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Recent news reports stated that the National Security Agency has pursued new methods that have allowed the agency to monitor telephone and online communication, encrypted information that was thought to be virtually immune to eavesdropping. What steps can and should computer scientists take in response to this privacy threat? How will the recent revelations affect the future of cryptography—the field of encoding and decoding electronic communication and transmissions for the purposes of privacy, reliability and efficiency?
To address these questions, the Johns Hopkins University Information Security Institute will host an hour-long roundtable discussion, moderated by Anton Dahbura, interim executive director of the Information Security Institute, and Avi Rubin, the institute’s technical director. Other participants will include Johns Hopkins cyber-security experts Matthew Green, Stephen Checkoway and Giuseppe Ateniese.
The event will be streamed live at https://connect.johnshopkins.edu/jhuisicrypto/, and also will be posted online following the event.
NOTE: Seating at this public event will be limited. Members of the media who plan to cover the discussion are asked to RSVP to Phil Sneiderman, firstname.lastname@example.org.
Laurent Younes, professor and chair of the Department of Applied Mathematics and Statistics at Johns Hopkins University, will present “Change Point Estimation of Brain Shape Data in Relation with Alzheimer’s Disease.”
Abstract: The manifestation of an event, such as the onset of a disease, is not always immediate and often requires some time for its repercussions to become observable. Slowly progressing diseases, and in particular neuro-degenerative disorders such as Alzheimer’s disease (AD), fall into this category. The manifestation of such diseases is related to the onset of cognitive or functional impairment and, at the time when this occurs, the disease may have already had been affecting the brain anatomically and functionally for a considerable time. We consider a statistical two-phase regression model in which the change point of a disease biomarker is measured relative to another point in time, such as the manifestation of the disease, which is subject to right-censoring (i.e., possibly unobserved over the entire course of the study). We develop point estimation methods for this model, based on maximum likelihood, and bootstrap validation methods. The effectiveness of our approach is illustrated by numerical simulations, and by the estimation of a change point for atrophy in the context of Alzheimer’s disease, wherein it is related to the cognitive manifestation of the disease. This work is a collaboration with Marilyn Albert, Xiaoying Tang and Michael Miller, and was partially supported by the NIH.
For those who cannot make it to the Homewood campus, the seminar will be video-conferenced to Traylor 709 on the School of Medicine campus.
For those who attend at Homewood, lunch will be provided at noon.