Emily Aiken

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


I am a fifth year PhD student at the UC Berkeley School of Information advised by Joshua Blumenstock. My research interests are in machine learning and development economics, with a focus on analyzing large digital traces to inform social protection 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).

★ I am on the academic job market (for tenure track positions and postdocs) in 2023-2024!

Recent News (See all news)

I will give a talk on data-driven and community-based poverty targeting in Bangladesh at PacDev '24.
I will give a research talk and be on a panel at the NeurIPS workshop on computational sustainability.
I was selected as a rising star in data science by the University of Chicago.
I will give a talk at NEUDC '23 on updating proxy-means tests.
Two posters at EAAMO '23 (updating proxy-means tests and fairness in satellite-based poverty prediction). I am also co-organizing a tutorial on AI for social impact.

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.
IJCAI (2023).
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).


Preprints and Working Papers

Estimating impacts with surveys vs. digital traces: Evidence from randomized cash transfers in Togo.
E. Aiken, S. Bellue, D. Karlan, C. Udry, and J. Blumenstock.
NBER Working Paper No. 31751 (2023).
Moving targets: The role of model and data recency in proxy means test accuracy.
E. Aiken*, T. Ohlenburg*, and J. Blumenstock.
Working Paper (2023).
Accepted for presentation at: NEUDC (2023), COMPASS (2023) and the NeurIPS Workshop on Computational Sustainability (spotlight talk, 2023).
Privacy gaurantees for personal mobility data in humanitarian response.
N. Kohli*, E. Aiken*, and J. Blumenstock.
arXiv Preprint (2023).
Accepted for presentation at: KDD Workshop on Humanitarian Mapping (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.
ICLR Workshop on Practical ML for Development (2023).
Home location detection from mobile phone data: Evidence from Togo.
R. Warren, E. Aiken, and J. Blumenstock.
ACM COMPASS, Poster Track (2022).

*Equal Contribution