Khurram Nadeem

headshot of Khurram Nadeem
Associate Professor
Department of Mathematics and Statistics
Phone number: 
(519) 824-4120 ext. 53136
MACN 517
Seeking academic or industry partnerships in the area(s) of: 
Forest Fire Analytics, Agriculture and Environmetrics, Quantitative Ecology
Available positions for grads/undergrads/postdoctoral fellows: 
Currently looking for graduate students having interest in applied statistics with strong programming skills (RStudio, Python). Nadeem also co-supervises graduate students in the Bioinformatics program at the University of Guelph.

Education and Employment Background

Dr. Khurram Nadeem received his PhD from the University of Alberta in 2013. He went on to work as a postdoctoral research fellow at Acadia University, an Environmetrics Research Scientist for Natural Resources Canada, and a Postdoctoral Research Associate for the University of Western Ontario. He joined the University of Guelph as an Assistant Professor of Statistics in 2019. 

Research Themes

Nadeem’s 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. Nadeem 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. Nadeem is 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.


  • Nova Scotia Habitat Conservation Fund Award, 2014-2015
  • MITACS Accelerate Postdoctoral Fellowship Award, 2013-2015
  • CANSSI Postdoctoral Fellowship Award, 2014-2015

Media Coverage

Wildland Fire

Food from Thought