Jessica M. Clark is Assistant Professor of Information Systems in the Robert H. Smith School of Business at The University of Maryland, College Park. Prior to joining the Smith School, she completed her Ph.D. in Information Systems at the NYU Stern School of Business. Her research and teaching interests focus on data science applications in business analytics, advertising, television, social media, and crowdfunding. A recent project, “Mining Massive Fine-Grained Behavior Data to Improve Predictive Analytics” (published in Management Information Systems Quarterly) was awarded the 2017 European Research Paper of the Year by the Association for Information Systems.
SMITH BRAIN TRUST – Attempting to conduct predictive modeling from sparse, binary data sets is complicated. So researchers typically use what’s called unsupervised matrix-factorization based dimensionality reduction as an initial step in the process. But it’s not clear whether dimensionality reduction actually helps improve predictive modeling performance.
Women Leading Research: Jessica M. Clark
Jessica M. Clark is Assistant Professor of Information Systems in the Robert H. Smith School of Business at The University of Maryland, College Park. Prior to joining the Smith School, she completed her Ph.D. in Information Systems at the NYU Stern School of Business. Her research and teaching interests focus on data science applications in business analytics, advertising, television, social media, and crowdfunding.
