1. L. Huwang and Ying Hung (2007). Effect of Measurement Error on Monitoring Multivariate Process Variability, Statistica Sinica, 17, 749-760.

2. V. R. Joseph and Ying Hung (2008). Orthogonal-Maximin Latin Hypercube Designs, Statistica Sinica, 18, 171-186.

3. V. R. Joseph, Ying Hung, and A. Sudjianto (2008). Blind Kriging: A New Method for Developing Metamodels, ASME (American Society of Mechanical Engineers) Journal of Mechanical Design, 130, 031102-1-8.

4. W. Tan, I. C. Ume, Ying Hung, and C. F. J. Wu (2008). Effects of Warpage on Fatigue Reliability of Solde Bumps: Experimental and Analytical Studies, Proceedings of the 58th Electronic Components and Technology Conference, 131-138.

5. Ying Hung, V. Zarnitsyna, Y. Zhang, C. Zhu, and C. F. J. Wu (2008). Binary Time Series Modeling with Application to Adhesion Frequency Experiments, Journal of the American Statistical Association, 103, 1248-1259.

6. Ying Hung, V. R. Joseph, and S. N. Melkote (2009). Design and Analysis of Computer Experiments with Branching and Nested Factors, Technometrics, 51, 354-365.

7. Ying Hung, Y. Amemiya, and C. F. Jeff Wu (2010). Probability-Based Latin Hypercube Design, Biometrika, 97, 961-968.

8. W. Tan, I. C. Ume, Ying Hung, and C. F. J. Wu (2010). Effects of Warpage on Fatigue Reliability of Solder Bumps: Experimental and Analytical Studies, IEEE Transactions on Advanced Packaging, 33, 314-322.

9. Ying Hung (2011). Adaptive Probability-based Latin Hypercube Designs, Journal of the American Statistical Association, 106, 213-219.

10. Ying Hung (2011). Penalized Blind Kriging in Computer Experiments, Statistica Sinica, 21, 1171-1190.

11. K. Wang, C. Zhang, J. Su, B. Wang, and Ying Hung (2013). Optimization of Composite Manufacturing Processes with Computer Experiments and Kriging Methods, International Journal of Computer Integrated Manufacturing, 26, 216-226.

12. Ying Hung (2012). Order Selection in Nonlinear Time Series Models with Application to the Study of Cell Memory, Annals of Applied Statistics, 6, 1256-1279.

13. Ying Hung (2012). Optimal Experiment Design: Latin Hypercube, Encyclopedia of Systems Biology, Springer.

14. R.-B. Chen, D.-N. Hsieh, Ying Hung, and W. Wang (2013). Optimizing Latin Hypercube Designs by Particle Swarm, Statistics and Computing, 23, 663-676.

15. L. Ju, Y. Wang, Ying Hung, C. F. J. Wu, and C. Zhu (2013). An HMM-Based Algorithm for Evaluating Rates of Receptor-Ligand Binding Kinetics from Thermal Fluctuation Data, Bioinformatics, 29, 1511-1518.

16. Ying Hung, and V. R. Joseph (2014). Discussion of “Three-Phase Optimal Design of Sensitivity Experiments,” Journal of Statistical Planning and Inferences, 149, 16-19.

17. Ying Hung (2014). Sequential Probability-based Latin Hypercube Designs without Replacement, Statistica Sinica, 24, 985-1000.

18. Ying Hung, Y. Wang, V. Zarnitsyna, C. Zhu, and C. F. J. Wu (2013). Hidden Markov Models with Applications in Cell Adhesion Experiments, Journal of the American Statistical Association, 108, 1469-1479.

19. R.-B. Chen, Y.-W. Hsu, Ying Hung, and W. Wang (2014). Central Composite Discrepancy-Based Uniform Designs for Irregular Experimental Regions, Computational Statistics and Data Analysis, 282-297.

20. Ying Hung, V. R. Joseph, and S. N. Melkote (2015). Analysis of Computer Experiments with Functional Response, Technometrics, 57, 35-44.

