Hamad Akram

Developed an AI-powered worker safety monitoring system using computer vision, integrating it with an HTML dashboard and Flask backend for real-time monitoring. Deployed the complete system on Azure using Container Instances and Virtual Machines. Built Chatbots and RAG applications using LLMs like gpt-4o, LLaMA, and Phi, leveraging Azure OpenAI for deployment and prompt engineering.

Designed complete deep learning pipelines for retail analytics, focusing on foot traffic, customer journey, and demographics. Trained, optimized, and deployed neural networks on edge devices for object detection, tracking, segmentation, pose estimation, and classification. Leveraged Nvidia tools like TensorRT and Deepstream for efficient inference and processing of multiple video streams on edge d

Worked on object detection and counting problems, specifically with ReDet, an oriented object detection model, to detect shrimps. Integrated training and inference code for the ReDet model on the company's GitHub repository, optimizing performance with anchor generation and data augmentation. Also worked on deep metric learning to find similarity between detected shrimps, utilizing triplet loss, a

Completed a Master of Science in Electrical Engineering. Focused on advanced topics within the field, contributing to a deeper understanding of electrical systems and technologies.

Obtained a Bachelor of Engineering in Electrical Engineering. Gained foundational knowledge and practical skills in electrical engineering principles and applications.