Summary
Mihaela Curmei is a research scientist and upcoming UC Berkeley PhD graduate (summer 2024) with a decade of experience at the intersection of machine learning, causal inference, control theory, and behavioral psychology. Her work probes how human behavior and institutional systems interact with algorithmic decision-making, producing applied research that informs private recommender models and long-term societal outcomes. She has industry research experience at Google Research (DP item embeddings for recommender systems) and joins Meta as a Research Scientist, building on prior applied ML and prototyping roles at Microsoft that spanned healthcare, humanitarian forecasting, and industrial RL. Comfortable moving between theory and production, she leverages public data and domain-informed priors to improve privacy-utility tradeoffs and real-world impact. Known for translating academic methods into deployed solutions, she combines rigorous experimental design with collaborative, cross-sector partnerships.
10 years of coding experience
2 years of employment as a software developer
Doctor of Philosophy - PhD Electrical Engineering and Computer Science, Doctor of Philosophy - PhD Electrical Engineering and Computer Science at University of California, Berkeley
Liceul Teoretic "Orizont"
Bachelor's Degree Operations Research and Financial Engineering, Bachelor's Degree Operations Research and Financial Engineering at Princeton University
Romanian, English, Russian, Turkish