I work on machine learning and digital data, applied in developing economies.

Machine Learning
with Samsun Knight and Joshua Blumenstock
Revision requested (second round), Review of Economic Studies. Presented at ACM EAAMO (2021)
with Joshua Blumenstock and Samsun Knight
Reject and resubmit, American Economic Review. Video (30m) | How do you say algorithm in Kiswahili?
with B. Douglas Bernheim, Jeffrey Naecker, and Michael Pollmann
with Jun Ho Choi, Oliver Garrod, Paul Atherton, Andrew Joyce-Gibbons, and Miriam Mason-Sesay
NeurIPS Workshop on Generative AI for Education (2023)
with Esther Rolf, Max Simchowitz, Sarah Dean, Lydia Liu, Moritz Hardt, and Joshua Blumenstock
International Conference on Machine Learning (ICML) (2020)
Workshop paper presented at NeurIPS Joint Workshop on AI for Social Good (2019). Best Paper Award

with Joshua Blumenstock
IMF Finance & Development (2023) Issue on AI
Network Industries
with Burak Ceyhun Karaca
Journal of Development Economics (2022)

with Chiara Farronato
Harvard Business Review (2021), website
Digital Credit
with Darrell Grissen
World Bank Economic Review (2020) Original Proposal (posted 2010)
Media (2015): NPR Morning Edition, The World Bank (syndicated to World Economic Forum), New Scientist, Radio New Zealand
with Darrell Grissen
American Economic Association Papers and Proceedings (2018)
with Joshua Blumenstock, Omowunmi Folajimi-Senjobi, Jacqueline Mauro, and Suraj Nair
Revise and resubmit, Economic Development and Cultural Change. Pre-Analysis Plan
Big Data for Development
with Sveta Milusheva and Leonardo Viotti
ACM Conference on Computing and Sustainable Societies (COMPASS) (2021) Blog Post
International Conference on Information and Communication Technologies and Development (ICTD) (2020)
In Progress
Public and Private Transit: Evidence from Lagos
with Alice Duhaut, Geetika Nagpal, and Nick Tsivanidis
Efficiency of Informal Transit Networks
with Alice Duhaut, Geetika Nagpal, and Nick Tsivanidis
Welfare-Sensitive Machine Learning
with Joshua Blumenstock
Other Papers