Name:Amir Ghasemian

Bio:

My research develops computational methods to understand, predict, and improve complex networked systems—while mapping the fundamental limits of inference: when data contains enough signal and when algorithms can efficiently recover it. Drawing on network science, machine learning, causal inference, statistical physics, and information theory, I organize my work around three aims: Empirical Analysis of Computational Methods (EACM)—evaluating how methods succeed or fail across different settings to understand when results generalize; Theoretical Analysis of Inference Limits (TAIL)—using probabilistic models and phase-transition analyses to determine information-theoretic and computational boundaries; and Real-world Applications of Inference and Learning (RAIL)—translating principled methods into actionable insights for social science, public health, and digital platforms. My methodological contributions span community detection, link prediction, and causal inference in network experiments, with applications in social dynamics, digital media ecosystems, public health interventions, and human-AI interaction.

Country:United States

Affiliation:University of California, Los Angeles

Research interests:network science, statistical inference, causal inference, information theory, machine learning, data mining, and signal processing