Yeganeh Alimohammadi

Yeganeh Alimohammadi is an Assistant Professor of Data Sciences and Operations at the USC Marshall School of Business. Her research develops algorithms and models for learning and decision-making under uncertainty, with applications to digital platforms, social networks, and epidemic forecasting.

Her work integrates probability theory, algorithm design, and machine learning to address challenges of scalability, robustness, and data uncertainty. In data-rich settings, she designs sampling and thinning methods that make large-scale analysis tractable and reliable. In data-scarce settings, she develops targeted data collection strategies and robust inference methods that enable policymakers and organizations to draw reliable conclusions from partial and noisy data.

Before joining USC, she received her PhD in Management Science & Engineering from Stanford University. She has been recognized as a UC President’s Postdoctoral Fellow at UC Berkeley and as a Research Fellow at the Simons Institute for the Theory of Computing.