I am Hassan Raza, a Lecturer in the Department of Artificial Intelligence at the National University of Technology (NUTECH), Islamabad. With over four years of experience as a Full Stack AI/ML Engineer and Generative AI Specialist, I bring a unique blend of industry-standard technical expertise and academic research to the classroom. My primary research interests lie at the intersection of Medical Image Analysis, where I develop hybrid Transformer-based frameworks for multi-organ segmentation, and Generative AI, focusing on building context-aware, Agentic RAG-based systems for intelligent automation. Additionally, I lead innovation in precision agriculture through real-time object detection models for sustainable farming. As an educator, I am committed to equipping students with the practical skills necessary to apply advanced AI techniques to solve complex, real-world challenges in healthcare and automation.
Lecturer, Department of Artificial Intelligence, National University of Technology (NUTECH), Islamabad (April 2026 – Present)
• Teaching undergraduate courses in Machine Learning, Artificial Intelligence, and Data Science, with a focus on practical implementation.
• Designing course content, lab activities, and assessments to strengthen students' programming and ML problem-solving skills.
• Supervising and mentoring student projects related to machine learning and computer vision applications.
• Supporting academic activities including student evaluation, curriculum implementation, and research-oriented guidance.
Teaching Assistant, Department of Computer Science, COMSATS University Islamabad (CUI), (Feb 2026 - Jul 2026)
• Assisted in conducting lab sessions and tutorials for undergraduate computing and machine learning-related courses.
• Supported students in implementing ML algorithms using Python and understanding model evaluation techniques.
• Assisted in preparing assignments, quizzes, and grading under faculty supervision.
Guided students in course projects related to machine learning and computer vision.
Machine Learning Engineer (SE-1), Payactiv SDS-IT, Islamabad (Jan 2025 – Jul 2025)
• Developed an AI-powered customer support chatbot using AWS Bedrock and OpenSearch, improving response automation and accuracy.
• Optimized LLM pipelines using Retrieval-Augmented Generation (RAG), enhancing retrieval performance and reducing query resolution time.
• Designed and deployed scalable AI microservices using FastAPI and Docker, ensuring high availability and production stability.
• Integrated AWS cloud services for secure, scalable, and cost-efficient deployment of AI applications.
• Implemented secure API authentication and data encryption to ensure compliance and protection of sensitive data.
Research Assistant (HEC NRPU Funded Project), National University of Technology, Islamabad (Oct 2024 – Jan 2025)
Project: Artificial Intelligence (AI) Based Smart Precision Agriculture for Rural Areas of Pakistan
• Developed and evaluated YOLO-based models for real-time cotton weed detection and classification using UAV imagery.
• Conducted dataset preprocessing, annotation verification, model training, and performance evaluation for field-based detection tasks.
• Contributed to research writing and experimentation, supporting publication-oriented work in AI-driven precision agriculture.
• Worked with precision agriculture technologies including AI, UAVs, and IoT-based monitoring systems to address sustainable farming challenges.
Machine Learning Engineer, IRIS Labs — Austin, USA (Remote), (Sep 2023 – Jan 2025)
• Developed AI-based services using Python and FastAPI, including intelligent Q&A solutions and chatbot systems for the Bizdemy project.
• Designed and deployed deep learning models for medical imaging applications, including brain tumor detection, diabetes detection from thermal images, and neuropathy detection.
• Built anomaly detection systems for industrial use-cases, including on/off anomaly detection and motor vibration-based fault detection using time-series sensor data.
• Developed a computer vision-based deepfake detection model for identifying manipulated media content.
• Implemented NLP solutions using LLMs and RAG architectures, improving contextual retrieval and response generation for real-world applications.
Academia
MS in Computer Science, COMSATS University Islamabad, Pakistan (2025)
BS in Computer Science, The Islamia University of Bahawalpur, Pakistan (2022)
I have research experience in medical image segmentation and computer vision, and I have published in a peer-reviewed journal (MDPI AgriEngineering) on UAV-based crop and weed detection using YOLO models. My current research interests include Medical AI, Vision Transformers, Multi-Organ Segmentation, and Multimodal Learning.
I have taught a variety of undergraduate-level courses in Computer Science and Artificial Intelligence. Below are the courses I have been involved in teaching:
- Machine Learning
- Artificial Intelligence
- Deep Learning / Artificial Neural Networks
- Data Science and Data Analytics
- Python Programming for Machine Learning
- Database Systems
- Computer Vision
- Data Structures and Algorithms
- Object-Oriented Programming (OOP)
- Programming Fundamentals
My research interests lie in Medical Artificial Intelligence, Deep Learning, Computer Vision, and Transformer-based architectures, with a strong focus on medical image segmentation and clinical decision support systems. My MS thesis at COMSATS University Islamabad investigates multi-organ CT segmentation using a hybrid CNN–Transformer framework, aiming to improve segmentation accuracy through effective feature fusion and attention-based learning.
I have also contributed to applied AI research in precision agriculture, where I worked on UAV-based crop growth and cotton weed detection using state-of-the-art YOLO models under an HEC NRPU-funded project at NUTECH. This work resulted in a peer-reviewed publication in MDPI (AgriEngineering).
In addition to academic research, I have industry experience developing AI systems, including medical imaging models, anomaly detection solutions, and LLM/RAG-based intelligent chatbots, bridging research innovation with real-world deployment. My long-term research goal is to advance robust and efficient deep learning models for healthcare imaging, multimodal learning, and real-time AI applications.
Publications:
Hassan Raza, Muhammad Abu Bakr, Sultan Daud Khan, Hira Batool, Habib Ullah, and Mohib Ullah. "Benchmarking YOLO Models for Crop Growth and Weed Detection in Cotton Fields." AgriEngineering 7, no. 11 (2025): 375.
Ongoing Research Projects:
Advancing Multi-Organ CT Segmentation with a Hybrid Transformer Framework and Single-Level Fusion.
