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Impact x2 Qais

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How can we work together to promote better cultural understanding worldwide?

Qais Akbar Omar (GRS’16), a graduate student in the Creative Writing Program, has published a much-praised memoir, A Fort of Nine Towers: An Afghan Family Story. He recalls how the violence and tumult of civil war jolted his family, who, despite losing relatives, their home, and possessions, continued to nurture his wish to attend a university.

Impactx2 Call to Action

With your help, students like Qais gain the skills they need to tell their story and give us a broader understanding of the world.

Will you support CAS?

Hao Chen, Stanford

Starts:
4:00 pm on Thursday, February 7, 2013
Ends:
5:00 pm on Thursday, February 7, 2013
Location:
MCS 148
Title: Graph-Based Change-Point Detection. Abstract: After observing snapshots of a network, can we tell if there has been a change in dynamics? After reading chapters of a historical text, can we tell if there has been a change in authorship? Given a sequence of independent observa- tions, we are concerned with testing the null hypothesis of homogeneity versus change-point alternatives, where a segment of the sequence diers in distribution from the rest. This problem has been well studied for observations in low dimension. Currently, many problems can be formulated in the change-point framework but with observations that are high-dimensional or non-Euclidean, where existing methods are limited. We develop a general nonparametric framework for change-point detection that relies on a distance metric on the sample space of observations. This new approach, which relies on graph-based tests, can be applied to high dimensional data, as well as data from non-Euclidean sample spaces. An analytic approximation for the false positive error probability is derived and shown to be reasonably accurate by simulation. We illustrate the method through the analysis of a phone-call network from the MIT Reality Mining project and of the authorship debate of a classic western novel.