Rate My Professor Hamid Alinezhad

Dr. Hamid Alinejad-Rokny holds the position of ARC DECRA Fellow and UNSW Scientia Fellow, serving as Senior Lecturer in the Graduate School of Biomedical Engineering at the University of New South Wales (UNSW Sydney), where he directs the BioMedical Machine Learning Laboratory (BML), leading a team of 25 researchers. He previously served as Honorary Lecturer Fellow at Macquarie University in the Faculty of Science and Engineering from October 2020 to 2023, collaborating on projects including PhD co-supervision and industry funding partnerships. His academic journey includes a Bachelor of Software Engineering (2004-2009), Master of Artificial Intelligence (2009-2012), PhD in BioMedical Machine Learning from UNSW Sydney (2014-2019), and postdoctoral research at the Harry Perkins Institute of Medical Research, University of Western Australia (2018-2019). Earlier, he worked as a Data Scientist in industry (2012-2014).

Alinejad-Rokny's research focuses on biomedical machine learning, systems biology, health data analytics, genomics, bioinformatics, quantitative genetics, and AI applications in medicine, including deep convolutional neural networks, clustering, classification, feature selection, evolutionary algorithms, and next-generation sequencing data analysis from protocols like Hi-C, RNA-seq, and ChIP-seq. He has led over 60 projects, producing more than 100 publications in leading venues such as Nature Communications, Nature Neuroscience, The Lancet, Cell Reports, PLOS Computational Biology, Blood, ICLR, NeurIPS, AAAI, and ACL, amassing over 5,600 citations with an h-index of 40 and high Field-Weighted Citation Impact scores (AI: 7.6; Neuroscience: 4.7; Health Informatics: 4.75). Notable publications include 'A review on deep learning approaches in healthcare systems: Taxonomies, challenges, and open issues' (Journal of Biomedical Informatics, 2021; 419 citations), 'Four-layer ConvNet to facial emotion recognition with minimal epochs and the significance of data diversity' (Scientific Reports, 2022; 142 citations), 'Diagnosis of Brain Diseases in Fusion of Neuroimaging Modalities Using Deep Learning: A Review' (Information Fusion, 2023; 130 citations), and 'Automated diagnosis of cardiovascular diseases from cardiac magnetic resonance imaging using deep learning models: A review' (Computers in Biology and Medicine, 2023; 121 citations). His accolades encompass the highly competitive UNSW Scientia Fellowship ($680,000), ARC Discovery Early Career Researcher Award, NHMRC Ideas Grant, CSIRO Next Generation Graduate Program Grant (totaling $3.5M as lead CI and $10.6M as co-CI), Millennium Prize for Outstanding Early- and Mid-Career Researcher (Lorne Genome, 2025), and various travel and scholarship awards. As Health Data Analytics Program Leader at the AI-enabled Processes Research Centre and Health Theme Leader at UNSW Data Science & AI Hub, he fosters collaborations with Sydney hospitals via Gene2Care and PreGen consortia, leads national initiatives like ClinAI, OmniST, ARGOS, and Molecular AI, organizes over 100 AI-in-medicine seminars, and influences funding through NSF-CSIRO and HUGO panels.