP) and its applications in machine learning, computer vision, optimization, and medical informatics. Key research themes include developing novel GP-based algorithms, exploring their use in structural similarity analysis, image quality assessment, and addressing ill-posed problems in computer vision. His contributions also extend to optimization libraries and pandemic impact studies.
Bakurov's research interests span the theoretical and applied aspects of genetic programming, emphasizing its role in solving complex computational problems across disciplines. Recent efforts include studies on dynamic populations in GP, sharpness-aware minimization techniques, and reduced-order modeling for quantum systems. He has also contributed to stacked generalization frameworks and the development of open-source optimization tools.
His publications reflect a strong focus on algorithmic innovation, with applications ranging from medical decision support systems to economic analysis. While no awards are explicitly listed, his prolific output indicates sustained academic engagement and interdisciplinary impact.
