Emily Aiken

CV | Google Scholar | GitHub | emilyaiken@berkeley.edu


I am a fourth year PhD student at the UC Berkeley School of Information advised by Joshua Blumenstock. My research interests are in machine learning, global health, and development economics, with a focus on analyzing large digital traces to inform evidence-based policy. Before Berkeley, I received my BA in computer science from Harvard University where I conducted research on digital epidemiology with Mauricio Santillana. I am funded by a Microsoft Research PhD Fellowship and am affiliated with the Global Policy Lab and Berkeley AI Research (BAIR).

Recent News (See all news)

Our paper on phone-based hybrid approaches to ultra-poverty targeting in Afghanstian was published in the Journal of Development Economics.
Our short paper on adaptive sampling for poverty prediction was accepted to the NeurIPS workshop on AI for humanitarian assistance and disaster response (AI+HADR '22).
I will present our work on impact evaluation from digital data at CODE@MIT and the UPenn Big Data for Development and Governance Conference.
I will present our work on fairness in satellite-based poverty mapping at the KDD Workshop on Humanitarian Mapping.
Our paper on phone sharing won the best paper award at COMPASS '22!

Publications and Working Papers

Journal Articles and Conference Proceedings

Machine learning and phone data can improve targeting of humanitarian aid.
E. Aiken, S. Bellue, D. Karlan, C. Udry, and J. Blumenstock.
Nature 603, No. 7903 (2022).
★ Cover article
Early versions: NBER Working Paper No. 29070 (2022).
Phone sharing and cash transfers in Togo: Quantitative evidence from mobile phone data.
E. Aiken*, V. Thakur*, and J. Blumenstock.
★ Best paper award
Targeting development aid with machine learning and mobile phone data:
Evidence from an anti-poverty intervention in Afghanistan.
E. Aiken, G. Bedoya, A. Coville, and J. Blumenstock.
Journal of Development Economics 161 (2022).
Early verisons: World Bank Policy Research Working Paper No. 10252 (2022). Extended abstract at ACM COMPASS (2020).
Towards the use of neural networks for influenza prediction at multiple spatial resolutions.
E. Aiken, A. Nguyen, C. Viboud, and M. Santillana.
Science Advances 7, No. 8 (2021).
Early versions: Extended abstract at NeurIPS ML for Health (2019).
Real-time estimation of disease activity in emerging outbreaks using internet search information.
E. Aiken, S. McGough, M. Majumder, G. Wachtel, A. Nguyen, C. Viboud, and M. Santillana.
PLoS Computational Biology 16, No. 8 (2020).


Workshop and Short Papers

Can strategic data collection improve the performance of poverty prediction models?
S. Soman, E. Aiken, E. Rolf, and J. Blumenstock.
NeurIPS Workshop on AI for Humanitarian Assistance and Disaster Response (2022).
Satellite-based poverty mapping: Accuracy, fairness, and policy implications.
E. Aiken, J. Blumenstock, and E. Rolf.
KDD Humanitarian Mapping Workshop (2022).
Home location detection from mobile phone data: Evidence from Togo.
R. Warren, E. Aiken, and J. Blumenstock.
ACM COMPASS, Poster Track (2022).
Privacy gaurantees for personal mobility data in humanitarian response.
N. Kohli, E. Aiken, and J. Blumenstock.
KDD Humanitarian Mapping Workshop (2020).

*Equal Contribution