Laura Selicato

Laura Selicato is a Research Fellow at the National Research Council (CNR), Water Research Institute (IRSA), and an expert on the subject "Metodi di Ottimizzazione per la Data Science e l'Intelligenza Artificiale" at the University of Bari Aldo Moro.

Laura's research interests focus on optimization methods and data science. She has worked in the MIDAS group since 2019 on different topics, ranging from dimensionality reduction techniques and matrix decompositions to optimization with the study of hyperparameter tuning in machine learning problems. MIDAS's main goal is to understand the mathematics behind data, in different application domains such as bioinformatics and environmental fields.

Recently, She has been working on decision support in the environmental context, through a multi-objective optimization methodology to support the identification of policy alternatives able to reduce trade-offs between competing objectives.

Academic e-mail: lauraselicato at cnr dot it, laura.selicato at uniba dot it

Journals

• Penalizing Low-Rank Matrix Factorization: From theoretical connections to practical applications. Del Buono N., Esposito F., Selicato L. Journal of Computational Mathematics and Data Science (2025): 100111. 

• Topological data analysis for resilience assessment of water distribution networks. Selicato L., Pagano A., Esposito F., Icardi M. Mathematics and Computers in Simulation 231 (2025): 62-70. 

• Penalty hyperparameter optimization with diversity measure for nonnegative low-rank approximation. Del Buono N., Esposito F., Selicato L., Zdunek R. Applied Numerical Mathematics 208 (2025): 189-204. 

• A targeted gene signature stratifying mediastinal gray zone lymphoma into classical Hodgkin lymphoma-like or primary mediastinal B-cell lymphoma-like subtypes. Gargano, G., Vegliante, M.C., Esposito, F., Pappagallo, S., Sabattini, E., Agostinelli, C., Pileri, S., Tabanelli, V., Ponzoni, M., Lorenzi, L., Selicato L. and others. Haematologica 109.11 (2024): 3771. 

• Effect of preoperative music therapy versus intravenous midazolam on anxiety, sedation and stress in stomatology surgery: a randomized controlled study. Giordano, F., Giglio, M., Sorrentino, I., Dell’Olio, F., Lorusso, P., Massaro, M., Tempesta, A., Limongelli, L., Selicato, L., Favia, G. and others. Journal of Clinical Medicine 12.9 (2023): 3215. 

• Bi-level algorithm for optimizing hyperparameters in penalized nonnegative matrix factorization. Del Buono N., Esposito F., Selicato L., Zdunek R. , Applied Mathematics and Computation 457, 128184, 2023

• A New Ensemble Method for Detecting Anomalies in Gene Expression Matrices, Selicato, L.; Esposito, F.; Gargano, G.; Vegliante, M.C.; Opinto, G.; Zaccaria, G.M.; Ciavarella, S.; Guarini, A.; Del Buono, N.  Mathematics 2021, 9, 882. https://doi.org/10.3390/math9080882

Book Chapter

• Detecting Anomalies in Marine Data: A Framework for Time Series Analysis. N Del Buono, F Esposito, G Gargano, L Selicato, N Taggio, G Ceriola, LOD 2022 (7th International Conference on machine Learning, Optimization and Data science),2022

• Toward a New Approach for Tuning Regularization Hyperparameter in NMF, Del Buono N., Esposito F., Selicato L. In: Nicosia G. et al. (eds) Machine Learning, Optimization, and Data Science. LOD 2021. Lecture Notes in Computer Science, vol 13163. Springer, Cham. https://doi.org/10.1007/978-3-030-95467-3_36

• Methods for Hyperparameters Optimization in Learning Approaches: an overview, N. Del Buono, F. Esposito and L. Selicato, International Conference on Machine Learning, Optimization, and Data Science LOD2020, LNCS, 2020.

Conference Proceedings

• Low rank approaches for the analysis of real data from pre to post processing. Esposito F., Selicato L., Del Buono N., PROCEEDINGS OF SIMAI 2020+ 21 (2021).

• Anomalies detection in gene expression matrices: Towards a new approach. Del Buono N., Esposito F., Selicato L., Vegliante MC. 12th International Conference on Bioinformatics Models, Methods and Algorithms, BIOINFORMATICS 2021-Part of the 14th International Joint Conference on Biomedical Engineering Systems and Technologies, BIOSTEC 2021. SciTePress, 2021.

• Nonnegative Matrix Factorization models for knowledge extraction from biomedical and other real world data, F. Esposito, N. Del Buono, and L. Selicato, PAMM 20.1 (2021): e202000032.

Thesis

• Bi-level optimization for hyperparameters tuning in sparse low-rank learning algorithm. L. Selicato, Department of Computer Science, University of Bari Aldo Moro, Italy PhD thesis, 2023