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Asif Ameer

Asif Ameer

Lecturer
  • Department Of AI & Data Science
  • asif.ameer@nu.edu.pk
  • (041) 111-128-128
  • Ext: 582

Introduction

  • Interests:
  • Metaheuristics Optimization , Evolutionary Computing , Artificial Intelligence, Multi objective / Many Object Optimization, AutoML / Neural Architecture Search , Precision Agriculture (PA), Machine/Deep Learning, Computer Vision

Mr. Asif Ameer has joined FAST in 2020 and working as a Lecturer in Department of Artificial Intelligence and Data Science. Before joining FAST, he has served in various organizations at national level. His core area of research is machine learning, deep learning and artificial intelligence. He has successfully completed various MOOC’s in the area of ML/DL with distinctions from well-renowned international organizations. He has published his research with joint collaboration of Kyungpook National University, South Korea.

Interest

Precision Agriculture (PA)

Precision Farming (PF)

Machine/Deep Learning

Computer Vision

Education

  • PhD. (Computer Science), FAST National University of Computer & Emerging Sciences, Lahore Pakistan, Continue

    MS (Computer Science), University of Agriculture Faisalabad, 2018

  • BS (Computer Science),  Riphah International University Faisalabad, 2016

Publications

Ameer, A., Bashir, M., Younas, I., & Fayyaz, M. (2026). Model-Free Surrogate-Assisted Neural Architecture Search for Evolving Variable-Length Dense Blocks. Expert Systems with Applications, 132442.

Conference Paper

  • Farooq, M. Trinh, A. Ly, A. Ameer, I. Razzak and S. Singh, “Seeing Beyond the Airways: Asthma Prediction via Cross-Attention on Dual Retinal Modalities,” 2025 40th International Conference on Image and Vision Computing New Zealand (IVCNZ), Wellington, New Zealand, 2025, pp. 1-6, doi: 10.1109/IVCNZ67716.2025.11281841.
  • Farooq, U., Trinh, M., Ly, , Ameer, A., Razzak, I., & Singh, S. (2026, February). Asthma identification through multimodal data fusion: towards reliable clinical decision support. In Eighteenth International Conference on Machine Vision (ICMV 2025) (Vol. 14114, pp. 80-87). SPIE.

Collaborations at National and International Level

Our research team works in close collaboration with the University of New South Wales (UNSW) Machine Learning and Ophthalmology departments. This interdisciplinary partnership bridges the gap between advanced artificial intelligence and clinical eye care, focusing on the development of cutting-edge diagnostic tools. By training sophisticated machine learning algorithms on massive datasets of retinal imagery and clinical records, we aim to automate the early detection of blinding diseases such as glaucoma, diabetic retinopathy, and macular degeneration. This joint effort combines UNSW’s world-class computational expertise with frontline ophthalmic insights to create scalable, high-accuracy screening solutions that can prevent vision loss globally.

 

Detail of Funded Projects