PROFILE SUMMARY

Naveed Ahmad is a dedicated AI Researcher, Research Assistant, and Lecturer with strong expertise in deep learning, medical image analysis, and advanced computer vision systems. As a Research Assistant, he has contributed to multiple high-level research projects focused on medical imaging, intelligent diagnostic systems, and transformer-based feature fusion. His research work involves designing multi-scale segmentation architectures, developing innovative attention mechanisms, and improving domain generalization across diverse medical imaging modalities. Naveed is highly proficient in Python, PyTorch, TensorFlow, and advanced machine learning workflows, with extensive experience working on CT, MRI, ultrasound, and WSI datasets. His research additionally spans Explainable AI (XAI), spatio-temporal analysis, and transformer-driven architectures for clinical decision support.

Naveed has published four peer-reviewed research papers in reputable international journals and conferences, including IEEE and other respected venues, showcasing his strong academic and scientific capabilities. Each work demonstrates his ability to design novel methodologies, conduct rigorous experimentation, and advance state-of-the-art performance within AI. Alongside his research achievements, Naveed has over two years of teaching and lab instruction experience, where he has taught Python Programming, Programming for AI, and Reinforcement Learning. He has supervising student projects and consistently translated complex technical concepts into clear, practical, and impactful learning experiences. This combination of research excellence, teaching experience, and publication record positions Naveed as a capable and promising contributor to advanced AI research and higher scientific discovery.

QUALIFICATION
MS	COMPUTER SCIENCE	Comsats University Islamabad, Pakistan	2023 – 2025
BS	COMPUTER SCIENCE	University of Malakand	2018 – 2022

TEACHING EXPERIENCE
Lecturer	Shifa Tameer-e-Millat University, Islamabad	April 2025 – Ongoing
Visiting Lecturer	Govt. Degree College Lal Qilla Lower Dir, KPK	Spring 2023

HONORS & AWARDS
1.	Merit Certificate in BS
2.	Gold Medal in MS

RESEARCH AREAS / PUBLICATIONS

CV and NLP and Image Analysis

1.      DynamicSwin-Fire: A Dynamic Swin Transformer with Fire-Specific Attention and Edge-Guided Gradient Fusion for Robust Fire

• Accepted in IEEE Transactions on Geoscience and Remote Sensing

(Q1, IF = 8.6, CiteScore = 13.6)

• Ahmad, Naveed, Mariam Akbar, Eman H. Alkhammash, and Mona M. Jamjoom
  • https://ieeexplore.ieee.org/document/11194241/

 

2. QFS-MSGRU: Quantum Feature Selection Approach Integrate with Multi-Scale GRU     Networks for Robust Parkinson’s Disease Prediction

1. • Published in IEEE Access        Q1,  IF = 3.6, Cite Score = 9
   • Ahmad, Naveed, Mariam Akbar, Eman H. Alkhammash, and Mona M. Jamjoom. “QFS-MSGRU: Quantum Feature Selection Approach Integrate with Multi-Scale GRU Networks for Robust Parkinson’s Disease Prediction.” IEEE Access (2025).

3. CN2VF-Net: A Hybrid Convolutional Neural Network and Vision Transformer Framework for Multi-Scale Fire Detection in Complex Environments

1. • Published in Fire (MDPI)        Q1,  IF = 2.7, Cite Score = 3.9
   • Ahmad, Naveed, Mariam Akbar, Eman H. Alkhammash, and Mona M. Jamjoom. “CN2VF-Net: A Hybrid Convolutional Neural Network and Vision Transformer Framework for Multi-Scale Fire Detection in Complex Environments.” Fire 8, no. 6 (2025): 211.

4. FireNet-KD: Swin Transformer-Based Wildfire Detection with Multi-Source Knowledge Distillation

1. •  Published in Fire (MDPI)    Q1,  IF = 2.7, Cite Score = 3.9
   • Ahmad, Naveed, Mariam Akbar, Eman H. Alkhammash, and Mona M. Jamjoom. “FireNet-KD: Swin Transformer-Based Wildfire Detection with Multi-Source Knowledge Distillation.” Fire (2025).

5. One Paper Under Review