About
Abdur Rehman Khan is an AI & Data Science expert with a strong passion for Machine Learning, Deep Learning, and Generative AI. He specializes in Computer Vision, UAV Analytics, and AI-driven automation, leveraging advanced technologies to solve real-world problems.
Currently, he serves as an AI & Data Science Instructor at NAVTTC – Quaid-e-Azam University, where he trains professionals in Python, SQL, TensorFlow, Power BI, and AI model development. With hands-on experience in data science, predictive modeling, and business analytics, he has worked across various industries, applying AI to smart cities, disaster management, and medical imaging.
His research contributions include published papers in AI-driven UAV localization, deep learning for disaster scenarios, and 3D medical imaging. Holding a 3.8 CGPA in MS (AI & Data Science) from Air University Islamabad, he is now actively pursuing PhD opportunities and industry roles to further advance AI research and applications.
Key Expertise:
✅ Machine Learning & Deep Learning (TensorFlow, PyTorch, Scikit-learn)
✅ Generative AI & LLMs (Chatbots, AI-driven automation)
✅ Computer Vision & UAV Analytics (RSSI-based localization, smart city solutions)
✅ Big Data & Business Intelligence (Power BI, Tableau, SQL)
✅ AI Research & Publications (CT image reconstruction, UAV task allocation)
Abdur Rehman is always open to PhD collaborations, AI research projects, and industry roles in ML & Data Science. He is eager to contribute to cutting-edge AI solutions that drive innovation and impact.
📩 Let’s Connect: [email protected]
Employment
Employment history is unavailable.
Education
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Edwardes College Bachelor of Science (BS)2016 - 2020
Projects & Funding
Projects & funding information is unavailable.
Publications (4)
- AGI-Enabled Robotics for Healthcare Industry Save
- Improving UAV Performance in Disaster Scenario Using Deep Learning Enabled RSSI-Controlled Task Allocation Save
- RSSI Guided Localization Using Genetic Neural Fusion in Multi-UAV Network Save
- A Systematic Literature Review of 3D Deep Learning Techniques in Computed Tomography Reconstruction Save