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)