Texas A&M University - United States
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🎓 Early Academic Pursuits
Mr. Zubair Saeed’s academic journey is marked by a consistent dedication to innovation and excellence in the field of computer engineering. Beginning with a Bachelor’s degree in Computer Engineering from HITEC University, Pakistan, he graduated with a stellar CGPA, securing a Silver Medal and receiving a fully funded final-year project grant from IGNITE Pakistan. His undergraduate design project on IoT-based plant disease detection showcased his early interest in using machine learning for practical, real-world applications. Pursuing his Master's in Computer Engineering at UET Taxila, he graduated with a Gold Medal, focusing on real-time small object detection for UAVs. This rigorous academic foundation paved the way for his Ph.D. at Texas A&M University, where he continues to advance his expertise in AI and medical imaging.
💼 Professional Endeavors
Mr. Saeed’s professional experience is a testament to his versatility and impact in artificial intelligence and computer vision. At Texas A&M University, he serves as a Graduate Teaching Assistant, teaching lab courses in machine learning and digital system design. His research assistant role at Texas A&M University at Qatar involved critical data collection and analysis at Hammad Medical Corporation, where he worked on AI solutions for segmenting organs-at-risk and tumors. His prior roles at UET’s Swarm Robotics Lab and as a researcher for CRD highlight his expertise in computer vision and small object detection, where he conducted impactful projects related to COVID-19 detection, plant disease identification, and real-time pothole detection.
🔬 Contributions and Research Focus
Mr. Zubair Saeed’s contributions center around AI-driven medical imaging, specifically focusing on AI-Based Automatic Brain Tumor Detection and lung cancer detection. His Ph.D. research is concentrated on predicting radiotherapy treatment responses for lung and head-neck cancers through advanced AI. His work integrates AI with medical imaging to develop segmentation solutions that accurately detect tumors and organs-at-risk. Additionally, he has published significant research on disease detection, small object detection, and cancer classification in prestigious, peer-reviewed journals. His work on AI-Based Automatic Brain Tumor Detection represents a groundbreaking approach that aims to enhance accuracy and efficiency in diagnostics, ultimately benefiting healthcare outcomes.
🌍 Impact and Influence
Mr. Saeed’s influence extends beyond his publications. As a reviewer for various IEEE journals, he actively contributes to the academic discourse in artificial intelligence and machine learning. His work has earned him numerous awards, including a Deep Learning Training Certificate from LUMS, the Best Article Presenter award at ICRAI’21, and several recognitions for his contributions in artificial intelligence. His efforts in AI-Based Automatic Brain Tumor Detection underscore his impact on the field, with his methodologies providing new perspectives for future AI applications in medical diagnostics.
🏆Academic Cites
Mr. Saeed’s research has garnered significant academic citations, reflecting the scientific community’s acknowledgment of his contributions. His work on AI and medical imaging is frequently referenced in research related to cancer detection and AI in healthcare. His expertise in fields such as small object detection and AI-driven diagnostic tools is highly regarded, with his work laying the groundwork for further advancements in AI-Based Automatic Brain Tumor Detection and other medical imaging applications.
🌟 Legacy and Future Contributions
As he continues his Ph.D. studies, Mr. Saeed’s future contributions are set to influence the intersection of AI and healthcare profoundly. His commitment to developing AI-based diagnostic tools that enhance the accuracy of treatments for life-threatening conditions, such as brain and lung cancers, positions him as a pioneer in the field. His legacy will be characterized by his significant contributions to AI-Based Automatic Brain Tumor Detection, AI-driven diagnostics, and his broader influence on machine learning in medical imaging. His career promises to leave a lasting impact, setting new standards for precision in healthcare technology.
📝Notable Publication
📝An Efficient Ensemble Approach for Brain Tumors Classification Using Magnetic Resonance Imaging
Authors: Saeed, Z., Torfeh, T., Aouadi, S., Ji, X., Bouhali, O.
Journal: Information (Switzerland)
Year: 2024
Citations: 0
📝Cancerous and Non-Cancerous MRI Classification Using Dual DCNN Approach
Authors: Saeed, Z., Bouhali, O., Ji, J.X., Aouadi, S., Torfeh, T.
Journal: Bioengineering
Year: 2024
Citations: 2
📝Demand Forecasting in Supply Chain Management for Rossmann Stores Using Weather Enhanced Deep Learning Model
Authors: Ul Haq Qureshi, N., Javed, S., Javed, K., Raza, A., Saeed, Z.
Journal: IEEE Access
Year: 2024
Citations: 0
📝Advances, Application and Challenges of Lithography Techniques
Authors: Raza, A., Saeed, Z., Aslam, A., Habib, K., Malik, A.N.
Conference: 2024 5th International Conference on Advancements in Computational Sciences (ICACS 2024)
Citations: 1
📝On-Board Small-Scale Object Detection for Unmanned Aerial Vehicles (UAVs)
Authors: Saeed, Z., Yousaf, M.H., Ahmed, R., Velastin, S.A., Viriri, S.
Journal: Drones
Year: 2023
Citations: 12
📝A Review of Swarm Robotics in a NutShell
Authors: Shahzad, M.M., Saeed, Z., Akhtar, A., Baloach, N.K., Hussain, F.
Journal: Drones
Year: 2023
Citations: 12