About Me

I am an assistant professor of the Dr. Bing Zhang Department of Statistics, University of Kentucky, Lexington. My research interests include machine learning, privacy-aware decentralized learning, deep learning, statistical inference, model selection and diagnostics.


J. Zhang, J. Ding, Y. Yang, Target cross-validation

Bernoulli 29 (1) (2023), 377--402, link

J. Zhang, J. Ding, Y. Yang, Is a classification procedure good enough? a goodness-of-fit assessment tool for classification learning

Journal of the American Statistical Association 118.542 (2023): 1115-1125, link

X. Wang, J. Zhang, M. Hong, Y. Yang, J. Ding, Parallel assisted learning

IEEE Transactions on Signal Processing 70 (2022), 5848--5858, link

J. Zhang, Y. Yang, J. Ding, Information criteria for model selection

Wiley Interdisciplinary Reviews: Computational Statistics (2023): e1607, link

E. Diao, G. Wang, J. Zhang, Y. Yang, J. Ding, V. Tarokh, Pruning deep neural networks from a sparsity perspective

submitted to ICLR 2023, accepted

C. Chen, J. Zhang (co‐first author), J. Ding, Y. Zhou, Assisted learning with unsupervised domain adaptation

Abstracts of papers IEEE International Symposium on Information Theory. 2023, link

X. Tang, J. Zhang (co-first author), Y. He, X. Zhang, Z. Lin, S. Partarrieu, E. B. Hanna, Z. Ren, Y. Yang, X. Wang, N. Li, J. Ding, J. Liu, Multi-task learning for single-cell multi-modality biology

Nature Communications 14, 2546 (2023), pdf

Manuscript Ready

J. Zhang, J. Ding, Y. Yang, Additive-effect assisted learning

Papers in Preparation

J. Zhang, Y. Yang, J. Ding, DeepDx for assessing classifiers beyond accuracy
J. Zhang, B. Zhao, J. Ding, Y. Yang, A comparison study of published experiments in model selection


Independent Instructor

Introduction to Probability and Statistics (2020)

  • Served as an independent instructor that redesigned the course lecture, created assignments, and developed exams
  • Led a TA and a grader to successfully teach 139 undergraduate students from diverse disciplines
  • Covered basic probability theory, random variables, sampling distributions, and statistical inference
Teaching Assistant

Applied Regression Analysis (2017-2018)

  • Worked as the TA by holding labs and grading homework for two semesters
  • The classes consisted of around 40 students, including undergraduate students from diverse disciplines and master or Ph.D. students from, e.g., computer science and business school
  • Covered estimation, testing, and prediction of regression models


Author and maintainer of the R package BAGofT published in CRAN that implements the methods given in the paper "Is a classification procedure good enough? a goodness‐of‐fit assessment tool for classification learning" paper