Dr. Brandeis Marshall

Dr. Marshall’s research is positioned at the intersection of business intelligence and data analytics, specifically the area of data integration and knowledge management. A grand challenge in business intelligence is creating an agile data infrastructure to answer unexpected inquiries while the growing rate, scale and diversity of data remain open issues within data analytics.

The central theme amongst these challenges is data representation and interpretation. Her research agenda addresses these challenges by centering on the study and improved characterization of complex data networks. Dr. Marshall’s research mission is aimed at effective summarization of data that creates valuable knowledge. Her research career has three main focuses, priority given to business intelligence needs: (1) labeled data analysis, (2) unlabeled data analysis and (3) fixed-length data analysis. As a result, Dr. Marshall has over 25 publications.

Dr. Marshall is the Director of the Data Analytics and Exploration (da+e) Laboratory, a research and education environment for educating lab members (faculty and students) of real-world data issues, challenges and solutions, and making research contributions in various aspects of business intelligence through federal and industry-sponsored projects. Current da+e lab activities include timely data acquisition for aviation project, #BlackTwitter project and data/database security curricular development.

Since 2009, she has been actively engaged in mentoring the next generation of STEM professionals, particularly those from under-represented groups. This engagement has included, but not limited to: serving on the program committees for the ACM Richard Tapia Diversity in Computing Conference, SuperComputing's Broader Engagement and leading the Purdue College of Technology cohort to the 2012 Grace Hopper Celebration of Women in Computing. Starting in 2012, Dr. Caio Soares (then Bosch, now Zynga) and she began co-leading the Broadening Participation in Data Mining Program, co-located with ACM SIGKDD Conference on Knowledge Discovery and Data Mining.