The Feng Lab at the NYU School of Global Public Health conducts methodological and theoretical research at the forefront of modern statistical learning, data science, and AI. Our group develops principled approaches for high-dimensional inference, network- and graph-structured data, nonparametric and semi-parametric modeling, and contemporary machine learning frameworks—including transfer learning, multi-task learning, and federated learning. We work closely with collaborators across medicine, genomics, epidemiology, neuroscience, and public health to translate methodological advances into impactful, domain‑driven applications.
Our research philosophy emphasizes rigor, creativity, and cross‑disciplinary engagement. Trainees in the group contribute to the full spectrum of statistical science, including developing new theory, designing computationally efficient algorithms, analyzing large-scale biomedical data, and presenting their work at leading scientific venues. The lab is committed to cultivating an environment that values intellectual independence, collaboration, and professional development.
📚 Explore our research outputs on the Publication Page.
Join the Feng Lab
We are always seeking curious, motivated students and researchers who are excited about statistical learning, AI, and high‑impact applications in health and biomedical science.
- 📘 Strong foundation in math & statistics
- 📝 Familiarity with LaTeX
- 💻 Experience with R and/or Python
- 🧠 Interest in theory and/or real‑world applications
- ⏱️ 10+ hours/week commitment
Principal Investigator
PhD Students
Ruby Chen
Iris Zhang
Aoran Chen
Master’s Students
Kaylen Wei
Sarvar Khamidov
Visiting Students
Martin Ondrus
Alumni
Our alumni have gone on to faculty positions, research appointments in academia and industry, data science leadership roles, and quantitative research careers across technology, healthcare, and public health. Listed below are the first positions held by alumni after completing their time in the lab.
Postdocs
| Photo | Name | Year | First position after the lab |
|---|---|---|---|
 | Ruiyang Wu | 2024 | Assistant Professor, Baruch College |
 | Xinwei Zhang | 2024 | Applied Scientist, Amazon |
PhD Graduates
| Photo | Name | Year | First position after the lab |
|---|---|---|---|
 | Ye Tian | 2025 | Postdoc, Yale University -> Assistant Professor, Penn State University |
 | Alessandro Grande | 2022 | Postdoc, MSK & Columbia University |
 | Sihan Huang | 2020 | Quantitative Researcher, Squarepoint Capital |
 | Kashif Yousuf | 2019 | Senior Data Scientist, Google |
 | Haolei Weng | 2017 | Assistant Professor, Michigan State University |
 | Diego Franco Saldana | 2016 | Data Scientist, Tapad |
Master’s Graduates
| Photo | Name | Year | First position after the lab |
|---|---|---|---|
 | Tun He | 2025 | PhD Student, Department of Biostatistics, University of Florida |
| | Aoran Chen | 2025 | PhD Student, Department of Statistics, Penn State University |
 | Cong Wang | 2023 | PhD Student, Department of Statistics, Texas A&M University |
 | Deron Tsai | 2023 | Software Developer, Pfizer |
 | Fan Bi | 2023 | Statistician, Harvard School of Dental Medicine |
 | Yawen Yuan | 2023 | Biostatistician, Albert Einstein College of Medicine |
 | Jianan Zhu | 2022 | PhD Student, Department of Biostatistics, New York University |
 | Yaojie Wang | 2022 | Statistical Analyst, Northwestern University |
 | Amy Ma | 2021 | PhD Student, Department of Statistics, Ohio State University |
 | Kehang Li | 2021 | PhD Student, Public Health, Chinese University of Hong Kong |
 | Shizhan Gong | 2020 | Machine Learning Engineer, Tencent |
 | Summer Yumeng Yang | 2021 | PhD Student, Biomedical Informatics, UTHealth Houston |