Professors

Xuefei LU

Assistant Professor

Statistical Machine Learning Uncertainty Quantification Big Data Problems Bayesian Non-Parametrics, Uncertainty Quantification, Recherche opérationnel, Intelligence artificielle

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Xuefei LU

Academy

Digitalization

Research center

SKEMA Centre for Analytics and Management Science

Location

Paris

Discipline

Analytics Information & Operations

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.

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
European Journal of Operational Research 2024 Editors’ awards for excellence in reviewing, European Journal of Operational Research (EJOR)
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
2023
European Journal of Operational Research 2023 Editors’ awards for excellence in reviewing, European Journal of Operational Research (EJOR)
2020
European Journal of Operational Research 2020 Editors’ awards for excellence in reviewingEuropean Journal of Operational Research 2020 Editors’ awards for excellence in reviewing, European Journal of Operational Research (EJOR)
PhD Supervision
2023
M. BUDZINSKI, Bocconi University, PhD thesis, Thesis Reviewer
Z. OUYANG, University of Edinburgh Business School, PhD thesis, Thesis co-director

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 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., 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.
HAZEN, G., BORGONOVO, E. and LU, X. (2023). Information Density in Decision Analysis. Decision Analysis, 20(2), pp. 85-185-C2.
LU, X. and BORGONOVO, E. (2023). Global Sensitivity Analysis in Epidemiological Modeling. European Journal of Operational Research, 304(1), pp. 9-24.
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.