Emily Aiken

CV | Google Scholar | GitHub | eaiken@ucsd.edu

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I am an assistant professor at UC San Diego, jointly appointed in the School of Global Policy and Strategy and the Halıcıoğlu Data Science Institute. My research interests are in data science and development economics, with a focus on analyzing large digital traces to inform the design and targeting of social protection and humanitarian aid programs. I was previously a postdoctoral scholar at Carnegie Mellon University Africa (2024-2025). I received my PhD from the UC Berkeley School of Information, where I was advised by Joshua Blumenstock. I also hold an MS (UC Berkeley) and BA (Harvard) in computer science.

News (See all news)

11/2025
I will give talks at the Stellenbosch workshop on economic history in the age of AI and the EAAMO 2025 Cape Town satellite event.
10/2025
Recent VoxDev articles covered our work on remote impact evaluation in Togo and targeting aid at scale in Bangladesh.
09/2025
Awarded an Afretec seed grant for research on privacy and mobile money use in Sub-Saharan Africa.
06/2025
New working paper on targeting aid at scale, comparing the cost-efficiency of algorithmic, survey-based, and community-based targeting methods in Bangladesh.
03/2025
Our paper on remote impact evaluation with mobile phone data was published in JDE.

Work with me

I am looking for exceptional students and research assistants who are motivated to work on problems at the intersection of data science and development. I currently prioritize students based at CMU Africa or UC San Diego with substantial expertise in programming, statistics, development economics, and/or human-computer interaction. Please get in touch if you are interested in working with me, following the instructions below. If you are a...

Papers

Preprints and Working Papers

Scalable targeting of social protection: When do algorithms out-perform surveys and community knowledge?
E. Aiken, A. Ashraf, J. Blumenstock, R. Guiteras, A. Mobarak
NBER Working Paper No. 33919 (2025).
          
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).
          

 

Peer Reviewed Journal Articles and Conference Proceedings

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.
Journal of Development Economics 175 (2025).
Early versions: NBER Working Paper No. 31751 (2023).
     
Expanding perspectives on data privacy: Insights from rural Togo.
Z. Kahn, C. PERE, E. Aiken, N. Kohli, and J. Blumenstock.
CSCW (accepted, 2025).
          
Privacy gaurantees for personal mobility data in humanitarian response.
N. Kohli*, E. Aiken*, and J. Blumenstock.
Scientific Reports 14, No. 28505 (2024).
          
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).
Early versions: ICLR Workshop on Machine Learning for Remote Sensing (2023) and KDD Workshop on Humanitarian Mapping (2022).
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).
          
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.
ACM COMPASS (2022).
★ Best paper award
Home location detection from mobile phone data: Evidence from Togo.
R. Warren, E. Aiken, and J. Blumenstock.
ACM COMPASS (2022).
          
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).
     

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