Amiremad Ghassami

Amiremad Ghassami is an Assistant Professor in the Department of Mathematics and Statistics at Boston University. His research focuses on causal inference and discovery, statistical learning theory, and semiparametric statistics. He develops methodologies to address challenges in causal effect estimation, policy optimization, and causal graph identification in complex real-world data scenarios such as unobserved confounders and measurement errors. His work integrates modern statistical techniques, machine learning, and information theory.

Dr. Ghassami holds a PhD in Data Science and Communications from the University of Illinois at Urbana-Champaign (2020), advised by Prof. Negar Kiyavash, and completed postdoctoral research at Johns Hopkins University under Professors Ilya Shpitser and Eric Tchetgen Tchetgen. He co-organizes the Statistics and Probability Seminars at Boston University.

Research Interests:

• Causal Inference and Discovery
• Statistical Learning Theory
• Semiparametric Statistics
• Probabilistic Graphical Models

His recent publications emphasize causal data fusion, mediation analysis, and brain connectivity studies using fMRI. He addresses methodological challenges in handling unobserved variables and nonignorable missing data through innovative data fusion strategies and debiased estimation techniques.

Dr. Ghassami has contributed to causal structure learning algorithms for cyclic and acyclic models, with applications to neuroscience and policy evaluation. His work bridges theoretical foundations and practical data-driven solutions for modern causal analysis problems.