Summary

Mr. Andrews Tang is a passionate Computer Engineering graduate and a cutting-edge researcher with a focus on Deep Learning and Computer Vision. With strong expertise in machine learning, blockchain, and IoT, he has contributed to high-impact research in agriculture, aviation safety, and food traceability. Currently, he is working as a Research Associate at KNUST’s DIPPER Lab, leading projects that utilize AI for real-world problem-solving.

Education

Bachelor of Science in Computer Engineering (2018-2022)
Kwame Nkrumah University of Science and Technology (KNUST), Kumasi, Ghana

• CGPA: 3.74/4.0 (First Class Honours)
• Awards: Excellent Student’s Award (2020, 2022, 2023)

Professional Experience

Machine Learning Engineer, Aviation Safety Analysis
DIPPER Lab, KNUST (Jan 2024 – Present)

• Led machine learning research to improve the Instrument Landing System (ILS) for low-visibility conditions using CatBoost classifiers and data visualization frameworks.

 Computer Vision Researcher, Red Palm Oil Adulteration Detection
RAIL & DIPPER Lab, KNUST (Jan 2023 – Oct 2023)

• Developed the AfroPALM deep learning models to detect adulteration in red palm oil, achieving over 90% accuracy in field tests.

 Blockchain Researcher, Food Traceability System
DIPPER Lab, KNUST (Mar 2022 – Nov 2022)

• Designed a decentralized food traceability system using Hyperledger Fabric, contributing to the first publication on blockchain-based solutions for food safety in Africa.

Research Interests

Deep Learning

• Applications in computer vision, natural language processing, and AI for real-world applications like food safety, agriculture, and healthcare.

 Computer Vision

• Specializing in AI models for image recognition, disease detection (e.g., tomato diseases), and adulteration detection in food products.

 AI for Safety & Systems

• Focused on machine learning solutions for enhancing safety protocols in critical systems like aviation.

Technical Skills

Python, PyTorch, OpenAI Gym, MobileNetV2, YOLO, Wandb, Deep Learning, Computer Vision

Honours & Awards

Best Poster Award: Deep Learning Indaba (2023)

Member: Black in AI Fellowship (2024)

Early Academic Pursuits

Prof. Yang Ling’s academic journey began with a solid foundation in engineering, leading to her doctorate in 2022 from China Agricultural University. During her doctoral studies, she focused on applying advanced technologies to agricultural automation and machine vision, laying the groundwork for her future research in computer vision and image processing. Her educational background was enriched by a strong interdisciplinary approach, combining engineering with artificial intelligence, which would later fuel her impactful research career.

Professional Endeavors

After completing her Ph.D., Prof. Yang joined the School of Information Engineering and Automation at Kunming University of Science and Technology, where she quickly established herself as a key faculty member. Her role as a lecturer has allowed her to share her expertise in deep learning, computer vision, and automation with the next generation of engineers and researchers. Prof. Yang has also made significant contributions through her leadership in several national and provincial research projects, further solidifying her position in academia and research communities.

Contributions and Research Focus

Prof. Yang’s research spans a wide range of topics in the field of computer vision and image processing. Her primary research interests include:

• Target Detection and Segmentation: Developing algorithms to identify and analyze objects in images or videos with high precision.
• Three-Dimensional Reconstruction: Innovating techniques to recreate three-dimensional models from two-dimensional images, which has important applications in medical imaging, robotics, and environmental modeling.
• Behavior Recognition and Analysis: Leveraging deep learning to understand and predict behaviors, with applications in surveillance, human-computer interaction, and autonomous systems.

Her research has not only contributed to advancing artificial intelligence in these areas but also has practical applications in agricultural machinery and automation, which aligns with the core goals of her academic institute.

Impact and Influence

Prof. Yang’s work has had a significant impact on the field of artificial intelligence and computer vision, both academically and practically. As the principal investigator in multiple high-profile projects, she has contributed to improving agricultural automation systems through advanced imaging and detection technologies. Her work in behavior recognition and three-dimensional reconstruction has applications beyond agriculture, influencing industries such as robotics, surveillance, and healthcare.

Prof. Yang’s publications in journals such as Computers and Electronics in Agriculture, Artificial Intelligence Review, and Archives of Computational Methods in Engineering have not only raised the profile of her work but have also shaped ongoing discussions in her research areas.

Technical Skills

Prof. Yang has mastered a diverse set of technical skills, making her an interdisciplinary expert in her field. Her areas of proficiency include:

• Deep Learning Algorithms: Understanding and developing cutting-edge algorithms for applications in vision systems, including convolutional neural networks (CNNs) and other AI-driven models.
• Image Processing: Expertise in techniques for processing, analyzing, and extracting information from images.
• Behavior Recognition: The use of AI to understand human or machine behaviors based on visual data.

Her ability to bridge theoretical knowledge with practical application makes her an invaluable asset to her research team and to the wider scientific community.

Teaching Experience

As a lecturer at Kunming University of Science and Technology, Prof. Yang is committed to fostering the next generation of engineers and computer scientists. She teaches courses on computer vision, deep learning, and image processing, inspiring students with real-world applications and cutting-edge research. Her approach combines theoretical knowledge with practical insights, providing students with the skills needed to excel in today’s rapidly evolving tech landscape.

Prof. Yang’s mentorship extends beyond the classroom as she actively guides postgraduate students in their research endeavors, particularly in the areas of AI and computer vision.

 Legacy and Future Contributions

Looking to the future, Prof. Yang’s contributions to deep learning and computer vision are poised to influence even more diverse industries. With ongoing participation in large-scale national projects, such as the National Key Research and Development Project and Yunnan Provincial Basic Research Program, she continues to shape the future of AI in agricultural automation and beyond.

Her leadership in pioneering technologies such as three-dimensional reconstruction and behavior analysis will further bridge the gap between academia and industry. Prof. Yang’s future research endeavors will undoubtedly focus on expanding AI applications to new frontiers, from enhancing human-computer interaction to advancing autonomous systems in agriculture and other fields.

 Future Directions and Collaboration

Prof. Yang is dedicated to exploring new avenues for collaboration, especially in interdisciplinary research. Her vision includes expanding her work on target detection, image segmentation, and three-dimensional modeling to solve pressing global challenges. With her leadership, Kunming University of Science and Technology will continue to be at the forefront of AI-driven innovation, further solidifying its position as a hub for cutting-edge computer vision research.

Prof. Yang’s continued collaboration with international research teams, as well as her involvement in national technology projects, ensures her ongoing influence in the field of deep learning and artificial intelligence.