Yeganeh Alimohammadi

Yeganeh 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 in data availability and quality, a challenge central to modern business operations. Her work connects theory to practice in domains such as digital platforms, social networks, and epidemic forecasting.

Methodologically, her work integrates probability theory, algorithm design, and machine learning to tackle the challenges of scalability, robustness, and uncertainty in operational systems across two extremes of data availability. In data-rich environments, where turning large-scale datasets into actionable insights is computationally demanding, she develops sampling and thinning methods that enable scalable and reliable analysis. In data-scarce environments, where obtaining data is costly, she designs targeted data collection algorithms and robust inference methods, allowing policymakers to make reliable inferences from partial and noisy data.

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