Usamah Zaheer's Projects

Developed an Android application for deploying machine learning models directly on edge devices, enabling real-time object detection using YOLO, Mask R-CNN, and SSD architectures. The app was designed for low-latency, on-device inference without requiring cloud connectivity.

Optimised models using TensorFlow Lite with quantisation and pruning techniques, significantly reducing model size and inference time while maintaining detection accuracy. The project demonstrated practical deployment of state-of-the-art detection models on resource-constrained mobile hardware.

Developed a robot utilising 26 sensors with SLAM (Simultaneous Localisation and Mapping) technology and 360-degree vision for fully autonomous navigation. The system fused data from multiple sensor modalities to build real-time environmental maps and navigate complex indoor environments.

Advanced path planning algorithms to optimise navigation efficiency and obstacle avoidance. Developed comprehensive integration and unit tests in C++ and Python, ensuring reliability and robustness of the navigation stack across diverse environments and edge cases.

Contributed to the embedded software powering all Dyson air purifiers, including the Pure Cool product line. Worked with C and Python to develop firmware that manages sensor data processing, device communication, and real-time control logic.

Designed the backbone logic behaviour system for hardware communication using FreeRTOS, enabling reliable real-time task scheduling and inter-component messaging. Updated a fundamental library stack shared across 10+ projects, improving code reuse and maintainability across the Dyson embedded ecosystem.

Classified high-resolution satellite images for forest fire detection using CNNs, Random Forest, and Support Vector Machines. The research applied AI for Earth Observation (AI4EO), using deep learning to identify fire-affected regions from multispectral satellite imagery with high accuracy.

Evaluated multiple CNN architectures for autonomous vehicle applications, benchmarking performance across detection accuracy, inference speed, and memory footprint. Optimised models with Transfer Learning and TensorRT to achieve real-time inference suitable for deployment on vehicle-mounted compute platforms.

Led the design and implementation of AI agents for Sales Development Representatives (SDRs), integrating NLP and LLMs to build accurate and scalable solutions. The system leveraged RAG (Retrieval-Augmented Generation) to enhance AI response precision by grounding outputs in domain-specific knowledge bases.

Managed cloud infrastructure on Google Vertex AI, orchestrating data pipelines with Databricks and Snowflake. Built semantic search and ML workflow orchestration using LangChain and LlamaIndex, enabling the agents to retrieve and reason over large document corpora.

Oversaw containerised deployment with Docker and Kubernetes, implementing MLOps practices with MLflow for experiment tracking, model versioning, and reproducible deployments.