Publications

Xu, M., Lan, S., and Kang, L. Bayesian Bridge Gaussian Process Regression. arXiv

Li, Y. and Kang, L. Kernel Discrepancy-Based Rerandomization for Controlled Experiments. arXiv

Deng, X., Kang, L., and Lin, C. D. Design of Experiments for Emulations: A Selective Review from a Modeling Perspective. arXiv

Bao, X., Kang, L., Liu, C., and Wang, Y. Accelerating Particle-based Energetic Variational Inference. arXiv

Kang, L. and Xu, M. Optimal Kernel Learning for Gaussian Process Models with High-Dimensional Input. arXiv

Fisher, W., Zhang, Q., Kang, L., and Deng, X. Collaborative Design of Controlled Experiments in the Presence of Subject Covariates. arXiv

Chen, Y., Wang, Y.,  Kang, L., and Liu, C.  A Deterministic Sampling Method via Maximum Mean Discrepancy Flow with Adaptive Kernel. arXiv

Zhang, Q., Kang, L., and Deng, X. (2025+) Collaborative Analysis for Paired A/B Testing Experiments. Statistica Sinica. 37(4). arXiv

La, V.  N. T., Kang, L., and Minh, D. (2025) Enzyme kinetics model for the coronavirus main protease including dimerization and ligand binding. Biophysical Journal. 124(16), 2627 - 2638. bioRxiv

Willow, S. Y., Kang, L., and Minh, D. D. L. (2023) Learned Mappings for Targeted Free Energy Perturbation between Peptide Conformations. Journal of Chemical Physics. 159, 124104.

La, V., Nicholson, S., Haneef, A., Kang, L., and Minh, D. N. (2023) Inclusion of control data in fits to concentration-response curves improves estimates of half-maximal concentrations. Journal of Medicinal Chemistry. 66(18), 12751--12761.

Kang, L., Deng, X., and Jin, R. (2023) Bayesian D-optimal Design of Experiments with Quantitative and Qualitative Responses. The New England Journal of Statistics in Data Science. 1(3), 371–385. arXiv Codes

Lan, S. and Kang, L. (2023) Sampling Constrained Continuous Probability Distributions: A Review. WIREs Computational Statistics. 15(6), e1608. arXiv

Li, Y., Kang, L., Deng, X. (2022) A Maximin Φp-Efficient Design for Multivariate Generalized Linear Models. Statistica Sinica. 32(4), 2047--2069. arXiv

Zhang, Q. and Kang, L. (2022) Locally Optimal Design for A/B Testing in the Presence of Covariates and Network Connection. Technometrics. 64(3), 358--369. arXiv Codes

Kang, X., Kang, L., Chen, W., Deng, X. (2022) A Generative Modeling Approach for Data with Qualitative and Quantitative Responses. Journal of Multivariate Analysis. 190, 104952. arXiv

Anahideh, H., Kang, L., Nezami, N. (2021) Fair and Diverse Allocation of Scarce Resources. Socio-Economic Planning Sciences. 80, 101193. arXiv

Kang, X., Ranganathan, S., Kang, L., Gohlke, J., Deng, X. (2021) Bayesian Quantitative-Qualitative Model via Latent Variable with Application to Birth Records Data. Journal of Statistical Computation and Simulation. 91(16), 3283-3303. arXiv

Wang, Y., Chen, J. Liu, C., and Kang, L. (2021) Particle-Based Energetic Variational Inference. Statistics and Computing. 31, 34. arXiv Codes

Li, Y, Kang, L., and Huang, X. (2021) Covariate Balancing Based on Kernel Density Estimates for Controlled Experiments. Statistical Theory and Related Fields. 5(2), 102-113. arXiv

Chen, J., Kang, L., and Lin, G. (2021) Gaussian Process Assisted Active Learning of Physical Laws. Technometrics. 63(3), 329-342. arXiv  Codes

Shen, S., Kang, L., and Deng, X. (2020) Additive Heredity Model for the Analysis of Mixture-of-Mixtures Experiments. Technometrics, 62(2), 265-276.

