Research Areas: Machine Learning, Data Science, Natural Language Processing, Computational Social Science, Forecasting, Information Systems, Public Policy, Law, Regulation and Sustainability.
I have conducted research as part of the data science components of multiple National Science Foundation-funded projects and serve as a Team Lead and Co-PI on a grant to combine machine learning and econometric approaches to estimate causal effects of drought across the U.S. and inform environmental law and policy-making.
I develop machine learning approaches to better understand and forecast. My computational work is directed toward law, policy and business applications, including the following recent publications:
- natural language processing of law and policy
- machine learning for predicting and understanding law-making
- computer simulations of climate prediction markets
- machine learning for forecasting drought globally with satellite data
- computational models predicting human cooperation
- machine learning for automatically estimating models of decision-making
Most of my current research focuses on: unsupervised learning methods for discovering meaningful structure in text and associated methods for automated validation and user-interface development; supervised learning methods for predicting outcomes related to text; engaging the public in the co-creation and interpretation of policy; combining causal inference and machine learning to estimate policy effects with big data; and the ethics of computational policy.
Email: john dot j dot nay at gmail dot com