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Dr. Asma Naseer

Associate Professor
HEC Approved PhD Supervisor

(042) 111-128-128 Ext:634


  • PhD (CS), NUCES, Lahore (2018)
  • MS (CS), NUCES, Lahore (2008)
  • MIT, Al-Khair University, AJK (2002)


Dr. Asma Naseer is associated with National University of Computer and Emerging Sciences since August 2021 in the FAST School of Computing. Prior to joining NUCES, she was a faculty member at University of Management and Technology in the Department of Computer Science from 2010 till 2021. She has worked in the area of machine learning, deep learning, computational linguistics, image processing, medical imaging, natural language processing etc. She has been involved in teaching and research at undergraduate as well as graduate levels and has supervised numerous final year projects and Thesis. Dr. Asma Naseer has been awarded full scholarship from HEC and other local and foreign bodies for her postgraduate and doctoral studies. She has published a number of research papers in international journals and conferences of high repute. Dr. Asma has hands-on experience in the IT industry and has worked in different organizations. She has practical experience of developing different projects under PAN Localization which involves 11 Asian countries. Some of the projects are Dareecha (Window) website, Rule based English to Urdu Machine Translation System, Rule based urdu stemmer using exception lists, Sindhi Dictionary etc.  She has around 14 years of full time teaching and research experience.


Distinctions / Scholarships

·         Scholarship for Summer School at University of Konstanz Germany in 2009 Funded by University of Konstanz, Germany

·         Scholarship for MS Leading to PhD. in 2006 Funded by HEC (Higher Education Commission)

·         Scholarship for Java Certification in 2001 Funded by PITB (Punjab Information Technology Board)

·         Scholarship for Oracle Certified Programmer in 1999 Funded by PITB (Punjab Information Technology Board)

Areas of Research

  • Image Processing
  • Medical Imaging
  • Computer Vision
  • Deep Learning
  • Natural Language Processing 



Journal Publications

  • Asma Naseer, Sarmad Hussain, Kashif Zafar, Ayesha Khan,A Novel Normal to Tangent line (NTL) Algorithm for Scale Invariant Feature Extraction for Urdu OCR, International Journal on Document Analysis and Recognition (IJDAR), Springer, 30 Nov, 2021. Impact Factor 2.08
  • Naseer, Asma, Maria Tamoor, and Arifah Azhar. "Computer-aided COVID-19 diagnosis and a comparison of deep learners using augmented CXRs." Journal of X-Ray Science and Technology Preprint: 1-21. Impact Factor 1.53
  • Naseer, Asma, et al. "Computer-Aided Brain Tumor Diagnosis: Performance Evaluation of Deep Learner CNN Using Augmented Brain MRI." International Journal of Biomedical Imaging 2021 (2021).
  • Naseer, Asma, and Kashif Zafar. "Meta features-based scale invariant OCR decision making using LSTM-RNN." Computational and Mathematical Organization Theory 25.2 (2019): 165-183. Impact Factor 1.558
  • Naseer, Asma, and Kashif Zafar. "Comparative analysis of raw images and meta feature based Urdu OCR using CNN and LSTM." Int J Adv Comput Sci Appl 9.1 (2018): 419-424.


Conference Publications


  • Dr. Farrukh Nadeem, Asma Naseer, Farrah Adeeba “Taxonomy of Data Management Models in Grid/Distributed Environments”, NU Graduate Colloquium on Computer Sciences  28th Dec. 2010.
  • Asma Naseer, Sarmad Hussain, “Tense and Aspect in the Context of Urdu”, 15th October 2010, IJECS-IJENS
  • Asma Naseer, "Supervised Word Sense Disambiguation for Urdu Using Bayesian",  In the Proceedings of Conference on Language & Technology (CLT10), Pakistan, October. (URL:
  • Akram, Q., Naseer, A., Hussain, S. Assas-band, an Affix-Exception-List Based Urdu StemmerIn the Proceedings of the 7th Workshop on Asian Language Resources, IJCNLP’09, Suntec City, Singapore, 2009 (URL:
  • Ali, Z. Asma, A. Sana, Z. Habib, "On a special case of path planning", Proceedings of the 7th CIIT workshop on research in computing, CWRC Spring 2008, Lahore, Pakistan, June 23, 2008, ISBN: 978-969-8779-18-4, pp. 97-100.