Lulu Kang

Ph.D. in Industrial Engineering, Georgia Institute of Technology.

M.S. in Operations Research, Georgia Institute of Technology.

B.S. in Mathematics, Nanjing University, China.

• Statistical Design of Experiments: optimal design of experiments; algebraic design construction methods; sequential experimental design; active learning; experimental design for engineering systems; computer experimental design.
• Statistical Learning: advanced machine learning methods; multivariate modeling methods for quantitative and qualitative data; functional data analysis; interpolation modeling methods for deterministic computer experiments.
• Uncertainty Quantification: experimental design and modeling for computer simulation results.
• Bayesian Statistics and Approximate Inference: Bayesian modeling and computational methods; variational inference.
• Optimization: new optimization algorithms in machine learning and design of experiments.
• Application of Data Science in sciences, engineering, healthcare, etc.

For a complete list of Kang's publications, see her Google Scholar page or ORCID page.

• 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(12), 124104. 
• Li, Y., Kang, L., Deng, X. (2022) A Maximin Φp-Efficient Design for Multivariate Generalized Linear Models. Statistica Sinica. 32(4), 2047--2069. 
• 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.
• Wang, Y., Chen, J. Liu, C., and Kang, L. (2021) Particle-Based Energetic Variational Inference. Statistics and Computing. 31, 34.
• Chen, J., Kang, L., and Lin, G. (2021) Gaussian Process Assisted Active Learning of Physical Laws. Technometrics. 63(3), 329-342.