Genomics and digital phenotyping (electronic medical records, imaging, self-reported surveys, etc) have the potential to transform our understanding, diagnosis and treatment of human diseases. Realizing the potential of these rich data requires aggregating and sharing data across individual centers where the data is currently siloed. However, sharing data raises concerns about individual privacy. This workshop will explore the foundational algorithmic challenges of protecting individual privacy in this bio-medical context.
This workshop has a number of aims:
The workshop will be a mix of talks, posters, and breakout sessions centered around the different focus areas in which participants can work together to identify key challenges that need to be overcome, new theory or algorithms that may be needed, and how to delineate problem classes and solution frameworks. Specific initial focus areas include:
Additional topics and refinements will be made as the list of participants is finalized. This workshop will include a poster session; a request for posters will be sent to registered participants in advance of the workshop.
(Harvard University, SEAS)
Sriram Sankararaman (University of California, Los Angeles (UCLA))
Anand Sarwate (Rutgers University New Brunswick/Piscataway)
James Zou (Stanford University)