Center for Statistics and Machine Learning

 

 

Featured News

Abigail Drummond: using data science to understand dengue fever and other viral outbreaks
June 29, 2022

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.

Featured Event

Seminars on security and privacy in machine learning: Alexandre Sablayrolles (Meta-Facebook AI)
Tue, Jul 5, 2022, 1:00 pm

The motivation for the seminar is to build a platform to discuss and disseminate the progress made by the community in solving some of the core challenges. We intend to host weekly talks from leading researchers in both academia and industry. Each session will be split into a talk (40 mins) followed by a Q&A + short discussion session (20 mins).

Open Positions

Data Scientist Positions Available at Princeton
June 12, 2020

Do you have a strong background in scientific programming, academic research, and are eager to contribute to groundbreaking research? Do you love to write code and analyze data? Then please consider joining our growing team of data scientists! 

Princeton University is building a community of data scientists to work in partnership…


 

Latest News

Abigail Drummond: using data science to understand dengue fever and other viral outbreaks

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.

DataX workshop held for researchers who want to incorporate data science and machine learning into their work

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 13th to the 14th 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.

Peter Ramadge, CSML director, recognized for outstanding teaching along with three faculty members

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.

Projects from CSML students showcase innovative thinking and diversity of disciplines

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 12th

Princeton University is actively monitoring the situation around coronavirus (COVID-19) and the evolving guidance from government and health authorities. The latest guidance for Princeton members and visitors is available on the University’s Emergency Management website

Upcoming Events

Seminars on security and privacy in machine learning: Alexandre Sablayrolles (Meta-Facebook AI)
Tue, Jul 5, 2022, 1:00 pm

The motivation for the seminar is to build a platform to discuss and disseminate the progress made by the community in solving some of the core challenges. We intend to host weekly talks from leading researchers in both academia and industry. Each session will be split into a talk (40 mins) followed by a Q&A + short discussion session (20 mins).

Location
Virtual
Speaker