Biography

Dr. Syed Farhan Alam Zaidi is a Specially Appointed Assistant Professor at the World Premier International Research Center Initiative (WPI), Osaka University, Japan. He received his Doctor of Engineering in Computer Science from Chung-Ang University, Republic of Korea, where he specialized in artificial intelligence and machine learning. Prior to joining Osaka University, he worked as a postdoctoral researcher at Chung-Ang University and as a Senior AI Researcher in industry, contributing to the development of advanced AI solutions for computer vision and monitoring tasks.

Research Overview

My research focuses on the application of artificial intelligence to advance biomedical research and precision medicine. I am developing AI methods for the analysis of medical images, particularly MRI and X-ray imaging, for osteoarthritis diagnosis and prognosis. My work also integrates computational biology and bioinformatics to analyze genomics and single-cell transcriptomic data for biomarker discovery and disease mechanism analysis. By combining multimodal clinical, imaging, and molecular data, I intend to develop patient-specific digital twin models to improve disease prediction, support drug discovery, and enable personalized therapeutic strategies.

Selected Publications

• Niaz, A., Umraiz, M., Zaidi, S.F.A., Song, H.C., Akram, F. and Choi, K.N., 2026. Multi-Scale Feature and Prompt Learning for Few-Shot Medical Image Anomaly Detection. IEEE Access.
• Niaz, A., Umraiz, M., Zaidi, S.F.A., Akram, F. and Choi, K.N., 2026. Synthesis-guided unsupervised anomaly detection in industrial images with large language model-driven analysis. Neural Computing and Applications, 38(2), p.21.
• Khan, H., Zaidi, S.F.A., Shah, P.M., Balakrishnan, K., Khan, R., Waqas, M. and Wu, J., 2025. MorphGen: Morphology-Guided Representation Learning for Robust Single-Domain Generalization in Histopathological Cancer Classification. arXiv preprint arXiv:2509.00311.
• Shah, P.M., Zeb, A., Shafi, U., Zaidi, S.F.A. and Shah, M.A., 2018, September. Detection of Parkinson disease in brain MRI using convolutional neural network. In 2018 24th international conference on automation and computing (ICAC) (pp. 1-6). IEEE.