Kang, L. and Huang, X. (2019) Bayesian A-optimal Design of Experiment with Quantitative and Qualitative Responses. Journal of Statistical Theory and Practice. 13(4), 64.

Pokhiko, V., Zhang, Q., Kang, L., and Mays, D. P. (2019) D-optimal Design for Network A/B Testing. Journal of Statistical Theory and Practice. 13(4), 61.

Huang, X., Kang, L., Kassa, M., and Hall, C. (2019) Cylinder-Specific Pressure Predictions for Advanced Dual Fuel CI Engines Utilizing a Two-Stage Functional Data Analysis. Journal of Dynamic Systems, Measurement, and Control, 141(5): 051006.

Kang, L. (2018) Stochastic Coordinate-Exchange Optimal Designs with Complex Constraints. Quality Engineering, 31(3), 401-416. 

Kang, L., Kang, X., Deng, X., Jin, R. (2018) Bayesian Hierarchical Models for Quantitative and Qualitative Responses.  Journal of Quality Technology, 50(3), 290-308.

Zhu, J., Kang, L., Anderson, P. (2018) Predicting influent biochemical oxygen demand: Balancing energy demand and risk management. Water Research, 128(1), 304-313.

Kang, L. and Joseph, V. R. (2016) Kernel Approximation: from Regression to Interpolation. SIAM/ASA Journal on Uncertainty Quantification, 4, 112-129.

Lin, C. D. and Kang, L. (2016) A General Construction for Space-filling Latin Hypercubes. Statistica Sinica, 26, 675-690.

Kang, L., Salgado, J. C., and Brenneman, W. A. (2016) Comparing the Slack-Variable Mixture Model with Other Alternatives. Technometrics, 58, 255-268.

Kang, L. and Brenneman, W. A. (2011) Product Defect Rate Confidence Bound with Attribute and Variable Data.Quality and Reliability Engineering International, 27, 353-368.

Joseph, V.R. and Kang, L. (2011) Regression-Based Inverse Distance Weighting with Applications to Computer Experiments. Technometrics, 53, 254-265.

Kang, L., Joseph, V.R., and Brenneman, W. (2011) Design and Modeling Strategies for Mixture-of-Mixtures Experiments. Technometrics, 53, 125-136.

Ai, M., Kang, L., and Joseph, V.R. (2009) Bayesian Optimal Blocking of Factorial Designs. Journal of Statistical Planning and Inference, 139, 3319-3328.

Kang, L. and Joseph, V.R. (2009) Bayesian Optimal Single Arrays for Robust Parameter Design. Technometrics, 51, 250-261. (Winner of the Best Student Paper Award, QSR, INFORMS, 2009.)

Wang, Y., Jin, R., and Kang, L. (2025) Robust Analysis for Resilient AI System. 2025 IEEE International Conference on Data Mining Workshops (ICDMW), Washington, D.C., USA. arXiv

Cheng, Y., Kang, L., Wang, Y., and Liu, C. (2025) Active Learning for Manifold Gaussian Process Regression. 2025 Winter Simulation Conference (WSC), Seattle, WA, USA. arXiv GitHub

Kang, L., Cheng, Y., Wang, Y., and Liu, C. (2024) Energetic Variational Gaussian Process Regression for Computer Experiments. 2024 Winter Simulation Conference (WSC), Orlando, FL, USA, pp. 3542-3553. arXiv

Haghighat, P., G'andara, D., Kang, L., and Anahideh, H. (2024) Fair Multivariate Adaptive Regression Splines for Ensuring Equity and Transparency. Proceedings of the AAAI Conference on Artificial Intelligence, 38(20), 22076-22086.

Li, Y., Kang, L., Hickernell, F. J. (2020) Is a Transformed Low Discrepancy Design Also Low Discrepancy? Book Chapter in Fan J., Pan J. (eds) Contemporary Design of Experiments, Multivariate Analysis and Data Mining--Festschrift in Honor of Professor Kai-Tai Fang. Springer, Cham. arXiv