Professors and Researchers

Page Content

Expertises

Statistical Machine Learning Uncertainty Quantification Big Data Problems Bayesian Non-Parametrics, Uncertainty Quantification, Operations Research, Artificial Intelligence

Personal links

ResearchGate / SSRN Personal website

Download CV

Xuefei LU

Assistant Professor

Academy: Digitalization

Research centre: SKEMA Centre for Analytics and Management Science

Location: PARIS

email: xuefei.lu@skema.edu

Biography

Publications

Career

Xuefei Lu is an assistant professor at SKEMA Business School.

Xuefei received her PhD in Statistics in 2019 at Bocconi University, Italy. Her thesis title is Sensitivity Analysis and Machine Learning for Computationally Challenging Computer Codes. She was a post-doc research fellow in the Department of Energy, Politecnico di Milano, Italy.

Xuefei was a Lecturer in Predictive Analytics at the University of Edinburgh Business School, Scotland, from 2020 to 2021.

Her research interests include machine learning, uncertainty quantification and operations research problems.

Peer-reviewed journal articles

FLOREALE, G., BARALDI, P., LU, X., ROSSETTI, P. and ZIO, E. (2024). Sensitivity Analysis by Differential Importance Measure for Unsupervised Fault Diagnostics. Reliability Engineering and System Safety, 243, pp. 109846.

LU, X. and BORGONOVO, E. (2023). Global Sensitivity Analysis in Epidemiological Modeling. European Journal of Operational Research, 304(1), pp. 9-24.

HAZEN, G., BORGONOVO, E. and LU, X. (2023). Information Density in Decision Analysis. Decision Analysis, 20(2), pp. 85-185-C2.

LU, X., BORGONOVO, E. and RABITTI, G. (2023). Sensitivity Analysis of Pandemic Models Can Support Effective Policy Decisions. Journal of Computational and Graphical Statistics, 32(3), pp. 767-768.

LU, X. and CALABRESE, R. (2023). The Cohort Shapley value to measure fairness in financing small and medium enterprises in the UK. Finance Research Letters, 58(Part C), pp. 104542.

LU, X., XU, M., BARALDI, P. and ZIO, E. (2022). Generative Adversarial Networks With AdaBoost Ensemble Learning for Anomaly Detection in High-Speed Train Automatic Doors. IEEE Transactions on Intelligent Transportation Systems, 23(12), pp. 23408-23421.

CERVI, E., LU, X., CAMMI, A., DI MAIO, F. and ZIO, E. (2022). Sensitivity-Analysis-Driven Surrogate Model for Molten Salt Reactors Control. Journal of Nuclear Engineering, 3(4), pp. 277 - 294.

LU, X., BARALDI, P. and ZIO, E. (2020). A Data-Driven Framework for Identifying Important Components in Complex Systems. Reliability Engineering and System Safety, 204(107197), pp. 107197.

LU, X., RUDI, A., BORGONOVO, E. and ROSASCO, L. (2020). Faster Kriging: Facing High-Dimensional Simulators. Operations Research, 68(1), pp. 233-249.

ANTONIANO-VILLABOS, I., BORGONOVO, E. and LU, X. (2020). Nonparametric estimation of probabilistic sensitivity measures. Statistics and Computing, 30, pp. 447-467.

BORGONOVO, E., LU, X., PLISCHKE, E. and RAKOVEC, O. (2017). Making the Most Out of a Hydrological Model Data Set: Sensitivity Analyses to Open the Model Black-Box. Water Resources Research, 53(9), pp. 7933-7950.

Conference proceedings

LU, X., ANTONELLO, F., BARALDI, P. and ZIO, E. (2019). Data-Driven Identification of Critical Components in Complex Technical Infrastructures Using Bayesian Additive Regression Trees., Proceedings of the 29th European Safety and Reliability Conference, pp. 1-5.

Conference presentations

LU, X. and BORGONOVO, E. (2024). Unveiling the Path to Desired Predictions: An Interpretability Approach for Black-Box Models. In: 2024 SIAM Conference on Uncertainty Quantification. Trieste.

LU, X. and BORGONOVO, E. (2023). What Can a Person Change to Obtain a Desired Prediction? An Interpretability Approach. In: 2023 INFORMS Annual Meeting. Phoenix.

LU, X., BORGONOVO, E. and HAZEN, G. (2021). Information Density in Simulation Experiments. In: INFORMS Annual Meeting. Anaheim.

LU, X. (2019). Data-Driven Identification of Critical Components in Complex Technical Infrastructures Using Bayesian Additive Regression Trees. In: The annual European Safety and Reliability Conference (ESREL). Hannover.

Faculty research seminar presentations

LU, X. (2024). Unveiling the Path to Desired Predictions: An Explainable Approach for Black-Box Models. In: Statistics Seminars at Department of Decision Sciences, Bocconi University. Milan.

Education

2019 Ph.D. in Statistics, Bocconi University, Italy

2013 MSc in Analytics: Operational Research and Risk Analysis, The University of Manchester, Great Britain

Experience

Full-time academic positions

Since 2021 Assistant Professor, SKEMA Business School, France

2020 - 2021 Assistant Professor, Management Science and Business Economics Group, University of Edinburgh Business School, Great Britain

2018 - 2020 Postdoctoral researcher, Politecnico di Milano, Italy

Research grants, Awards and Honors

Awards and Honors

2024 Clemen-Kleinmuntz Decision Analysis Best Paper Award, The Institute for Operations Research and the Management Sciences (INFORMS)The Institute for Operations Research and the Management Sciences (INFORMS), United States of America

FACULTY AND RESEARCH

Professors and Researchers

SKEMA Business School

Global BBA, ESDHEM, Master in Management (PGE), Mastère Spécialisé®, Masters of Science, Executive Education, Executive MBA, Doctorate, PhD/DBA…