Projects - Page 2 of 2
Building a Neural Network to Address Canada’s Housing Crisis
Increasingly unaffordable housing prices have become a major challenge for several developed countries, including Canada. I am leading a project to model the complex factors that contribute to these price changes using longitudinal data, enabling policy makers to understand how policy changes can impact housing prices.
Enhancing Crop Yield Predictions in Rural Bihar with XYieldBoost
In collaboration with Digital Green, I led a team to build a machine learning solution that predicts the crop yield per acre of rice or wheat crops in Bihar. The goal is to empower smallholder farmers and break the cycle of poverty and malnutrition in northern India.
Generating Machine Learning-Based Streamflow Forecasts for the Western U.S.
In collaboration with DrivenData and the United States Bureau of Reclamation (USBR), I led a team to develop a spatially-aware, explainable machine learning forecast seasonal streamflow in the Western United States.
Increasing the Efficacy of the G7 and G20 Using Predictive AI
Data collected by the G7 and G20 Research Groups has made it possible to produce data-driven estimates of the probability that each member nation will meet their summit commitments. I developed this predictive model to enable Sherpas to better allocate resources towards at-risk commitments, thus making the G7 and G20 more effective.
Forecasting the Post-Pandemic Transit Recovery to Inform Relief Funding
As cities recover from the COVID-19 pandemic, there is a pressing need for fare-dependent transit agencies to have access to a reliable forecast of post-pandemic ridership recovery. I developed a machine learning-based forecast of this important metric for Infrastructure Canada.