• Start Date: July 18, 2017
  • End Date: July 22, 2017
  • Event Start Time: 9:30 AM
  • Event End Time: 3:00 PM
  • Organizers: Xu Wang | Sibel Tari | Emina Soljanin | Linda Ness | Deanna Needell | Julie Mitchell | Regina Liu | Kathryn Leonard | Giseon Heo | Ellen Gasparovic | Carlotta Domeniconi
  • Location: ICERM | Brown University | 11th Floor
  • Research Collaboration Workshop for Women in Data Science and Mathematics (WiSDM). This program will bring together women at all stages of their careers, from graduate students to senior researchers, to collaborate on problems in data science. The scientific focus will be on cutting edge problems in the areas of predictive modeling, multi-scale representation and feature selection, statistical and topological learning, and related areas. Data science is a cross-disciplinary field relying on statistics, computer science and mathematics and driven by problems in many other disciplines. While data science has emerged as a prominent new field enrolls record numbers and attracts research talents from many scientific disciplines, the role of theoretical and applied mathematics has not been highly visible. Mathematics provides many structured representations that can be in the analysis of data arising from such diverse fields as geometric measure theory, classical analysis, computational topology, shape theory, algebraic statistics, and spectral graph theory. Furthermore, mathematics may enable more classes of data sets to be represented as measures and distributions which could then leverage classical statistical techniques.

    Meanwhile, mathematics and computer science are two of three disciplines with the lowest percentage of women attaining PhDs (28% and 24%, respectively). Creating explicit research bridges between these groups will provide networks of women with similar research interests, and will also create pathways for the female-friendly culture in statistics to make its way into mathematics and computer science. This workshop will generate research collaborations, and highlight mathematics as a primary contributor. Successful applicants will be assigned to a research problem based on their expertise. Each group will aim to include a more senior person in each of statistics, machine learning, and mathematics.

    This workshop is being organized by the Institute for Computational and Experimental Research in Mathematics (ICERM) at Brown University. Additional details are available on the workshop's main webpage.

  • Sponsors: Institute for Computational and Experimental Research in Mathematics (ICERM) | Brown University
  • Event Keywords: data science