Emily Aiken

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

Headshot

I am a postdoctoral scholar at Carnegie Mellon University Africa. Starting in January 2026, I will be 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 machine learning and development economics, with a focus on analyzing large digital traces to inform social protection policy. 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.

★ I am accepting PhD students at UCSD for fall 2025. See below for information on how to apply.

News (See all news)

11/2024
New paper published in Scientific Reports on differentially private mobility data for humanitarian response.
11/2024
Upcoming talks @ CMU Africa: Panel on AI in Africa (Nov. 14 at 4:30pm, virtual), panel on AI ethics at DevFest Kigali (Nov 16 at 3:30pm), and public research talk for CMU students (Nov. 25 at 12:30pm).
09/2024
New paper, led by Zoe Kahn, on perspectives on privacy and mobile phone data in rural Togo accepted at CSCW (2025).
09/2024
I will give a talk at the workshop on digital technologies and sustainable development at Oxford on Oct. 2.
07/2024
I have accepted a postdoc fellowship at CMU Africa (2024-2025) and an assistant professorship at UC San Diego (starting January 2026, jointly appointed in data science and public policy)!

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

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).
          
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).
Accepted for presentation at: Georgetown/World Bank Conference on New Directions in the Fight Against Poverty Honoring Martin Ravallion (2023).
     

 

Peer Reviewed Journal Articles and Conference Proceedings

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).
     

*Equal Contribution

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