Elynn Chen

44 West 4th Street
New York, NY 10012

Modern business runs on messy, high-dimensional, ever-shifting data — and the decisions built on that data are only as good as the statistics underneath them. My research builds those foundations: methods that preserve structure, adapt to heterogeneity, and quantify uncertainty, so that companies and AI systems can make reliable decisions rather than confident guesses.

I am an Assistant Professor of Technology, Operations and Statistics (TOPS) at NYU Stern School of Business. My work spans three connected streams: structured multi-way learning for matrix, tensor, and network data; learning and decision-making under heterogeneity — reinforcement learning, bandits, and dynamic pricing across diverse populations and environments; and statistical decision analytics for business and AI systems, from revenue management to generative and agentic AI. This work appears in JASA, JRSS-B, the Annals of Statistics, and Management Science, and at NeurIPS, ICML, ICLR, and AISTATS. It is supported by an NSF DMS Research Award (2024–2027), following an earlier NSF Postdoctoral Fellowship. I serve as an Associate Editor of ACM TKDD and an Area Chair for NeurIPS.

My path here was a winding one: a Ph.D. in Statistics at Rutgers with Rong Chen, a postdoc with Jianqing Fan at Princeton ORFE, time as a research scientist at OpenAI working on latent heterogeneous reinforcement learning for societal applications, and a postdoc with Michael I. Jordan in EECS at UC Berkeley. That mix of theory and practice shapes how I pick problems: rigorous enough to prove something, real enough to matter.

The part of the job I’m proudest of is mentoring. Much of my work is co-authored with students I advise, who have gone on to faculty positions (City University of Hong Kong) and doctoral programs at Stanford GSB, Dartmouth, and Purdue. I’m actively recruiting motivated students — see the Students page for openings and how to apply.

For the most up-to-date list of publications, see my Google Scholar profile.

news

Jul 16, 2026	Presenting on dual-channel tensor neural networks at JSM 2026
Jul 16, 2026	Organizing two sessions at JSM 2026 in Boston
Jul 15, 2026	I will give a short course on Introduction to Reinforcement Learning at the Deep Learning for Science School (DL4Sci), July 20-24, at Lawrence Berkeley National Laboratory. The tutorial materials are available under courses.

selected publications

JASA

Data-Driven Knowledge Transfer in Batch Q-Learning

Elynn Chen, Xi Chen, and Wenbo Jing

Journal of the American Statistical Association, 121(553):276-288, 2026

DOI Bib

@article{chen2026batchq,
  stream = {heterogeneity},
  title = {Data-Driven Knowledge Transfer in Batch Q-Learning},
  author = {Chen, Elynn and Chen, Xi and Jing, Wenbo},
  journal = {Journal of the American Statistical Association, 121(553):276-288},
  year = {2026},
  doi = {10.1080/01621459.2025.2603731}
}

Mgmt. Sci.

Dynamic Contextual Pricing with Doubly Non-Parametric Random Utility Models

Elynn Chen, Xi Chen, Gao Lan, and Jiayu Li

Management Science, 2026

arXiv Bib

@article{chen2026pricing,
  stream = {decision},
  title = {Dynamic Contextual Pricing with Doubly Non-Parametric Random Utility Models},
  author = {Chen, Elynn and Chen, Xi and Lan, Gao and Li, Jiayu},
  journal = {Management Science},
  year = {2026},
}

ICLR

Seeing Through the Brain: New Insights from Decoding Visual Stimuli with fMRI

Zheng Huang, Enpei Zhang, Yinghao Cai, Weikang Qiu, Carl Yang, Elynn Chen, Rex Ying, Xiang Zhang, Dawei Zhou, and Yujun Yan

In International Conference on Learning Representations (ICLR, Oral Presentation, Top 1%), 2026

arXiv Bib

@inproceedings{huang2026brain,
  stream = {decision},
  title = {Seeing Through the Brain: New Insights from Decoding Visual Stimuli with fMRI},
  author = {Huang, Zheng and Zhang, Enpei and Cai, Yinghao and Qiu, Weikang and Yang, Carl and Chen, Elynn and Ying, Rex and Zhang, Xiang and Zhou, Dawei and Yan, Yujun},
  booktitle = {International Conference on Learning Representations (ICLR, Oral Presentation, Top 1%)},
  year = {2026},
}

