Lujain Ibrahim
Visiting Student Researcher at Google DeepMind
Beijing, United Kingdom
Summary
👤
Senior🎓
Top SchoolLujain Ibrahim is a researcher-engineer bridging social data science and AI safety, currently pursuing a PhD at Oxford’s Internet Institute while holding visiting research roles at Stanford and DeepMind. With eight years of experience spanning academia, industry, and policy — including positions at OpenAI, NYU Abu Dhabi, and the Centre for the Governance of AI — she blends rigorous empirical methods with practical trust-and-safety expertise. Her background in computer engineering and global affairs (Schwarzman Scholars, Tsinghua) enables her to tackle technical problems with policy-savvy perspectives. She has taught in Oxford-affiliated programs and supported interdisciplinary projects across Asia, Europe, and the US, reflecting a global research network. Known for moving between applied research and operational roles, she is as comfortable analyzing social datasets as she is advising on AI governance. Contact via email or her Calendly link for collaborations.
8 years of coding experience
3 years of employment as a software developer
Bachelor's degree Computer Engineering, Bachelor's degree Computer Engineering at New York University Abu DhabiDoctor of Philosophy - PhD Social Data Science, Doctor of Philosophy - PhD Social Data Science at University of OxfordMaster's degree Global Affairs concentrations in Technology and Policy, Master's degree Global Affairs concentrations in Technology and Policy at Schwarzman ScholarsMaster of Arts - MA Global affairs, Master of Arts - MA Global affairs at Tsinghua University
Github Skills (5)
prediction8
ieee7
machine-learning4
deep-learning3
shapley2
Programming languages (3)
Github contributions (5)
lujainibrahim/iGEM2018
Jun 2018 - Aug 2023
Contributions:41 pushes, 2 branches in 5 years 3 months
lujainibrahim/ecg-view-II-machine-learning
May 2020 - Feb 2021
This repository is the official implementation of Explainable Prediction of Acute Myocardial Infarction using Machine Learning and Shapley Values published in IEEE Access in November 2020.
Contributions:32 commits, 5 pushes in 8 months
novembershapley-valuespredictiondeep-learningmachine-learning
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