Abigail Drummond advanced a novel machine-learning driven technique to map the outbreak risk for dengue, as a case study example. She started by collecting climate and anthropogenic data from 2000 to 2019. She used this data to model current dengue outbreak risk using various machine-learning based species distribution models. She then compared the outbreak risk to the distribution of the mosquito species, Aedes aegypti, the main vector for dengue.

A two-day DataX workshop that covered a wide range of scientific topics, from Bayesian inference techniques to looking at machine learning in the context of the larger world, was held from May 13^th to the 14^th at Princeton University’s Friends Center. According to its organizers, the event, “Tutorial Workshop on Machine Learning for Experimental Science,” was meant to disseminate current topics and techniques in the field so that scholars may advance their research.

The detailed lecture notes that Ramadge distributes every week are legendary. They have proved to be indispensable to the many students who have taken his courses over the years since Ramadge, whose scholarship focuses on signal processing and machine learning, joined the faculty in 1984.

The Center for Statistics and Machine Learning held its annual undergraduate poster session earlier this month. Hosted virtually, 124 students participated in the event and hailed from 13 departments and centers, including African American Studies, chemical and biological engineering, and ecology, just to name a few. CSML feted the poster session participants with a celebratory in-person event on May 12^th.