Nawaf Aldawsari

Innovative and results-driven engineering professional with expertise in biomedical technologies, data analysis, and strategic problem-solving. Adept at combining technical knowledge in areas such as medical device development, software systems, and data analytics to deliver impactful solutions. Demonstrated success in managing cross-functional projects, providing actionable insights through data-driven approaches, and utilizing cutting-edge technologies such as computer vision and mixed reality. Strong communication and collaboration skills, with experience in working across diverse industries, including healthcare, consulting, and technology. Passionate about leveraging engineering principles and analytical frameworks to drive business value, optimize processes, and improve outcomes for clients and stakeholders.

· Specialization: Expertise in Biomedical Engineering with a deep focus on Computational Engineering and Robotics, particularly in integrating XR (extended reality) systems to enhance telesurgical applications.

· Research Focus: Committed to pioneering advancements in XR technologies for medical use, aiming to transform traditional surgical methods by integrating immersive, real-time 3D visualization and interaction.

• Mixed Reality (MR) Systems for Surgical Assistance:
• Applied mixed reality technologies to assist with surgical guidance, enabling real-time visualization of medical imaging data in augmented reality (AR) environments.
• Utilized Unity and Microsoft HoloLens to implement MR solutions that allow surgeons to visualize 3D anatomical structures during complex procedures, improving precision and reducing errors in minimally invasive surgeries.
• Computer Vision-Based Guidewire Tracking:
• Implemented computer vision algorithms for real-time detection and tracking of guidewires in endovascular surgeries, utilizing established machine learning techniques such as convolutional neural networks (CNNs).
• Worked on 2D to 3D reconstruction of guidewire paths, providing more accurate spatial representation and enhancing surgical navigation.
• Machine Learning in Biomedical Systems:
• Leveraged machine learning models to predict the optimal guidewire path in complex vascular environments, enhancing surgical outcomes and reducing procedural time.
• Explored reinforcement learning techniques to assist in automating guidewire navigation, providing surgeons with real-time feedback and decision support.
• 3D Modeling and Simulation for Surgical Training:
• I used 3D-printed phantoms of blood vessels to test guidewire navigation systems, utilizing realistic anatomical models for surgical simulation.
• Collaborated with cross-functional teams to validate the accuracy of MR-guided procedures in real-world surgical settings.