Dr. Taj Muhammad Khan

Dr. Taj
+92 938 281026

Dr. Taj Muhammad Khan

Assistant Professor

Qualifications: Ph.D Computer Science
Research Interests: Computer Architecture, Operating Systems


August, 2007               T. Saidani and L. Lacassagne and S. Bouaziz and T. M. Khan, Parallelization Strategies for the Points of Interests Algorithm on the Cell Processor. ISPA ’07: Proceedings of the 5th International Symposium on Parallel and Distributed Processing and Applications. Niagara Falls, Canada.

July, 2010                   Taj Khan, Daniel Gracia-Pérez, Olivier Temam, Transparent Sampling. International Conference on Embedded Computer Systems: Architectures, Modeling, and Simulation (IC-SAMOS), Samos, Greece.

February, 2011            Taj Muhammad Khan, Daniel Gracia-Pérez and Olivier Temam. Combining On-Line Sampling and Adaptive Warm-Up for a More Practical Sampling Strategy. Rapid Simulation and Performance Evaluation: Methods and Tools (RAPIDO’ 11), Heraklion, Greece.

November, 2021         Taj Muhammad Khan and Syed Waqar Nabi. English versus Native Language for Higher Education in Computer Science: A Pilot Study. In 21st Koli Calling International Conference on Computing Education Research (Koli Calling ’21).


2007 – 2011   Ph.D. In Computer Science        INRIA Saclay Ile-de-France / University of Paris XI, France.  Acceleration of processor simulation techniques.

2007 –  2008  Cycle DFE (Découvert de la France Entrepreneuriale)   ISEFRE, France.  Diploma in entrepreneurship.

2005 –  2007  Master Research in Computer Science. Université de Paris-Sud 11, France.   Specialization: «Information systems and infrastructures».

1998 – 2002   Bachelors in Computer System Engineering. GIK Institute of Engineering Sciences and Technology, Pakistan.



Co-PI in Hetro-Cloud an NCBC funded  (14 million PKR) project.

Co-PI in establishing the HBL Blockchain center at GIK institute. (6.3 million PKR).



  • Implementation of a fault-tolerant processor on an FPGA.
  • An autonomous garbage collecting robot on static water bodies.
  • SwiftBot: An autonomous delivery robot for indoor scenarios.
  • Predicting parts of speech of Pashto language using machine learning.
  • Implementing a machine learning model on FPGA




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