Contact information

Cell: 0336-0457067
Phone: 042-99211449-50
Ext. 306
Dr. Fareed Ahmad

I.T Manager

MS Computer Science (UET), Ph.D. Computer Science (UET)


Dr. Fareed Ahmad is an I.T Manager at Quality Operations Laboratory, Institute of Microbiology, University of Veterinary and Animal Sciences, Lahore. He holds M.S and a Ph.D. degree in Computer Science and has completed his Ph.D. degree from the University of Engineering and Technology, Lahore. The title of his research thesis was "Deep Learning Techniques for Pathogen Classification". Currently, he has published four papers in high-impact factor journals and one conference paper.  Dr. Fareed’s fundamental expertise is in the field of Machine learning for the classification of soil-borne pathogens along with the identification of the most relevant features that help in their prevalence in the environment. He has also applied Computer Vision and Deep Learning techniques for distinguishing COVID-19 from other chest-related infections in X-ray images. He is also acting as a Quality Manager for ISO/IEC 17025:2017 accreditation-related activities at Quality Operations Laboratory, and has successfully completed the ISO 17025 Assessor training course organized by Pakistan National Accreditation Council.

He has acted as a reviewer for Elsevier and Springer journals, which are as follows:

  1. Computers in Biology and Medicine
  2. Artificial Intelligence Reviews

He has also been involved in teaching activities at UVAS, Lahore, and during his Ph.D. studies at UET, Lahore.

His Ph.D. research work stood 3rd in the ICOSST-2018 poster competition arranged by Al-Kawarzimi Institute of Computer Sciences, UET, Lahore.

Area of Interest

  • Machine learning techniques like Feature ranking and classification
  • Deep learning techniques like Merging Deep learning models, Extracting features from Pre-trained models, Devising new deep learning models
  • Image processing Techniques like Data augmentation, Image resizing, image patching etc.
  • Machine learning for Classification and identification of most relevant features for Soil-borne pathogens
  • Deep learning for Colony Classification bacterial pathogens using gram-stained images