Biography
Wenyu Zeng is an Intramural Research Training Award postbaccalaureate fellow in the Informatics Core within NCATS’ Division of Preclinical Innovation. Her projects focus on building predictive models using various kinds of data. Zeng helps predict the toxicities of chemical compounds from the Tox21 data set, as well as the severity levels for patients with COVID-19 using their pre-COVID metabolomic data.
Prior joining NCATS, Zeng received her Bachelor’s degree in statistics from the University of Pittsburgh and her Master’s degree in data science from The George Washington University, where she cultivated her interest in the implementation of machine and deep learning techniques using biomedical data.
Research Topics
Zeng is interested in using her data science skills to help interpret biomedical data more visually, identifying intrinsic factors and reducing the time and cost associated with experiments.