Mr. Ahmet Serhat Yildiz | Computer Vision Awards | Best Researcher Award

Mr. Ahmet Serhat Yildiz | Computer Vision Awards | Brunel University of London | United Kingdom

Mr. Ahmet Serhat Yildiz is an emerging researcher in sensing technology with growing expertise in machine learning, deep learning, embedded systems, and multi-sensor fusion, demonstrating strong potential for advanced research roles and academic leadership. He is currently pursuing his PhD in Electronic and Computer Engineering at Brunel University London, where he focuses on real-time object detection, semantic 3D depth sensing, LiDAR–camera fusion, and intelligent autonomous perception systems, aligning closely with sensing applications in robotics, transportation, surveillance, and industrial automation. His academic foundation includes degrees in electronics, electrical engineering, business management, and extensive English language training, providing a multidisciplinary perspective that strengthens his analytical and communication abilities. His professional experience includes roles as a Graduate Teaching Assistant in digital design, embedded systems, and computer architecture, as well as serving as an IoT facilitator, where he mentored learners and contributed to community-oriented technology initiatives. Mr. AHMET SERHAT YILDIZ has developed notable research projects, including FPGA-based embedded game systems, PLC-controlled industrial automation setups, and biomedical sensing circuits for pulse wave velocity measurement, demonstrating strong hands-on engineering skills. His research portfolio includes Scopus-indexed publications on YOLO-based detection models, sensor fusion for autonomous vehicles, and real-time navigation using LiDAR and deep learning frameworks, reflecting his ability to integrate theory with practical sensing applications. His technical skills include Python, PyTorch, embedded C, FPGA development, digital circuit design, PLC programming, and multi-sensor signal processing, enabling him to contribute to both algorithmic and hardware-oriented research environments. His achievements include scholarly publications, increasing citation impact, and recognition through participation in international conferences and multidisciplinary research projects.

Professional Profiles: ORCID | Google Scholar

Featured Publications

Alkandary, K., Yildiz, A. S., & Meng, H. (2025). A comparative study of YOLO series (v3–v10) with DeepSORT and StrongSORT: A real-time tracking performance study. Electronics.
Tunali, M. M., Yildiz, A., & Çakar, T. (2022). Steel surface defect classification via deep learning. International Conference on Computer Science and Engineering (UBMK).
Yildiz, A. S., Meng, H., & Swash, M. R. (2025). Real-time object detection and distance measurement enhanced with semantic 3D depth sensing using camera–LiDAR fusion. Applied Sciences.
Tunali, M. M., Sayar, A., Aslan, Y., Mutlu, İ., & Çakar, T., including Yildiz, A. (2023). Enhancing quality control in plastic injection production: Deep learning-based detection and classification of defects. International Conference on Computer Science and Engineering (UBMK).
Yıldız, A., Mişe, P., Çakar, T., Terzibaşıoğlu, A. M., & Öke, D. (2023). Spine posture detection for office workers with hybrid machine learning. International Conference on Computer Science and Engineering (UBMK).
Yildiz, A. S., Meng, H., & Swash, M. R. (2025). YOLOv8–LiDAR fusion: Increasing range resolution based on image-guided sparse depth fusion in self-driving vehicles. Lecture Notes in Networks and Systems.
Yildiz, A. S., Meng, H., & Swash, M. R. (2024). A multi-sensor fusion approach to real-time bird’s-eye view navigation: YOLOv8 and LiDAR integration for autonomous systems. Korkut Ata Scientific Research Conference Proceedings.

Ms. Hyunseo Kim, Konkuk University, South Korea

Hyunseo Kim is an ambitious student pursuing dual degrees in Biomedical Science and Engineering and Computer Science and Engineering at Konkuk University in Seoul, South Korea. With a strong focus on applying artificial intelligence techniques to the medical domain, he is currently engaged in research at the AI & CV Lab, where he works on projects involving computer vision and audio data for medical applications, including MRI data analysis and hearing loss classification. Hyunseo’s passion for healthcare technology led him to participate in various competitions, winning first place in a medical hackathon for developing a drug side effect management program and third place in a software convergence competition for creating a DNA editing application. Fluent in Korean and proficient in English and Chinese, he is well-equipped for interdisciplinary collaboration. As he approaches graduation, Hyunseo is eager to leverage his skills in AI and programming to contribute to advancements in healthcare and improve the quality of life for individuals.

Work Experience 💻

AI & CV Lab (June 2022 – Present)

Conducting projects on MRI data, including tasks for lacunar detection, Enlarged Perivascular Spaces (EPVS) detection, and microbleed detection.
Engaged in audio data projects, including a study on hearing loss classification and participating in the ADRESSM Challenge, where the team won first place in Alzheimer’s Disease classification tasks.

Achievements 🏆

1st Place in Medical Hackathon (March 2022 – September 2022)

Developed a drug side effect management program in collaboration with school seniors, focusing on patient medication tracking and side effect reporting.

3rd Place in SW Convergence Competition (August 2022 – November 2022)

Created a DNA editing program in partnership with a senior, enhancing the efficiency of DNA sequence editing compared to existing programs.

2nd Place in Big Data Analysis Competition (November 2022)

Participated in a competition organized by CJ Enterprises, focusing on exploratory data analysis (EDA) of corporate financial statements.