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Yang Feng

Associate Professor of Biostatistics

New York University

Yang Feng is an associate professor of biostatistics in the School of Global Public Health at New York University.

Feng focuses on developing and applying machine learning methods in public health, high-dimensional data analysis, network models, nonparametric and semiparametric methods, and bioinformatics. He also leads the Feng Lab.

News

  • Dr. Feng was elected as a fellow of the American Statistical Association in 2022.

Info

  • 2022 Workshop on Statistical Network Analysis and Beyond:

Hosted by the GPH Department of Biostatistics from Wednesday, August 3 - Friday, August 5, 2022.

We are bringing together researchers on network analysis and beyond to exchange ideas and recent works for SNAB2022. The workshop will cover topics including analysis of social networks and biological networks, tensor analysis, and deep learning.

Please visit here to learn more about the workshop.

He has published papers in Annals of Statistics, Annals of Applied Statistics, Biometrika, IEEE Transactions on Pattern Analysis and Machine Intelligence, Journal of Royal Statistical Society Series B, Journal of the American Statistical Association, Journal of Machine Learning Research, Science Advances, Journal of Econometrics, Journal of Business & Economic Statistics, etc.

He is currently an associate editor for

His research is partially supported by

  • NIH 1R21AG074205-01: Multiclass classification under prioritized error control and specific error costs with applications to dementia classification
  • NSF CAREER Grant DMS-2013789: CAREER: Statistical inference of network and relational data
  • NSF Grant 2034022: RAPID: Behavioral Epidemic Modeling For COVID-19 Containment

My Google Scholar Page ( By Year)

My Math Genealogy Graph

Open Positions and Opportunities:

  • Post-Doctoral Associate in Biostatistics:

The Department of Biostatistics at the NYU School of Global Public Health is seeking candidates with a Ph.D. in Statistics/Biostatistics, Computer Science, or related fields for an exciting full-time Post-Doctoral Associate position available beginning on June 1 or Sep 1, 2022, working with Dr. Yang Feng and Dr. Rebecca Betensky.

The postdoc associate will work to:

  • Develop novel machine learning methods and their associate theory.
  • Apply existing and new machine learning methods to public health and medical data.
  • Develop companion software.

Please apply via Interfolio here.

  • Research Assistant:

Feng Lab is continuously looking for talents at undergraduate, master and Ph.D. level. If you are interested, please submit an application at https://bit.ly/2KMEflH.

Interests

  • Machine learning in public health
  • High-dimensional statistics
  • Network models
  • Nonparametric and semiparametric methods
  • Bioinformatics

Education

  • PhD in Operations Research, 2010

    Princeton University

  • BS in Mathematics, 2006

    University of Science and Technology of China (USTC)