Ayesha Ali

Associate Professor, Statistics
Phone number: 
519-824-4120 x53896
MacNaughton 509

Available positions for grads/undergrads/postdoctoral fellows: Inquire by email

An overarching theme to my research is studying complex systems in high dimensional settings, but the specific projects are quite diverse. I approach all of these problems by first considering the associated data generating mechanism and relating it to a graphical Markov model. My research typically involves strong statistical computing skills. Specific applications include:

  • Modelling plant-pollinator networks. We have developed a grouped Dirichlet-multinomial regression model with regularization for model selection, and adapted latent Dirichlet allocation to the plant-pollinator network setting.  Future work involves accommodating sampling weights, zero-inflation, and the development of statistical to compare networks in space and/or time. 
  • Risk factors for disease in a longitudinal setting.  Causal models can be used to provide unbiased parameter estimates in the presence of time-dependent confounding.  I have applied marginal structural models to natural history data and developed a Cox score bootstrap for fast, reliable bootstrapping to be used in large scale simulation studies.  I am interested in studying time-dependent weighting within the context of cure rate models and gee models for correlated outcomes.
  • Model selection/Structure learning/Machine learning. I am interested in several applied problems for the genetic selection of livestock.  We have developed models that exploit the graphical structure of predictors for the prediction of an outcome, whether continuous or binary, using regularization.  A specific application involves the selection of candidate genes in the prediction of boar taint.  We have also analyzed milk spectral data for the purpose of improving the nutraceutical properties of milk and overall health of cows. This project also involves developing methods to link the spectral data with genetic, physical and performance traits in a longitudinal setting.

I am also involved with the Bioinformatics program and can supervise students on research projects.

Media Coverage:



  • Graphical Markov models
  • Pollination networks
  • Multivariate statistics
  • Causal inference
  • Survival analysis
  • Bioinformatics
  • High performance computing
  • Artificial intelligence
  • Ecological modelling
  • Machine learning

Graduate Theses

  • Bootstrapped Confidence Intervals for Cure Rates in  First Hitting Time Regression Models.
    Radia Taisir, Master of Science (Expected: 2019).
  • Doubly Sparse Regression Incorporating Graphical Structure.
    Matthew Stephenson, PhD (Expected: 2018).
  • Conditional Replicated Softmax for Topic Modelling with Metadata.
    Charles Austria, Master of Science (Expected: 2018).
  • On  Optimization  and  Regularization  for  Grouped Dirichlet-multinomial  Regression.
    Catherine Crea, PhD (2018).
  • Pooling  Methods  and  Similarity  Measures  for  Fractional  Factorial Designs  in  the  Presence  of  an  Auxiliary  Variable.
    Andrew Porter, Master of Science (2017).
  • Comparison  of  Topic  Models  for  Analyzing  Pollination  Networks  with Competition.
    Jiayong (Wing) Li, Master of Science (2016).
  • On the Estimation of Network Metrics in the Presence of Sampling Effects.
    Steven Lin, Master of Science (2015).
  • DEEPR: A Dirichlet-multinomial randomization test for estimating relative coevolutionary event differences between groups of symbiotic species.
    Mark Merilo, Master of Science in Bioinformatics (2014).

Undergraduate Projects

  • Modeling  Species Using  Lotka–Volterra Equations  & Constructing  Foodweb  Networks.
    Abul Sheikh (2016).
  • A Replicated Softmax model with Additional Inputs.
    Kim Krieger (2015).
  • Pollination  networks  and  pollinator behaviour  types using latent Dirichlet allocation.
    Sarah Priamo (2015).

B.Sc. Honours in Statistics and Actuarial Science (University of Western Ontario), 1996.

M.Sc. in Statistics (University of Toronto), 1998.

Ph.D. in Statistics (University of Washington), 2002.

A Fleming, FS Schenkel, F Malchiodi, RA Ali, B Mallard, M Sargolzaei, J Jamrozik, J Johnston, and F Miglior (2018). Genetic correlations of mid-infrared-predicted milk fatty acid groups with milk production traits, Journal of Dairy Science, 101(5):4295-4306.

NDJ Strzalkowski, RA Ali, and LR Bent (2017). The firing characteristics of foot sole cutaneous mechanoreceptor afferents in response to vibration stimuli, Journal of Neurophysiology, 118(4):1931-1942. 

A Fleming, FS Schenkel, J Chen, F Malchiodi, V Bonafatti, RA Ali, B Mallard, M Corredig, and F Miglior (2017). Prediction of milk fatty acid content with mid-infrared spectroscopy in Canadian dairy cattle using differently distributed model development sets, Journal of Dairy Science, 100(6 ):5073-5081.

C Crea, RA  Ali, and R Rader (2016).  A new model for ecological networks using species-level traits. Methods in Ecology and Evolution, 7:232-24.

M Stephenson, RA Ali, and G Darlington (2015).  A Simple Approach to Analyzing Clustered Data. Communications in Statistics - Simulation and Computation, 46(5):3553-3562.

RA Ali, MA Ali, and Z Wei (2014). On computing standard errors for marginal structural Cox models. Lifetime Data Analysis, 20(1), 106-131.  


  • The Canadian Journal of Statistics Award, 2020 for “Doubly sparse regression incorporating graphical structure among predictors.”
  • NSERC Discovery Grant, 2018
  • NSERC Collaborative Research and Development Grant, 2015
  • Senior Program Committee member, Uncertainty in Artificial Intelligence Conference, 2015-2017
  • Publications Officer and Newsletter Editor, The International Environmetrics Society, 2011-2017