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Adjunct Faculty

K. Brent Venable| Professor
Ph.D., 2005, University of Padova
Research Interests: Artificial Intelligence

Parisa Kordjamshidi | Assistant Professor
Ph.D., 2013, University of Leuven
Research Interests: Machine learning, Artificial Intelligence, declarative learning based programming, knowledge representation, natural language processing, information extraction, big data, and text mining.

Helmut Alt | Visiting Professor
Ph.D., 1976, Universitaet des Saarlandes, Germany
Research Interests: Algorithms and Complexity, Computational Geometry

Jeffrey S. Vitter | Adjunct Professor, Distinguished Professor of Computer & Information Science at the University of Mississippi
Ph.D., 1980, Stanford University
Research Interests: Design and analysis of algorithms with many practical applications, primarily in four key subfields dealing with big data and data science. Dr. Vitter is perhaps best known for his work on external memory algorithms, which focuses upon alleviating the I/O communication bottleneck between different levels of memory. A second key aspect of his work on big data involves compressed data structures, where the goal is to search and operate directly upon compressed representations of data, yet still achieve fast response time. In a third aspect of big data, Dr. Vitter has done fundamental work on data compression for text, images, and video and is noted for his analytical bent. Fourth, Dr. Vitter and collaborators were the first in the database and systems communities to apply wavelets and compression techniques as key tools for summarizing, approximating, and predicting data, with applications to machine learning.

Yu-Ping Wang | Associate Professor of Biomedical Engineering
Ph.D., 1996, Xi'an Jiaotong University
Research Interests: Biostatistics and Bioinformatics

Prakash Panangaden | Adjunct Professor
Professor of Computer Science, McGill University, Montreal, CA

PhD, 1980, Physics, University of Wisconsin - Milwaukee
Research Interests: Probabilistic systems, logic and computation, quantum information theory