Behshad Azizian is an industrial engineer with a professional background in safety engineering for transport, logistics, and production. Her profile reflects a strong interest in applying engineering and analytical methods to complex industrial systems, particularly in operational reliability and performance improvement.

Since 1 October 2024, she has been serving as a Visiting Master Researcher at the Data-Driven Industrial Systems group. Her research focuses on Adaptive Causal Inference for Predictive Maintenance. In this work, she has developed advanced models that integrate causal AI and reinforcement learning to optimize predictive and preventive maintenance strategies. Her research emphasizes real-time feedback mechanisms to ensure model stability, resolve operational conflicts, and improve decision-making for vehicle component reliability. She also applies nonlinear data analysis techniques to identify causal relationships and support more robust maintenance planning.