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Colloquium Talk

Enforcing equality: court rulings, indigenous women, and political participation in Oaxaca, Mexico

Within the last decade, Mexico´s federal electoral courts have taken unprecedented steps to promote affirmative action in favor of women´s political participation. At the federal, state, and municipal levels, this has largely meant rulings that support legislation on gender-based quotas for public posts.  A stumbling block to this affirmative action initiative has been the predominately indigenous municipalities that hold local elections through tradition and custom instead of universal suffrage and secret ballot. Legally recognized as part of indigenous people´s collective right to self-determination, election through custom and tradition has been difficult to fit into existing juridical logics of gender equality.  In the past three years, however, a growing number of electoral conflicts appealed to the federal courts have brought the question of indigenous women´s political participation to the forefront. I examine several of these cases to explore how the courts mediate between the question of collective self-determination and individual women´s rights, how they seek to promote a liberal notion of gender equality, and how women and communities are responding to their rulings in unexpected ways.  I argue that what is at stake is more than just women´s political participation; rather, these rulings reflect contemporary contestations over gender, indigeneity, modernity, and democracy in Mexico more broadly.  
Holly Worthen is a Professor at the Instituto de Investigaciones Sociológicas at the Universidad Autónoma Benito Juárez de Oaxaca in the state of Oaxaca, Mexico.  She received her Phd in Geography from the University of North Carolina at Chapel Hill.  Her work focuses on gender, migration, development and indigenous politics.
 
Date:
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Location:
231 White Hall Classroom Building
Event Series:

Large-Scale Numerical Linear Algebra Techniques for Big Data Analysis

As the term ``big data'' appears more and more frequently in our daily life and research activities, it changes our knowledge of how large the scale of the data can be and challenges the application of numerical analysis for performing statistical calculations on computers. In this talk, I will focus on two basic statistics problems---sampling a multivariate normal distribution and maximum likelihood estimation---and illustrate the scalability issue that many traditional numerical methods are facing. The large-scale challenge motivates us to develop linearly scalable numerical linear algebra techniques in the dense matrix setting, which is a common scenario in data analysis. I will present several recent developments on the computations of matrix functions and on the solution of a linear system of equations, where the matrices therein are large-scale, fully dense, but structured. The driving ideas of these developments are the exploration of the structures and the use of fast matrix-vector multiplications to reduce the quadratic cost in storage and cubic cost in computation for a general dense matrix. ``Big data'' provides a fresh opportunity for numerical analysts to develop algorithms with a central goal of scalability in mind. Scalable algorithms are key for convincing statisticians and practitioners to apply the powerful statistical theories on large-scale data that they currently feel uncomfortable to handle.

Date:
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Location:
CB 335
Event Series:
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