Sadegh Shirani

Sadegh received his PhD in Operations, Information & Technology from the Stanford Graduate School of Business, where he was fortunate to be advised by Mohsen Bayati. 

His research develops principled methods for modeling, learning, and decision-making in complex systems. His work spans causal reasoning from complex data, LLM-based simulation of human and human–AI interactions, AI safety in interactive and multi-agent systems, and reliable AI for decision-making. A recurring challenge in Sadegh's research is that the underlying structure is difficult to observe directly: network connections may be unknown, agent types may be latent, or the relevant information may be contained in unstructured data. He studies the theoretical foundations of these problems and develop methods that are both theoretically grounded and practically reliable.

Sadegh's work combines ideas from probabilistic modeling, statistical physics, and optimization. His research is mainly motivated by systems where uncertainty and limited data make reliable decision-making challenging. Examples include public health interventions, experimentation on online platforms, and interactive AI systems.

 He is always happy to discuss research ideas and potential collaborations with students and colleagues, and is actively looking for students interested in these research directions.