Improved methods for studying hard-to-reach populations published in PNAS

Respondent-driven sampling is a popular network-based approach to
sample hard-to-reach populations, where participants refer contacts
into the sample through a coupon system. It has been particularly
useful in HIV research where individuals most at risk (e.g., people
who inject drugs) are unlikely to participate in conventional sampling
schemes. Many major health organizations, including the Centers for
Disease Control and the World Health Organization, employ this
approach to quantify the prevalence of HIV in these at-risk groups.
Unfortunately this type of network sampling suffers from a significant
drawback: because referred contacts often share similar
characteristics, samples are highly correlated which can lead to
exceedingly variable estimates.

In work that just appeared in the Proceedings of the National Academy
of Sciences, IFDS members Sebastien Roch and Karl Rohe introduced a
new estimation technique for respondent-driven sampling with a
substantially reduced variability…

Roch wins Best Paper Award at RECOMB

IFDS member Sebastien Roch and former mathematics Ph.D. student Kun-Chieh (Jason) Wang won Best Paper Award at the prestigious 22nd Annual International Conference on Research in Computational Molecular Biology (RECOMB) 2018.

Willett and Raskutti Build Better Tools for Big Data

In the era of Big Data, researchers like electrical and computer engineer Rebecca Willett and statistics professor Garvesh Raskutti, both part of the Institute for Foundations of Data Science at the University of Wisconsin-Madison, are developing new methods and tools to make sense of how discreet events can influence the occurrence of other events over time.