I am a PhD candidate in the Heart Research Group at the Murdoch Children’s Research Institute (Royal Children’s Hospital), specializing in cardiovascular modeling for cuffless blood pressure monitoring. My work spans advanced signal processing and machine learning, particularly deep learning to translate complex data into actionable clinical insights.With a strong foundation in electrical engineering and extensive hands on experience in Python, C++, TensorFlow, and PyTorch, I thrive at the intersection of theory and practical deployment. I’ve led and contributed to a diverse range of projects including EEG-based seizure detection, federated learning for medical imaging, predictive fault analysis in transformers and YOLO based object detection in aerial imagery. I’ve developed the skills to deploy scalable AI systems on edge devices using TensorRT, and NVIDIA Jetson TX2.I place a high value on building robust, reproducible pipelines and leveraging MLOps best practices, from Docker containerization to scalable deployments on Azure. My aim is to create production grade systems that can be confidently integrated into clinical workflows and beyond.Recognized with a Melbourne Research Scholarship, a University Gold Medal, and multiple certificates of excellence, I also serve as an educator and collaborator, fostering partnerships across academia and industry. My goal is to advance AI driven healthcare solutions that not only push the boundaries of research, but also deliver meaningful, lasting impact.