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Daniel Lowd is an Associate Professor in the Department of Computer and Information Science at the University of Oregon. His research covers a range of topics in statistical machine learning, including statistical relational representations, unifying learning and inference, and adversarial machine learning applications (e.g., spam filtering). He has received a Google Faculty Award, an ARO Young Investigator Award, and the best paper award at DEXA 2015. He also coauthored the book "Markov Logic: An Interface Layer for Artificial Intelligence" with Pedro Domingos, published by Morgan & Claypool.