IFDS workshop brings together data science experts to explore ways of making algorithms that learn from data more robust and resilient
The Institute for Foundations of Data Science (IFDS) recently brought mathematicians, statisticians, computer scientists and other data science experts to the University of Washington campus to discuss ways of addressing these questions at the IFDS Workshop on Distributional Robustness in Data Science, which was held in early August at the Bill & Melinda Gates Center for Computer Science & Engineering. As its name suggests, the workshop focused on exploring “distributional robustness.” This is a promising framework and research area in data science aimed at addressing complex shifts and changes in data, which are fielded by automated devices and processes such as the algorithms used in AI and machine learning.
Zaid Harchaoui, who is a Professor in the UW Department of Statistics, an IFDS founding member and part of the Institute’s leadership, and the workshop’s program chair participated in the IFDS workshop as one of the speakers. Invited speakers were from a wide range of academic disciplines and well over half of the invited speakers were female or from underrepresented minority groups. The talks covered theoretical, technical and socio-technical aspects of distributional robustness.