Rate My Professor Sami Azam

His research specializations include machine learning, artificial intelligence, deep learning, advanced signal processing, image analysis, and explainable AI. Azam develops and implements cutting-edge machine learning models for the automated detection, classification, and prediction of patterns in images and diverse datasets. Key applications encompass biosignals such as electroencephalogram (EEG), electrocardiogram (ECG), and acceleration plethysmogram (APG), medical image analysis for early disease diagnosis, and solutions for data security and Internet of Things (IoT) challenges, including privacy frameworks for electronic health records. As a key member of the Biomedical Engineering and Health Informatics research group, he has authored 194 research outputs, comprising 123 journal articles, 58 conference papers, 10 review articles, and 1 book chapter. Prominent publications include "Attention-driven deep object detection for improved gallbladder cancer diagnosis from ultrasound images" (2026, Engineering Applications of Artificial Intelligence), "A review of artificial intelligence techniques for anomaly detection in smart grid" (2026, Artificial Intelligence Review), "Clinical Profile Identification of Indigenous Infants With Bronchiolitis Through Using Unsupervised Feature Extraction and Clustering" (2026, Pediatric Pulmonology), and "HANS-Net: Hyperbolic Convolution and Adaptive Temporal Attention for Accurate and Generalizable Liver and Tumor Segmentation in CT Imaging" (2026, IEEE Transactions on Radiation and Plasma Medical Sciences). Azam supervises higher degree research students and contributes to 15 United Nations Sustainable Development Goals.