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Professional Background

Since 2020, Giselle has been a Scientist at Lawrence Livermore National Laboratory. Her expertise encompasses machine learning and uncertainty quantification for hazardous material flow, fusion energy design optimization, and the high-pressure behavior of highly porous metals. Appointed in 2023, she continues to serve as Deputy Director of the Data Science Institute Consulting Services at LLNL.

Giselle's academic journey began with a Bachelor's degree in Nuclear Engineering from the Balseiro Institute, Argentina, in 2014, where her thesis concentrated on the surveillance program for the Argentinian reactor CAREM 25. She then earned her Master's in Mechanical Engineering and Ph.D. in Aerospace Engineering, both summa cum laude, from the University of Florida in 2016 and 2018, respectively. Her Ph.D. thesis, under the guidance of Drs. Raphael T. Haftka and S. Balachandar, focused on particle behavior in multiphase cylindrical detonations. In 2019, as a postdoctoral research associate at Los Alamos National Laboratory, She specialized in machine learning applications for fracture mechanics and inertial confinement fusion physics. She completed her M.B.A. at the University of Arizona Global Campus in 2024.

Research Interests

Giselle's research focuses on machine learning, verification, validation, and uncertainty quantification, especially in multi-fidelity models, optimization, and multidisciplinary physics. Her involvement extends to large-scale simulations in material science, atmospheric science, and inertial fusion energy, aiming to integrate theoretical models with practical applications across various scientific domains.

M.B.A., University of Arizona Global Campus, Forbes School of Business and Technology, Tucson, Arizona, 2024

Ph.D., Aerospace Engineering, University of Florida, Gainesville, Florida, 2018

M.S., Mechanical Engineering, University of Florida, Gainesville, Florida, 2016

B.S., Nuclear Engineering, Instituto Balseiro, San Carlos de Bariloche, Río Negro, Argentina, 2014

M. G. Fernández-Godino. (2023) "Review of multi-fidelity models". Advances in Computational Science and Engineering, 2023, 1(4): 351-400. DOI: 10.3934/acse.2023015

M. G. Fernández-Godino, D. D. Lucas, and Q. Kong. (2023). "Predicting wind-driven spatial deposition through simulated color images using deep autoencoders". Nature Scientific Reports, 13(1), 1394. DOI: 10.1038/s41598-023-28590-4.

M. G. Fernández-Godino, N. Panda, D. O’Malley, K. Larkin, A. Hunter, R. T. Haftka, and G. Srinivasan, “Accelerating continuum-scale brittle fracture simulations with machine learning”. Computational Materials Science, Elsevier, Volume 186, January 2021, p. 109959. DOI: 10.1016/j.commatsci.2020.109959.

M. G. Fernández-Godino, S. Dubreuil, N. Bartoli, C. Gogu, S. Balachandar, and R. T. Haftka, “Linear Regression Based Multi-fidelity Surrogate for Disturbance Amplification in Multi-phase Explosion". Structural and Multidisciplinary Optimization 60.6 (2019): 2205-2220. DOI: 10.1007/s00158-019-02387-4.

M. G. Fernández-Godino, F. Ouellet, S. Balachandar, R. T. Haftka, “Evolution of Initial Modal Particle Fraction Perturbations in Cylindrical Multiphase Detonations". ASME. Journal of Fluids Engineering. Vol. 141, Issue 9, 09130201-09130220, September 2019. DOI: 10.1115/1.4043055.

M. G. Fernández-Godino, S. Balachandar, R. T. Haftka, “On the Use of Symmetries in Building Surrogate Models”. ASME Journal of Mechanical Design. Vol. 141, Issue 6, 06140201-06140214, June 2019. DOI: 10.1115/1.4042047.

M. G. Fernández-Godino, C. Park, N. H. Kim, and R. T. Haftka, “Issues in Deciding whether to Use Multi-fidelity Models”. AIAA Journal, Vol 57, No. 5, 2039-2054, May 2019. DOI: 10.2514/1.J057750.

M. G. Fernández-Godino, C. Park, N. H. Kim, and R. T. Haftka, “Review of Multi-fidelity Models”. arXiv preprint arXiv:1609.07196. September, 2016.

• Award for Advancing Machine Learning Initiatives Across the Laboratory, 2023
• SIAM Computational Science and Engineering Early Career Travel Award, 2023
• Rising Star in Computational and Data Sciences, 2020
• Predictive Science Academic Alliance Program Scholarship, 2014–2018
• Instituto Balseiro Scholarship, 2011–2014