Bio: Emily is a Ph.D. student in the School of Information at UC Berkeley. She is a recipient of a 2022-2024 Microsoft Research Ph.D. Fellowship and is pursuing a concurrent M.S. in the electrical engineering and computer science department. Prior to Berkeley, she graduated from Harvard University in 2019 with a B.A. in computer science.
Research: Emily's research interests are at the intersection of machine learning, global health, and development economics, with a focus on leveraging large-scale digital traces for evidence-based policymaking. Her doctoral work has focused on data-intensive methods for allocating humanitarian aid in crisis settings.
Fields of Interest: Machine learning, social protection policy, development economics, algorithmic fairness and transparency
Website: emilylaiken.github.io
Publications:
Machine learning and phone data can improve targeting of humanitarian aid
forthcoming, Nature, 2022
Phone sharing and cash transfers in Togo: Quantitative evidence from mobile phone data
Targeting development aid with machine learning and mobile phone data: Evidence from an anti-poverty intervention in Afghanistan
Towards the use of neural networks for influenza prediction at multiple spatial resolutions
Science Advances, 2021
Real time estimation of disease activity in emerging outbreaks using internet search information
