Dr. Brandeis Marshall Biography

Dr. Marshall's research focuses on data integration and knowledge management, which is at the confluence of business intelligence and data analytics. While the rising velocity, scale, and variety of data remain unresolved challenges within data analytics, establishing an adaptable data architecture to handle unanticipated queries is a big challenge in business intelligence. The representation and interpretation of data is a recurring issue in these difficulties. Her research goal focuses on the study and enhanced characterization of complex data networks in order to address these issues. Dr. Marshall's study goal is to produce meaningful information through effective data summarization. Her research career has three major emphases, with business intelligence demands taking precedence: There are three types of data analysis: (1) labeled data analysis, (2) unlabeled data analysis, and (3) fixed-length data analysis. Dr. Marshall has over 25 papers as a consequence of this. Dr. Marshall is the Director of the Data Analytics and Exploration (da+e) Laboratory, a research and education environment dedicated to informing lab members (faculty and students) about real-world data issues, challenges, and solutions while also contributing to research in various aspects of business intelligence through federal and industry-sponsored projects. Timely data gathering for an aviation project, the #BlackTwitter initiative, and data/database security curricular development are among the current da+e lab activities. She has been mentoring the next generation of STEM workers, particularly those from underrepresented communities, since 2009. Serving on the program committees for the ACM Richard Tapia Diversity in Computing Conference, SuperComputing's Broader Participation, and heading the Purdue College of Technology cohort to the 2012 Grace Hopper Celebration of Women in Computing are just a few examples of this engagement. Beginning in 2012, she and Dr. Caio Soares (then Bosch, now Zynga) began co-leading the ACM SIGKDD Conference on Knowledge Discovery and Data Mining's Broadening Participation in Data Mining Program.

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