Principal Data Scientist
- Ph.D. Carleton University
- M.S. IIT Madras
Ajit Desai is currently working as a principal data scientist in the research division of the Bank of Canada's Banking and Payments Department. His current work leverages cutting edge techniques such as artificial intelligence, machine learning, and quantum computing to study payments data (including cryptocurrency data) with the primary objective of making Canada’s digital payments infrastructure safe and efficient. Dr. Desai received his Ph.D. from Carleton University in 2018 in Computational Science and Engineering and M.S. from Indian Institute of Technology Madras in 2011 in Aerospace Engineering.
Staff working papers
Macroeconomic Predictions Using Payments Data and Machine LearningWe demonstrate the usefulness of payment systems data and machine learning models for macroeconomic predictions and provide a set of econometric tools to overcome associated challenges.
Estimating Policy Functions in Payments Systems Using Reinforcement LearningWe demonstrate the ability of reinforcement learning techniques to estimate the best-response functions of banks participating in high-value payments systems—a real-world strategic game of incomplete information.
Using Payments Data to Nowcast Macroeconomic Variables During the Onset of COVID-19We use retail payment data in conjunction with machine learning techniques to predict the effects of COVID-19 on the Canadian economy in near-real time. Our model yields a significant increase in macroeconomic prediction accuracy over a linear benchmark model.
- Chapman, J. T., & Desai, A. (2020). Using payments data to nowcast macroeconomic variables during the onset of COVID-19. Journal of Financial Market Infrastructures, 9(1).
- Desai, A., Khalil, M., Pettit, C. L., Poirel, D., & Sarkar, A. (2020). Domain Decomposition of Stochastic PDEs: Development of Probabilistic Wirebasket-based Two-level Preconditioners. Journal of Computational Physics (review).
- Desai, A., Khalil, M., Pettit, C., Poirel, D., & Sarkar, A. (2018). Scalable domain decomposition solvers for stochastic PDEs in high performance computing. Computer Methods in Applied Mechanics and Engineering, 335, 194-222.
- Desai, A., Witteveen, J. A., & Sarkar, S. (2013). Uncertainty quantification of a nonlinear aeroelastic system using polynomial chaos expansion with constant phase interpolation. Journal of Vibration and Acoustics, 135(5).
- Desai, A., & Sarkar, S. (2010). Analysis of a nonlinear aeroelastic system with parametric uncertainties using polynomial chaos expansion. Mathematical Problems in Engineering.