Dr. Arinan Dourado is an Assistant Professor of Mechanical Engineering whose research advances trustworthy artificial intelligence for engineering and high-consequence decision-making. Drawing on dual PhDs in mechanical engineering, his work integrates machine learning with uncertainty quantification, system dynamics, control, and domain knowledge to determine not only whether an AI model is accurate, but whether its predictions are robust, explainable, reproducible, and consistent with the principles governing its application.

This vision anchors the TRUST (Trustworthy, Resilient, Uncertainty-Aware Systems and Technologies) initiative, which develops engineering methods for evaluating and improving the trustworthiness of AI-enabled systems. Dr. Dourado’s research spans physics-informed machine learning, prognostics and health management, autonomous and intelligent systems, and human-centered clinical decision support. His work seeks to translate these foundations into practical AI-assurance approaches for model validation, risk identification, and responsible deployment.

Dr. Dourado leads and contributes to interdisciplinary research involving engineering and medicine. His accomplishments include serving as Co-PI on a funded NSF project applying explainable machine learning to engineering-student persistence; developing physics-informed and probabilistic methods for industrial equipment health management; and collaborating with neurosurgery and orthopedic researchers on interpretable AI for clinical decision support. He also mentors graduate researchers and supervises industry-connected AI/ML projects with GE Appliances.

His teaching encompasses dynamics, system dynamics, vibrations, control, optimization, and graduate machine learning. Through research-guided and project-based instruction, Dr. Dourado prepares students to develop, evaluate, and responsibly apply computational methods in complex engineering environments.