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Dr. Firdos Khan
Assistant Professor
Department of Mathematics

SNS
National University of Sciences and Technology (NUST)
School of Natural Sciences,Sector H-12, Islamabad, Pakistan
Tel : +92 51 9085 5602
Email : firdos.khan (at mark) sns.nust.edu.pk

Specialization
Applied Statistics

Education
Doctor of Science, University of Klagenfurt, Klagenfurt, Austria, Master of Philosophy, Quaid-i-Azam University, Islamabad, Pakistan, Master of Science, University of Peshawar, Peshawar, Pakistan , Bachelor of Science, University of Peshawar, Peshawar.

Dr. Firdos Khan joined the institute of Statistics, University of Klagenfurt as a doctoral student in 2013 and earned a doctoral degree in 2017. Before, his doctoral degree, he worked as scientific officer in Global Change Impact Studies Centre (GCISC) under the Ministry of Climate Change in Islamabad. He remained research fellow in the International Institute for Applied Systems Analysis (IIASA) in Vienna, Austria in 2012. He joined the School of Natural Sciences as Assistant Professor in January 2019.

Dr. Firdos has numerous research papers to his credit in the area of statistical modeling, climate change risk vulnerability assessment, water availability, drought risk assessment and climate extremes analysis.​​

Recent research interests of Dr. Firdos include Statistical downscaling, statistical modeling, climate vulnerability risk assessment, optimization of reservoir operation, water availability assessment, droughts risk assessment and model selection.

List of Publications:

  • Khan, F., Spöck, G., Pilz, J. (2019) A novel approach for modelling pattern and spatial dependence structures between climate variables by combining mixture models with copula models. International Journal of Climatology. DOI: 10.1002/joc.6255
  • Hamdullah, Akbar M., Khan F. (2019) Construction of homogeneous climatic regions by combining cluster analysis and L-moment approach on the basis of Reconnaissance Drought Index for Pakistan. International Journal of Climatology. DOI: 10.1002/joc.6214
  • Ali S, Eum H, Jaepil C, Li D, Khan F, Dairaku K, Shrestha M L, Hwang S, Naseem W, Khan I A, Fahad S (2019). Assessment of climate extremes in future projections downscaled by multiplestatistical downscaling methods over Pakistan . Online first on 22 February 2019 .
  • Khan F., Pilz J., Ali S. (2018) Evaluation of Statistical Downscaling Models by using Pattern and Dependent Structure in the Monsoon Dominated Region of Pakistan. Weather Vol. 73, No. 6. DOI: 10.1002/wea.3164.
  • Khan F., Pilz J. (2018) Modelling and Sensitvity analysis of River Flow in the Upper Indus Basin, Pakistan. Int. J. Water, Vol. 12, No. 1, 2018.
  • Zhao B., Han L., Pilz, J. Wu j., Khan F., Zhang D., 2017. Metallogenic efficiency from deposit to region–A case study in western Zhejiang Province, southeastern China. Ore Geology Reviews, 86, 957-970, DOI: 10.1016/j.oregeorev.2016.10.003.
  • Khan, F., Pilz, J., Ali S. (2017) Improved hydrological projections and reservoir management in the Upper Indus Basin under the changing climate. Water and environmrnt journal, 31 (2), 235-244. DOI: 10.1111/wej.12237.
  • Khan, F., Pilz, J., Amjad. M., Wiberg, W. (2015) Climate variability and its impacts on water resources under IPCC climate change scenarios in the Upper Indus Basin, Pakistan. Int. J. of Global warming, 8(1), 46-69. DOI: 10.1504/ijgw.2015.071583.
  • Ali, S., Li, D., Congbin, F., Khan, F., (2015) 21st Century climatic and hydrological changes over Upper Indus Basin of Himalayan region of Pakistan. Environmental Research Letters, 10 (1), 014007. DOI: 10.1088/1748-9326/10/1/014007.
  • Amjad, M., Zafar. Q., Khan, F., Munir, M., 2015. Evaluation of Weather Research and Forecasting Model for the Assessment of Wind Resource over Gharo, Pakistan. International Journal of Climatology, 35 (8) 1821–1832. DOI:10.1002/joc.4089.​

Chapter Published:

  • Khan F., Pilz J. Statistical Methodology for Evaluating Process-Based Climate Models. Published in Book " Climate Change and Global Warming ". IntechOpen (2018).

Taught following courses at undergraduate and postgraduate level:


  • Statistical Inference
  • Bayesian Statistics
  • Mathematical Statistics-II
  • Stochastic Processes
  • Probability and Statistics​