Dr. Amir Anees is a Postdoctoral Research Fellow at the South Western Sydney Clinical Campus, School of Clinical Medicine, UNSW Australia. He holds a PhD in Computer Science, with a research focus on federated learning, privacy-preserving machine learning and secure distributed AI systems. His work is motivated by real-world clinical and healthcare applications, where data privacy, security,and regulatory constraints are critical. Dr. Anees has extensive experience in designing and deploying federated and collaborative learning frameworks, including horizontal and vertical federated learning, as well as studying privacy risks such as gradient inversion and data leakage attacks. He has published in peer-reviewed venues and is actively involved in interdisciplinary research bridging machine learning and healthcare. His current research interests include federated neural networks, trustworthy AI, and privacy-aware model training.
Dr. Amir Anees is a Postdoctoral Research Fellow at the South Western Sydney Clinical Campus, School of Clinical Medicine, UNSW Australia. He holds a PhD in Computer Science, with a research focus on federated learning, privacy-preserving machine learning and secure distributed AI systems. His work is motivated by real-world clinical and healthcare applications, where data privacy, security,and regulatory constraints are critical. Dr. Anees has extensive experience in designing and deploying federated and collaborative learning frameworks, including horizontal and vertical federated learning, as well as studying privacy risks such as gradient inversion and data leakage attacks. He has published in peer-reviewed venues and is actively involved in interdisciplinary research bridging machine learning and healthcare. His current research interests include federated neural networks, trustworthy AI, and privacy-aware model training.