Jin Tian

Jin Tian is a Professor affiliated with the Mohamed bin Zayed University of Artificial Intelligence (MBZUAI), having previously held a Graduate Faculty position at Iowa State University. His research focuses on causal inference, machine learning, and artificial intelligence. Dr. Tian's work emphasizes causal discovery, causal effect identification, adversarial robustness in machine learning, and graphical models. He holds a Ph.D. in Computer Science from UCLA, an M.S. in Physics from UCLA, and a B.S. in Physics from Tsinghua University.

His research interests span causal inference frameworks, including handling hidden variables and developing algorithms for causal model testing. He also explores adversarial machine learning, regularization techniques for robustness, and large-scale data systems like AnnotatedTables. His contributions to causal effect estimation via instrumental variables and combining experimental results highlight his interdisciplinary approach.

Recent work includes advancements in polynomial-delay algorithms for causal model testing, unified covariate adjustment methods, and vulnerability-aware adversarial training. His research bridges theoretical foundations (e.g., graph-based complexity) with practical applications in machine learning security and causal reasoning.