Prof. Michael Cafarella's research has advanced the practice and application of big data. In particular, he has built software systems for crucial data management challenges such as information extraction, database integration, and feature engineering.
Prof. Cafarella has applied these systems to a number of problems, especially in the social sciences. He used data from social media streams to accurately predict government statistics, such as unemployment rates, without the overhead of surveys and data gathering that is traditionally employed. More recently, he contributed toward the DeepDive information extraction system and applied it to the problem of illegal sex trafficking; by analyzing online text sources, this effort generated information and tools that can aid law enforcement.
In previous work, he and Doug Cutting developed Hadoop, an open-source software framework for distributed storage and processing of very large data sets on computer clusters. Hadoop is currently employed by many research groups and large companies, including Facebook, Yahoo, Twitter, and more than half of the Fortune 50.
Prof. Cafarella received his PhD in Computer Science from the University of Washington in 2009 and joined the faculty at Michigan that year. He has published extensively in venues such as SIGMOD, VLDB, and elsewhere. He received an NSF CAREER award in 2011.
Prof. Honglak Lee's research interests lie in machine learning and its applications to artificial intelligence. In particular, he focuses on deep learning and representation learning, which aims to learn an abstract representation of the data by a hierarchical and compositional structure. Specific application areas include computer vision, audio recognition, robotics, text modeling, and healthcare.
Prof. Lee is currently developing deep learning algorithms aimed at disentangling variations from complex data, and the development of a graphical model with deep representations that can model complex dependencies between output variables.
Prof. Lee received his PhD in Computer Science from Stanford University in 2010 and joined the faculty at Michigan that year. He received best paper awards at ICML 2009 and CEAS 2005, and is a recent recipient of a Google Faculty Research Award. He has served as a guest editor of IEEE TPAMI Special Issue on Learning Deep Architectures, as well as area chairs of ICML, NIPS, ICCV, AAAI, IJCAI, and ICLR. He was named one of AI's 10 to Watch by IEEE Intelligent Systems in 2013 and received an NSF CAREER award in 2015.About the Morris Wellman Faculty Development Professorship
Michael P. Wellman, Professor of Electrical Engineering and Computer Science, endowed the Morris Wellman Faculty Development Professorship in his grandfather's name. Morris Wellman was an engineer who worked for most of his career as a civil servant of the City of New York. The professorship is awarded to junior faculty members in recognition of outstanding contributions to teaching and research.
Posted: January 14, 2016