21. X. Deng, Ying Hung, and C. D. Lin (2015). Design for Computer Experiments with Qualitative and Quantitative Factors, Statistica Sinica, 1567-1581.

22. T. Park, B. Yum, Ying Hung, Y.-S. Jeong, and M. K. Jeong (2016). Robust Kriging Models in Computer Experiments, Journal of Operational Research Society, 67, 644-653.

23. Y. Zhao^§, Y. Amemiya, and Ying Hung (2018). Efficient Gaussian Process Modeling using Experimental Design-based Subagging, Statistica Sinica, 28, 1459-1479.

24. C.-C. Lin and Ying Hung (2018). A Prior-Less Method for Multi-Face Tracking in Unconstrained Videos, Conference on Computer Vision and Pattern Recognition (CVPR) 2018.

25. R.-B. Chen, C.-H. Li, Ying Hung, W. Wang (2019). Optimal Non-collapsing Space-filling Designs for Irregular Experimental Regions, the Journal of Computational and Graphical Statistics, 28, 74-91.

26. M. Stein and Ying Hung (2019). Discussion of “Probabilistic Integration: A Role in Statistical Computation?” Statistical Science, 34, 34-37.

27. C.-L. Sung*, Ying Hung*, W. Rittase, C. Zhu, and C. F. J. Wu (2020). A Generalized Gaussian Process Model for Computer Experiments with Binary Time Series, the Journal of American Statistical Association, 115, 945-956.

28. C. Li, Ying Hung, and M. Xie (2020). A Sequential Split-Conquer-Combine Approach for Gaussian Process Modeling in Computer Experiments, the Canadian Journal of Statistics, 48, 712-730.

29. C.-L. Sung*, Ying Hung*, W. Rittase, C. Zhu, and C. F. J. Wu (2020). Calibration for Computer Experiments with Binary Responses, the Journal of American Statistical Association, 115, 1664-1674.

30. Ying Hung, L.-H. Lin, and C. F. J. Wu (2019). Varying Coefficient Frailty Models with Applications in Single Molecular Experiments, Biometrics, to appear.

31. L. He^§ and Ying Hung (2020). Gaussian Process Prediction using Experimental Design-based Subagging, Statistica Sinica, to appear.

32. C-C. Lin, Ying Hung, R. Feris, L. He^§ (2020). Video Instance Segmentation Tracking With a Modified VAE Architecture, Conference on Computer Vision and Pattern Recognition (CVPR) 2020.

33. M. Y. Sengul, Y. Song, N. Nayir, Y. Gao, Ying Hung, T. Dasgupta, and A. C. T. van Duin (2021). INDEEDopt: a Deep Learning-based ReaxFF Parameterization framework, npj Computational Materials, 7, 68.

34. C.-L. Sung and Ying Hung (2020). Efficient Calibration for Imperfect Epidemic Models with Applications to the Analysis of COVID-19, submitted.

35. Y. Song^§, M. Y. Sengul, L. He^§, A. C. T. van Duin, Ying Hung, and T. Dasgupta (2020). CLAIMED: A CLAssification-Incorporated Minimum Energy Design to Explore a Multivariate Response Surface with Feasibility constraints, IEEE Transactions on Automation Science and Engineering, to appear.

36. F. Zhang, R.-B. Chen, Ying Hung, and X. Deng (2020). Bayesian Indicator Selection Approach for the Gaussian Process Models in Computer Experiments, submitted.

37. Y. Hung, L.-H. Lin, and C. F. J. Wu (2021). Optimal Simulator Selection, the Journal of American Statistical Association, to appear.

38. Ying Hung and L. Wang^§ (2021). Optimal Crossover Designs for Quantitative Variables, under review.

In Preparation

39. Y. Zhao^§ and Ying Hung (2019). Autoregressive Markov Switching Mixed Model with Applications in Cell Biology, to be submitted.

40. L. Wang^§ and Ying Hung (2019). Quantile Regression for Computer Experiments, to be submitted.

*: joint first authors. §: Ph.D. students.