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James Cussens is a Senior Lecturer at the University of Bristol, specializing in machine learning with a focus on learning Bayesian networks from data. His work primarily explores the representation of relationships between variables and their application in modeling causal relations. In addition to his expertise in Bayesian networks, he conducts research on the intersection of machine learning and logic, aiming to develop algorithms that can analyze complex data structures. His recent projects include the 'Code Encounters' initiative, which investigates algorithmic risk profiling tools in the housing market, as well as various health policy studies utilizing machine learning and causal inference methods. Cussens is dedicated to teaching machine learning at the undergraduate level and has contributed significantly to various academic discussions and collaborations within the field.
University of Bristol • Bristol, England
Teaching and researching machine learning, focusing on Bayesian networks and their applications in various fields.
Department of Physics research themes include Astrophysics, Materials and Devices, Particle Physics, and Quantum and Soft Matter.