معرفی
Jinho Shin is an active researcher and likely faculty member specializing in computer vision, machine learning, and their applications across various domains including biometrics, fraud detection, and semiconductor manufacturing. His work spans from theoretical foundations in queueing theory to cutting-edge applications of deep learning and federated learning systems.
Dr. Shin's research interests focus on computer vision (particularly face recognition and anti-spoofing systems), machine learning algorithms, federated learning frameworks, and applications in healthcare fraud detection and semiconductor manufacturing optimization. His work demonstrates expertise in both theoretical foundations and practical implementations of AI systems.
His recent publication trend shows a strong focus on computer vision security applications, particularly face anti-spoofing techniques using advanced neural network architectures like Vision Transformers. He has also made significant contributions to federated learning through his ProtoFL framework. Earlier in his career, he focused more on industrial applications in semiconductor manufacturing and queueing theory.
Dr. Shin has established productive collaborations with researchers including Youngjun Kwak, Minyoung Jung, Changick Kim, and James R. Morrison, indicating active participation in research communities across computer vision, machine learning, and industrial engineering domains.
