Lectures

Gerrymandering, which occurs in district-based legislative systems, is defined as the drawing of boundaries to protect or target an individual or a group. In the United States, partisan gerrymandering has distorted representation to all-time modern highs. Data analysis and computing, which powered the original offenses, can also be harnessed for diagnosis and prevention. Diagnosis, which can help state and federal courts, can be as simple as a t-test, or as complex as the automated exploration of billions of plans. Prevention requires the ability to draw plans and predict their consequences. With the right open data and software, reformers and interested citizens can get involved in the redistricting process. This talk will describe the road to reform from a data-analytic perspective, and ask how data science can help terminate gerrymandering.

Many systems have been proposed for using technology to help individuals and public health officials better respond to the COVID-19 pandemic. This talk will analyze the major proposed uses of information technology in the public health response to COVID-19, including aggregate ...

James Baldwin Lecture Series: Arvind Narayanan - The Limits Of The Quantitative Approach To Discrimination

In a couple of years, "study items" for the 6G security standard will be set. Security issues not included in these study items are unlikely to be standardized and patched even in 6G. Therefore, before these study items are set, the security research community needs to put in ...