With over 15 years of professional experience in government research and development as well as being the principle investigator (PI) of more than $2 million in research funds, Dr. Patton, a Distinguished Research Staff member, leads the Learning Systems Group within the Data and AI Systems Section at Oak Ridge National Laboratory (ORNL). His research focuses on the development of artificial intelligence and high performance computing to enable knowledge discovery from large and complex data sets. His work has achieved more than 100 publications, 4 patents, 3 software copyrights, 3 R&D 100 Awards, and 3 nominations for the Association of Computing Machinery Gordon Bell Award where 2 of those 3 nominations resulted in being selected as a finalist. Using his expertise and passion for artificial intelligence and machine learning systems, he actively leads multiple teams of research staff in creating new learning systems for different applications. He is fully involved in the development cycle of his projects as well as the mentoring of his team members. Based on this experience, he successfully leads and motivates several teams of highly trained research staff to develop award winning artificial intelligence technologies that solve significant national challenges.

Dr. Patton leads the Nature Inspired Machine Learning team with the Computational Data Analytics group at Oak Ridge National Laboratory. His research at ORNL has focused on nature-inspired analytic techniques to enable knowledge discovery from large and complex data sets, and has resulted in approximately 40 publications pertaining to nature-inspired analytics and 3 patents. He has developed several software tools for the purposes of data mining, text analyses, temporal analyses, and data fusion, and has led the development of an evolutionary algorithm to identify optimal hyper parameters of a deep learning network.

Dr. Patton joined the Computational Data Analytics Research group at Oak Ridge National Laboratory (ORNL) in 2003. His research at ORNL has focused on nature-inspired analytic techniques to enable knowledge discovery from large and complex data sets, and has resulted in approximately 30 publications pertaining to nature-inspired analytics and 3 patent applications. He has developed several software tools for the purposes of data mining, text analyses, temporal analyses, and data fusion, and has developed a genetic algorithm to implement maximum variation sampling approach that identifies unique characteristics within large data sets.He has participated as a researcher for the Data Analytics for Medicine using Semi-Supervised Learning (DAMSEL) project at ORNL. For that project, he participated in the research and development of text analysis techniques based on maximum variation sampling implemented as a genetic algorithm and techniques for longitudinal patient record analysis based on wavelets and has successfully applied them to patient records in mammography and abdominal aortic aneurysms. He has published this work in several conferences, workshops, and book chapters. His techniques have enabled the automated analysis of unique patient characteristics that show correlation to patient outcomes as well as longitudinal analysis of patient records that help identify precursor words and phrases. Dr. Patton is a member of the Institute of Electrical and Electronics Engineers (IEEE) as well as the IEEE Computational Intelligence Society. He is also an active member of the Association for Computing Machinery (ACM) as well as the ACM Special Interest Group for Genetic and Evolutionary Computation (SIGEVO) and also the ACM Special Interest Group on Health Informatics (SIGHIT).

Research assistant to Dr. Gwendolyn Walton

Research assistant to Dr. Lynne Parker

Research assistant to Dr. Gwendolyn Walton

Research Assistant to Dr. Lynne Parker