Graduate Research Assistant

I am an interdisciplinary researcher working at the intersection of wireless communications, signal processing, machine learning, computer vision, biosensors, and intelligent networking. My recent research focuses on applying deep learning, explainable AI, and large language models to next-generation communication systems and medical diagnostics.

My work includes developing intelligent resource allocation frameworks for holographic MIMO-enabled 6G networks using deep learning and explainable AI techniques. I have published research on deep learning-based resource allocation for holographic MIMO systems and am also working on explainable AI approaches for intelligent network optimization in advanced wireless communication environments.

I have also contributed to medical image analysis through deep learning-based frameworks for lung cancer classification and Mpox diagnosis. My research in this area combines convolutional neural networks, Vision Transformers, and explainable AI techniques to improve diagnostic accuracy and interpretability.

In addition, I have a strong research interest in large language models, prompt engineering, and context engineering. I co-authored a comprehensive survey on prompt engineering and context engineering techniques in LLMs published in Computer Science Review. I am also involved in research exploring how LLMs, foundation models, and intelligent metasurfaces can transform integrated sensing and communication systems, enabling future networks to move from sensing toward reasoning.

My broader interests include deep learning for wireless systems, signal processing, biosensors, medical image analysis, explainable AI, intelligent networking, and LLM-driven communication frameworks.