the Ng

Lab

Exploring Human-Environment Systems

Our group works at the intersection of biology and chemistry to understand and predict the fate of chemicals in the environment. We build and validate models for legacy and emerging chemicals at multiple scales, from molecules to 🐁 organisms 🐟 to global systems. Learn about our modeling philosophy (we ❤️ mechanisms), the chemicals we focus on (per- and polyfluorinated alkyl substances; brominated flame retardants; polycyclic aromatic hydrocarbons-- and more...) and our current projects and interdisciplinary collaborations.

Projects
Publications
People

Why Model? Read about or research philosophy

Latest News from the Ng Lab

February 2020: Welcome to the Future! Hard to believe we are in a new decade. While I see no signs of jet packs or flying cars, the Ng lab has some fun new equipment. Read on for more...

The PFAS in 💩 Project:

Jenna Kuhn, who recently joined our lab for a rotation from her Public Health graduate program, is working with senior graduate student Manoochehr Khazaee to investigate the impacts of PFAS on the microbiome. This means looking at mouse 💩 and cutting up mouse tissues in very frosty conditions! Bet they can't wait for spring.









This project is an exciting collaboration with Pitt CEE's Dr. Sarah Haig, Dr. Stacy Wendell from Pitt Pharmacology & Chemical Bioloy, and ECU's Dr. Jamie DeWitt. Stay tuned for results!

Our first LC-QQQ!









The Environmental Engineering Laboratory at Pitt CEE has recently acquired its first LC/MS-MS and we are gearing up to measure PFAS in-house! This is an exciting addition to the department's analytical capabilities and will enable better assessment of PFAS contamination in Western PA. Another recent addition to our group, Ms. Congyue (Vicky) Wu, has been instrumental (sorry I couldn't help myself) in getting us up and running. We are looking forward to exploring this new capability as the year hums along.










Machine Learning to Predict PFAS Bioactivity

Weixiao Cheng's latest paper describing the use of machine learning for predicting the bioactivity of PFAS was selected as ACS Editor's Choice. Read the article here, and scroll to the end of the paper to watch a short video where Cheng explains the highlights of his study.