Khurram Nadeem

Khurram Nadeem
Associate Professor, Statistics
Phone number: 
519-824-4120 x53136
MacNaughton 517

RESEARCH  INTERESTS: Predictive modeling of ecological and environmental processes via big data analytics; Wildland fire occurrence prediction; modeling of spatio-temporal processes; statistical bioinformatics.

Seeking academic or industry partnerships in the area(s) of:  Forest Fire Analytics, Agriculture and Environmetrics, Quantitative Ecology 

Available positions for grads/undergrads/postdoctoral: 

  • Research projects for both MSc and PhD programs are currently available in my lab. Prospective graduate students with an academic background focusing on statistics, biostatistics or a closely related field and having strong programming skills (e.g RStudio) are welcome to apply.
  • International doctoral students with a minimum 80% (A-) admission average are strongly encouraged to apply and avail an opportunity to apply for the International Doctoral Student Scholarship (see details here).
  • I also co-supervise graduate students in the Bioinformatics program at the University of Guelph.
  • Postdoc Positions

Research Themes:

My research focuses on leveraging innovative techniques and methods in computing and statistical machine learning to extract meaningful and actionable insights from massive volumes of high-dimensional data. His work focuses on predictive modeling of ecological and environmental processes via big data analytics. Key research themes include:

  1. Wildland fire occurrence prediction. My research harnesses the availability of large scale historical environmental, wildland fire and demographic data in Canada to spatially predict severe and large forest fires for two weeks ahead into the future. Apart from contributing to improvements to the Canadian Interagency Forest Fire Centre’s (CIFFC) management, coordination and information services, this research has implications for sustainable forest management in the face of changing climate and increasing human anthropogenic activity. The outcomes of this ongoing work will have significant impact on Canada’s ability to efficiently respond to the danger of severe wildland fires and to understand fire-weather dynamics in the wake of changing Earth climate.
  2. Agro-environmental science. Within agro-environmental science, data across multiple sources and scales are increasingly becoming available, encompassing the full spectrum of four V’s of big data: volume, variety, velocity, and veracity. Sources of these datasets range from agronomic data through precision agriculture such as monitoring site-specific soil characteristics and harvest yield on geo-spatial scales; to functional crop genomics data, to agroeconomics data. Data availability in such volume and variety presents an opportunity to integrate and analyze these data streams for evidence-based decision making in implementing sustainable agricultural practices. I am interested in: i) leveraging the availability of varied data sources to answer important large-scale comprehensive research questions related to agricultural productivity and its interaction with the environment, ii) developing a digital framework to identify, collect and integrate relevant data from various sources to develop databases and decision-support tools, and iii) employing advanced statistical and computational techniques, including machine learning and artificial intelligence methods, to develop predictive models for answering the key research questions, and to deploy them in a decision-support system. Key agricultural research problems to explore are: i) how climate change will impact food productivity and security in Canada and to what extent northern areas will become suitable for agriculture, and ii) how biodiversity components and ecosystem services interact across a wide range of agricultural landscapes and farming practices.

Media Coverage:

Wildland Fire

Food from Thought



PhD, Statistics 2013 University of Alberta, Edmonton, AB, Canada. Advisor: Dr. Subhash R. Lele Dissertation Title: Estimability and Likelihood Inference for General Hierarchical Models using Data Cloning.

MSc, Statistics  2005 University of Karachi, Pakistan, Advisor: Dr. Javed Iqbal Dissertation Title: Exploring the causal relationship among social, real, monetary and infrastructure development in Pakistan.

BSc (Honours), Statistics 2003 University of Karachi, Pakistan. First Class Second Position.

Recent Publications


Nadeem, K. & Jabri, M. (2022). Stable variable ranking and selection in regularized logistic regression for severely imbalanced big binary data. PLOS ONE. (Under Review)


Colgan, P., Rieke, E., Nadeem, K., Moorman, T., Soupir, M., Howe, A. & Ricker, N. (2022). Impact of swine and beef cattle manure treatment on the resistome of soil and water effluent from artificially drained cropland. International Journal of Molecular Sciences. (Submitted)


Niazy, M., Hill, S., Nadeem, K., Ricker, N., & Farzan, A. (2022). Compositional analysis of the tonsil microbiota in relationship to Streptococcus suis disease in nursery pigs in Ontario. Animal microbiome4(1), 1-13.


Taylor, S. W., & Nadeem, K. (2022). Predicting daily initial attack aircraft targets in British Columbia. International Journal of Wildland Fire31(4), 449-468.


KC, K. B., Green, A. G., Wassmansdorf, D., Gandhi, V., Nadeem, K., & Fraser, E. D. (2021). Opportunities and trade-offs for expanding agriculture in Canada’s North: an ecosystem service perspective. FACETS6(1), 1728-1752.


Nadeem, K., Taylor, S. W., Woolford, D. G., & Dean, C. B. (2019). Mesoscale spatiotemporal predictive models of daily human-and lightning-caused wildland fire occurrence in British Columbia. International journal of wildland fire29(1), 11-27.


Nadeem, K., Chen, E., & Zhang, Y. (2018). A Novel Hierarchical Multinomial Approach to Modeling Age-Specific Harvest Data. In Quantitative Methods in Environmental and Climate Research (pp. 29-48). Springer, Cham.


Dean, C. B., Bull, S. B., Nadeem, K., & Wolters, M. A. (2017). Big Data in Biosciences. Wiley StatsRef: Statistics Reference Online, 1-9.
Nadeem, K., Moore, J. E., Zhang, Y., & Chipman, H. (2016) Integrating  population dynamics models and distance sampling data: a spatial hierarchical state
space approach. Ecology, 97, 1735–1745.


Zhang, Y., P. Cabilio, & Nadeem, K (2016) Improved seasonal Mann–Kendall tests for trend analysis in water resources time series. Advances in Time Series Methods and Applications. Springer New York, 215-229.

Recent Student Projects/Theses


Graduate Projects/Theses

  • Modeling impact of climate change on agriculture land suitability in Canada using novel machine learning methods

       Aman Bhullar, PhD (in progress).

  • Land Suitability Assessments for Maize Production in Ontario, Canada, using a Weighted Overlay Method and Random Forest Algorithm

      Vivek Gandhi, Master of Science (2022).

  • A novel automatic variable ranking and selection algorithm for severely imbalanced big binary data

      Mehdi-Abderrahman Jabri, Master of Science (2021).

  • Modelling human-caused wildfire occurrence data in British Columbia, Canada, using penalized zero-inflated regression models

      Jonathan Moore, Master of Science (2021).

  • A multi-omics model to identify host-microbiome interactions and pathogen dynamic impacts on streptococcus suis disease development in pigs

       Maysa Niazy, Master of Science in Bioinformatics (2021).

  • A Study of Methods for Spatial Interpolation of Fire Weather in the Canadian Prairies

      Yue Cheng, Master of Science (2020).


Undergraduate Projects

  • Evaluating predictive performance of wildland Fire occurrence models for severe and large fires in Canada

       Mehdi-Abderrahman Jabri (2019).

  • Identification of topographic and fire-weather factors affecting human-caused fire occurrence in British Columbia, Canada

      Jenelle Barker (2020)