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)

I will be at ICLR '23. Find our work presented at the workshop on Practical ML for Development (10:15am on Friday) and the workshop on ML for Remote Sensing (3pm on Friday).
Our paper on fairness in satellite-based poverty mapping was accepted at IJCAI '23!
I coauthored a policy review paper for GIZ on digital data in social protection. We will present the report in a public webinar on April 25.
Two workshop papers accepted at ICLR 2023: An oral presentation on our work on fairness in satellite-based poverty prediction at ML for Remote Sensing and an oral presentation on active learning at Practical ML for Development.
Our paper on phone-based hybrid approaches to ultra-poverty targeting in Afghanstian was published in the Journal of Development Economics.

Publications and Working Papers

Journal Articles and Conference Proceedings

Fairness and representation in satellite-based poverty maps:
Evidence of urban-rural disparities and their impacts on downstream policy.
E. Aiken*, E. Rolf*, and J. Blumenstock.
Accepted, IJCAI (2023).
Early versions: ICLR Workshop on Machine Learning for Remote Sensing (2023) and KDD Workshop on Humanitarian Mapping (2022).
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).
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