Asaad Cheema

Designed a cloud-based computer vision system for luxury product authentication, achieving 90%+ accuracy and reducing manual verification time by 60%. Optimised AWS infrastructure and re-architected data pipelines, cutting cloud costs by 15% and enhancing availability and latency. Exploring agentic AI to personalise shopping experiences and boost customer engagement.

Architected a Federated Meta-Learning framework, reducing training time by 90% and enabling convergence in 5–10 epochs using just 10% data. Deployed models improving anomaly detection by 15% and cutting costs by 25%. Built a topology-aware solution that reduced wireless data transmission by 70%. Developed a Human-in-the-Loop diagnostic system using LLMs, boosting prediction interpretability by 20–40%.

Researched graph network robustness under evolving structures and benchmarked parameter-efficient fine-tuning (PEFT) methods for LLMs. Demonstrated that LoRA reduces trainable parameters by over 95% while preserving full performance, enabling deployment on consumer-grade hardware.