PHD atNorwegian University of Science & Technology (NTNU), Norway
Ammar Ahmed is a Deep Learning Engineer and Researcher with expertise in computer vision and AI. He has worked on projects at the Norwegian University of Science & Technology (NTNU), developing advanced recognition algorithms and multi-modal models. His work includes fine-tuning YOLOv8 for X-ray abnormality detection, achieving significant accuracy improvements. Ammar graduated with a gold medal in Computer Science from Sukkur IBA University and has a strong foundation in machine learning, data analysis, and cloud computing. His projects span image captioning for the visually impaired, student performance prediction, and AI-driven applications, demonstrating his versatility and innovation in the field.
Professional Profiles
Orcid
Googlescholar
Researchgate
Strengths for the Award
Cutting-edge Research in Deep Learning & Computer Vision: Ammar Ahmed’s work on fine-grained recognition algorithms and multi-modal metadata-augmented models demonstrates his expertise in deep learning and computer vision. His ability to achieve state-of-the-art (SOTA) results on complex datasets, such as the 98.9% accuracy in wildlife classification and 87% accuracy in wrist pathology recognition, highlights his innovative approach and technical proficiency.
Publication & Collaboration Experience: His collaboration with a master’s student to co-author a paper at NTNU indicates his involvement in academic research and his ability to work in a team-oriented environment. This experience is crucial for a young researcher and strengthens his profile for the award.
Broad Skill Set: Ammar possesses a comprehensive skill set in deep learning, machine learning, computer vision, and software development. His proficiency in tools like PyTorch, TensorFlow, CUDA, and Docker, along with his experience in cloud computing and web development, make him a versatile candidate capable of tackling a wide range of research problems.
Innovation in Personal Projects: His personal projects, such as the Image Captioning System to Assist the Blind and the Student Performance Indicator, showcase his ability to apply theoretical knowledge to real-world problems. These projects also emphasize his creativity and practical implementation skills.
Academic Excellence: Graduating with a Gold Medal and a high CGPA from Sukkur IBA University, along with being selected for a semester exchange at NTNU, highlights his strong academic background and commitment to excellence.
Areas for Improvement
Long-term Research Impact: While Ammar has demonstrated significant achievements in short-term projects, he could benefit from developing a more focused research agenda that outlines his long-term goals and contributions to the field. This would strengthen his case for the award by showing his potential for sustained impact in his research area.
Publication Record: Although he has co-authored a paper, a more extensive publication record in peer-reviewed journals or conferences would further solidify his standing as a researcher. Increasing his involvement in academic writing and dissemination of his work could be a strategic area of improvement.
Diverse Research Experiences: Expanding his research experience beyond his current domains, such as exploring interdisciplinary collaborations or applying his expertise to new fields, could demonstrate his versatility and adaptability as a young scientist. This could enhance his profile for broader recognition.
Mentorship and Leadership: Taking on more leadership roles in research projects, mentoring junior students, or leading initiatives within his research group could showcase his potential as a future leader in the field, which is often a key consideration for awards aimed at young scientists.
Publications
Enhancing wrist abnormality detection with yolo: Analysis of state-of-the-art single-stage detection models, Publication date: 2024.
Learning from the few: Fine-grained approach to pediatric wrist pathology recognition on a limited dataset, Publication date: 2024.
Conclusion
Ammar Ahmed is a strong candidate for the Research for Young Scientist Award, with significant strengths in cutting-edge research, a broad skill set, and academic excellence. His achievements in developing high-performing models for computer vision tasks and his collaborative research experience make him a promising young scientist. However, to further strengthen his candidacy, he could focus on building a more robust publication record, outlining a long-term research agenda, and taking on leadership roles in research projects. With these improvements, Ammar has the potential to make a lasting impact in his field and be a strong contender for the award.