The ADSI Summer School on Foundations of Data Science was organized by U. Washington and U.W. Madison over 4.5 days (August 13-17, 2019) at the University of Washington in Seattle. The event gave a large and advanced introduction to fundamental aspects of data science, including stochastic optimization, convex optimization, convex geometry and sampling, computational imaging, deep generative models, fairness in machine learning, reinforcement learning and active machine learning. The format was mostly lectures accompanied with notes. They were generally organized by a first part covering basic concepts and a second part introducing present research with an emphasis on recent discoveries of fundamental principles. An interactive lab was also conducted in computational imaging.
The target audience was graduate students and postdocs, with some knowledge of some of the fundamental areas of data science, but a desire to learn about other topics in the area. Lecturers included Paquette (U. Waterloo); Diakonikolas, Hardt (U.C. Berkeley); Lee, Jamieson, Oh (U. Washington); Nowak (U.W. Madison); Willett (U. Chicago); Agarwal (Microsoft Research). A poster session was also organised at which participants presented their research.
The summer school was made possible by support from National Science Foundation. The program was organized by Dmitriy Drusvyatskiy and Zaid Harchaoui.