JASA

Distributed Tensor Principal Component Analysis with Data Heterogeneity

Elynn Chen, Xi Chen, Wenbo Jing, and Yichen Zhang

Journal of the American Statistical Association, 2025

DOI Bib

@article{chen2025distributedtpca,
  stream = {multiway},
  title = {Distributed Tensor Principal Component Analysis with Data Heterogeneity},
  author = {Chen, Elynn and Chen, Xi and Jing, Wenbo and Zhang, Yichen},
  journal = {Journal of the American Statistical Association},
  year = {2025},
  doi = {10.1080/01621459.2025.2483481}
}

JASA

Factor Augmented Matrix Regression

Elynn Chen, Jianqing Fan, and Xiaonan Zhu

Journal of the American Statistical Association, 2025

DOI Bib

@article{chen2025famr,
  stream = {multiway},
  title = {Factor Augmented Matrix Regression},
  author = {Chen, Elynn and Fan, Jianqing and Zhu, Xiaonan},
  journal = {Journal of the American Statistical Association},
  year = {2025},
  doi = {10.1080/01621459.2025.2595734}
}

Ann. Stat.

Deep Transfer Q-Learning for Offline Non-Stationary Reinforcement Learning

Jinhang Chai, Elynn Chen, and Jianqing Fan

Revised and Resubmitted, Annals of Statistics, 2025

arXiv Bib

@article{chai2025deeptransfer,
  stream = {heterogeneity},
  title = {Deep Transfer Q-Learning for Offline Non-Stationary Reinforcement Learning},
  author = {Chai, Jinhang and Chen, Elynn and Fan, Jianqing},
  journal = {Revised and Resubmitted, Annals of Statistics},
  year = {2025},
}

NeurIPS

Transfer Faster, Price Smarter: Minimax Dynamic Pricing under Cross-Market Preference Shift

Yi Zhang, Elynn Chen, and Yujun Yan

In Advances in Neural Information Processing Systems (NeurIPS, Spotlight, Top 2%), 2025

arXiv Bib

@inproceedings{zhang2025transferpricing,
  stream = {heterogeneity},
  title = {Transfer Faster, Price Smarter: Minimax Dynamic Pricing under Cross-Market Preference Shift},
  author = {Zhang, Yi and Chen, Elynn and Yan, Yujun},
  booktitle = {Advances in Neural Information Processing Systems (NeurIPS, Spotlight, Top 2%)},
  year = {2025},
}

JRSS-B

Semiparametric Tensor Factor Analysis by Iteratively Projected SVD

Elynn Chen, Dong Xia, Chencheng Cai, and Jianqing Fan

Journal of the Royal Statistical Society: Series B, 2024

DOI Bib

@article{chen2024stefa,
  stream = {multiway},
  title = {Semiparametric Tensor Factor Analysis by Iteratively Projected SVD},
  author = {Chen, Elynn and Xia, Dong and Cai, Chencheng and Fan, Jianqing},
  journal = {Journal of the Royal Statistical Society: Series B},
  year = {2024},
  doi = {10.1093/jrsssb/qkae001}
}

JASA

Reinforcement Learning in Latent Heterogeneous Environments

Elynn Chen, Rui Song, and Michael I. Jordan

Journal of the American Statistical Association, 119(548):3113-3126, 2024

DOI Bib

@article{chen2024heterorl,
  stream = {heterogeneity},
  title = {Reinforcement Learning in Latent Heterogeneous Environments},
  author = {Chen, Elynn and Song, Rui and Jordan, Michael I.},
  journal = {Journal of the American Statistical Association, 119(548):3113-3126},
  year = {2024},
  doi = {10.1080/01621459.2024.2308317}
}