Ayesha studies complex systems in high dimensional settings, with specific projects in diverse application domains funded by NSERC, CFREF, CANSSI and MITACS. She first considers the associated data generating process and then relates it to a graphical Markov model to solve problems inspired by real world applications. Research contributions include developing optimization methods for regularization of multivariate count data, XAI visualization techniques for deep models in the presence of small sample sizes, and proximal methods for regularized regressions that exploit predictor graph structure. Specific application domains include gut-microbiome compositions, nitrogen testing timing for corn, crop yield estimation using remotely sensed data, and biomonitoring of chemical exposures and health, to name a few. Ayesha completed her PhD in Statistics at the University of Washington. She briefly held a position at the National University of Singapore before returning to Canada.