Clayton Morrison

Clayton Morrison is an Associate Professor at the University of Arizona, specializing in artificial intelligence and machine learning. His research focuses on machine learning, probabilistic modeling, causal inference, knowledge representation, and automated planning. He is affiliated with the Core Faculty and PhD Faculty in Artificial Intelligence and Machine Learning programs.

His recent work emphasizes information extraction from scientific literature, legal documents, and code binaries, leveraging neural networks and generative models. Notable projects include the Eidos, INDRA, and Delphi systems for causal model recovery and the Tomcat dataset for benchmarking. His research bridges theoretical advancements with practical applications in 3D texture generation, lightweight object detection, and code synthesis.

He has contributed to over 60 peer-reviewed articles spanning machine learning, computer vision, and computational linguistics. His work often addresses interdisciplinary challenges such as linking mathematical formulas to textual descriptions (MathAlign) and modeling biological event contexts in biomedical texts.

Morrison's lab focuses on creating AI systems that can autonomously learn from complex data, with applications in automated model assembly (Automates) and federated learning frameworks. His research has been supported by grants exploring reinforcement learning, multi-hop inference, and explainable AI.