CIS department welcomes Assistant Professor Daniel Lowd
Daniel Lowd's 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). In 2009, he coauthored book on Markov logic with Pedro Domingos, published by Morgan & Claypool. He is also the recipient of graduate research fellowships from the National Science Foundation and Microsoft Research.
Daniel received his B.S. from Harvey Mudd College in 2003 and M.S. from the University of Washington in 2005. He is currently a doctoral candidate at the University of Washington.
This winter, Daniel will teach a new course called "Probabilistic Methods in Artificial Intelligence" (CIS 410/510), covering the probabilistic and statistical approaches that are revolutionizing AI, machine learning, bioinformatics, computer vision, natural language processing, and other fields. In the spring, he will teach CIS 413/513, "Advanced Data Structures," as well as a seminar on Markov logic.
More information about Daniel can be found at his website: http://ix.cs.uoregon.edu/~lowd/