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.
Publications
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
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), link
E. Diao, G. Wang, J. Zhang, Y. Yang, J. Ding, V. Tarokh, Pruning deep neural networks from a sparsity perspective
International Conference on Learning Representations (2023), link
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
Papers Submitted
J. Zhang, J. Ding, Y. Yang, Additive-effect assisted learning
submitted to Journal of the American Statistical Association, under revision.
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
Teaching
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