This program brought together researchers working on algorithmic, mathematical, and statistical aspects of modern data science, with the aim of identifying a set of core techniques and principles that form a foundation for the subject. While the foundations of data science lie at the intersection of computer science, statistics, and applied mathematics, each of these disciplines in turn developed in response to particular long-standing problems. Building a foundation for modern data science requires rethinking not only how those three research areas interact with data, implementations, and applications, but also how each of the areas interacts with the others. For example, differing applications in computer science and scientific computing have led to different formalizations of appropriate models, questions to consider, computational environments (such as single machine vs. distributed data centers vs. supercomputers), and so on. Similarly, business, Internet, and social media applications tend to have certain design requirements and generate certain types of questions, and these tend to be very different from those that arise in scientific and medical applications. As well as these differences, there are also many similarities among these areas. Developing the theoretical foundations of data science requires paying appropriate attention to the questions and issues of domain scientists who generate and use the data, as well as to the computational environments and platforms supporting this work.
This program was supported in part by the Kavli Foundation and the Patrick J. McGovern Foundation.
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Long-Term Participants (including Organizers):
Visiting Graduate Students and Postdocs:
Those interested in participating in this program should send an email to the organizers at this datascience2018 [at] lists.simons.berkeley.edu (at this address.)
Program image by Luisa Lee