Abstract: Massive amounts of medical data such as from electronic health records and body-worn sensors are being collected and mined by researchers, buttranslating findings into actionable knowledge remains difficult. The first challenge is finding causes, rather than correlations, when the data arehighly noisy and often missing, and relationships are more complicated than pairwise links between variables. The second challenge is then using these relationships to explain specific cases, such as why an individuals blood glucose is raised. In this talk Idiscuss new methods for both causal inference and explanation from complex and uncertain data, and how they can be used to make better decisions and provide individualized feedback.

Speaker Bio:

Samantha Kleinbergis an Assistant Professor of Computer Science at Stevens Institute of Technology. She received her PhD inComputer Science from New York University in 2010 and was a Computing Innovation Fellow at Columbia University in the Departmentof Biomedical informatics from 2010-2012. She is the recipient of NSF CAREER and JSMF Complex Systems Scholar Awards. She isthe author of "Causality, Probability, and Time" (Cambridge University Press, 2012) and "Why: A Guide to Finding and Using Causes" (OReilly Media, 2015), a nontechnical introduction to causality.

Schedule:
6pm to 6:20 networking and "chit-chat". 6:20pm we do a lightning round of introductions for five attendees.

Talk (and periscope) starts at 6:30pm.

We will turn off the periscope for the last fifteen minutes of the talk, where we can talk freely about unpublished recent work.

Quiz at 7:45pm and networking after that.

The prize in the quiz will be a copy of Samantha's book.

The aim of the meetup is for people to share their knowledge with like-minded people.

Apply to speak in this meetup group (on a future date)