Abstract:

In this talk I will present several works on the interplay between machine learning and social dynamics. These revolve around three themes.

The first theme explores how machine learning can be applied to predicting the outcomes of social processes. For the task of predicting the spread of information in networks, we propose a hybrid approach for discriminatively training generative models of information propagation. Learning and inference in this approach can be done efficiently via a certain kernel embedding technique, which can...

Making sense out of data is one of the biggest challenges of our time.  As more data is gathered and ML systems become ubiquitous, our society can benefit from better predictions and enhanced data-driven decision systems. Yet, understanding data remains challenging in many application domains such as personalized medicine. Most relevant questions, e.g., what the underlying mechanisms of cancer are, cannot be stated as well-defined supervised problems, and might benefit enormously from interpretable models and rigorous data exploratory analyses involving multidisciplinary...

Could thousands of citizens, brought together by a common cause, work together to collaboratively draft a letter to their elected representatives, a mission statement for a new organization, or guidelines for legislation? Could they collaborate as equals, making decisions about content without an organizational hierarchy? Could they still do in the presence of significant disagreements?

We examine the design space of democratic document collaboration, in which a large number of authors are given equal power to draft changes to a document and to decide which of the proposed...

Leveraging “Real World Data” to Accelerate Progress in Cancer Medicine: Opportunities and Obstacles

Abstract: Historically, progress in cancer medicine stemmed from mechanistic basic research which led to the development of drugs tested in the context of randomized controlled trials. Although this process...