Rate My Professor Farshid Hajati

Dr. Farshid Hajati is a Senior Lecturer in Data Science at the School of Science and Technology, University of New England. He possesses a PhD from Western Sydney University, a Master of Engineering from Amirkabir University of Technology, and a Bachelor of Engineering from K.N. Toosi University of Technology. Prior to joining UNE, Dr. Hajati served as a Senior Lecturer and Course Coordinator at Victoria University, Sydney, where he managed the Master of Applied Information Technology and Bachelor of Cybersecurity programs. He taught artificial intelligence and machine learning subjects, receiving three teaching excellence awards for high-quality teaching and outstanding student feedback. Earlier, he was a researcher at the Institute for Integrated and Intelligent Systems at Griffith University, focusing on computer vision and image processing for biometric systems, including 2D and 3D face recognition. His publications from this period appeared in top-tier venues such as Pattern Recognition by Elsevier. Dr. Hajati also held an honorary research fellowship at the Graduate School of Health, University of Technology Sydney, developing deep learning models for early detection of ocular diseases, with results published in leading conferences including NeurIPS 2023. In industry, he worked as a Senior Data Scientist at the Australian Institute of Health and Welfare and the Australian Government Department of Health and Aged Care, applying machine learning to national health datasets like Medicare Benefits Schedule, Pharmaceutical Benefits Scheme, National Disability Insurance Scheme, and aged care services.

At UNE, Dr. Hajati teaches computer science and statistics units such as COSC102 Data Science Studio 1, STAT330/430 Statistical Learning, COSC240 Operating Systems, and ICT100 Computational Thinking. His research interests encompass machine learning, artificial intelligence, computer vision, and AI in medicine. He leads research projects and supervises master's and PhD students. Key publications include 'Classification of Direct Microscopic Fungi Images Using Optimized Graph Networks' (Biomedical Signal Processing and Control, 2025), '3DECG-Net: ECG Fusion Network for Multi-Label Cardiac Arrhythmia Detection' (Computers in Biology and Medicine, 2024), 'Post-Cardiac Arrest Outcome Prediction Using Machine Learning: A Systematic Review and Meta-Analysis' (International Journal of Medical Informatics, 2024), 'RVD: A Handheld Device-Based Fundus Video Dataset for Retinal Vessel Segmentation' (NeurIPS, 2023), 'Surface Geodesic Pattern for 3D Deformable Texture Matching' (Pattern Recognition, 2017), and '2.5D Face Recognition Using Patched Geodesic Moments' (Pattern Recognition, 2012). His work contributes to advancements in biomedical signal processing, disease prediction, and AI applications in healthcare.