Bio

Mateus Gheorghe de Castro Ribeiro is a PhD candidate in the Stanford Sustainable Systems Lab. He has worked on various topics at the intersection of engineering applications and artificial intelligence (AI). His main area of research focuses on AI applied to sustainable energy systems, specifically using data-driven methods to accelerate the electrification of bus fleets, ensure reliable operations with minimal costs, and achieve 24/7 carbon-free operations. Mateus obtained his bachelor's and master's degrees in mechanical engineering from the Federal University of Juiz de Fora and the Pontifical Catholic University of Rio de Janeiro, respectively. In 2022, he was awarded the CAPES/Fulbright Scholarship to pursue his PhD in the Department of Civil and Environmental Engineering at Stanford University.

Contact

  • Academic
    mateusgh@stanford.edu
    University - Student Department: Civil and Environmental Engineering Position: Graduate

Additional Info

Links

Lab Affiliations

All Publications

  • Optimal coordination of electric buses and battery storage for achieving a 24/7 carbon-free electrified fleet Applied Energy Luke, J., Ribeiro, M., Martin, S., Balogun, E., Cezar, G. V., Pavone, M., Rajagopal, R. 2025; 377

    View details for DOI 10.1016/j.apenergy.2024.124506

  • Machine learning-based evaluation of eccentricity and acoustic impedance in oil well using VDL data GEOENERGY SCIENCE AND ENGINEERING Ribeiro, M., Ferreira, G., Parente, L., Batista, J., Kubrusly, A., Ayala, H., Braga, A. 2023; 231

    View details for DOI 10.1016/j.geoen.2023.212288

    View details for Web of Science ID 001080911600001

  • Machine Learning-Based Corrosion-Like Defect Estimation With Shear-Horizontal Guided Waves Improved by Mode Separation IEEE ACCESS de Castro Ribeiro, M., Kubrusly, A., Ayala, H., Dixon, S. 2021; 9: 40836-40849

    View details for DOI 10.1109/ACCESS.2021.3063736

    View details for Web of Science ID 000631194400001

  • Supervised Machine Learning Models for Mechanical Properties Prediction in Additively Manufactured Composites APPLIED SCIENCES-BASEL Prada Parra, D., Ferreira, G., Diaz, J. G., Ribeiro, M., Braga, A. 2024; 14 (16)

    View details for DOI 10.3390/app14167009

    View details for Web of Science ID 001305118600001

  • Modeling and predicting the backstroke to breaststroke turns performance in age-group swimmers. Sports biomechanics Chainok, P., de Jesus, K., Coelho, L., Ayala, H. V., de Castro Ribeiro, M. G., Fernandes, R. J., Vilas-Boas, J. P. 2023; 22 (12): 1700-1721

    Abstract

    The purpose of the present study was to identify the performance determinant factors predicting 15-m backstroke-to-breaststroke turning performance using and comparing linear and tree-based machine-learning models. The temporal, kinematic, kinetic and hydrodynamic variables were collected from 18 age-group swimmers (12.08 ± 0.17 yrs) using 23 Qualisys cameras, two tri-axial underwater force plates and inverse dynamics approach. The best models were obtained: (i) with Lasso linear model of the leave-one-out cross-validation in open turn (MSE = 0.011; R2 = 0.825) and in the somersault turn (MSE = 0.016; R2 = 0.734); (ii) the Ridge of the leave-one-out cross-validation (MSE = 0.016; R2 = 0.763) for the bucket turn; and (iii) the AdaBoost tree-based model of the leave-one-out cross-validation for the crossover turn (MSE = 0.016; R2 = 0.644). Model's selected features revealed that optimum turning performance was very similarly determined for the different techniques, with balanced contributions between turn-in and turn-out variables. As a result, the relevant feature's contribution of each backstroke-to-breaststroke turning technique are specific; developing approaching speed in conjunction with proper gliding posture and pull-out strategy will result in improved turning performance, and may influence differently the development of specific training intervention programmes.

    View details for DOI 10.1080/14763141.2021.2005127

    View details for PubMedID 34907864

  • Machine learning-based cement integrity evaluation with a through-tubing logging experimental setup GEOENERGY SCIENCE AND ENGINEERING de Souza, L., Ferreira, G., Camerini, I., Correia, T., Ribeiro, M., Hidalgo, J., Joao, B., Llerena, R., Kubrusly, A., Ayala, H., Braga, A., Batista, J. 2023; 227

    View details for DOI 10.1016/j.geoen.2023.211882

    View details for Web of Science ID 001053395000001

  • Improved feature extraction of guided wave signals for defect detection in welded thermoplastic composite joints MEASUREMENT Ferreira, G., Ribeiro, M., Kubrusly, A., Ayala, H. 2022; 198

    View details for DOI 10.1016/j.measurement.2022.111372

    View details for Web of Science ID 000817188300001

  • Type-1 and singleton fuzzy logic system binary classifier trained by BFGS optimization method FUZZY OPTIMIZATION AND DECISION MAKING Calderano, P. S., Mateus Gheorghe, d., Teixeira, R. S., Finotti Amaral, R. P., Menezes, I. M. 2023; 22 (1): 149-168

    View details for DOI 10.1007/s10700-022-09387-y

    View details for Web of Science ID 000786045600001

  • Damage Detection in Composite Plates with Ultrasonic Guided-waves and Nonlinear System Identification de Castro Ribeiro, M., Kubrusly, A., Hultmann Ayala, H., IEEE IEEE. 2020: 2039-2046

    View details for DOI 10.1109/ssci47803.2020.9308212

    View details for Web of Science ID 000682772902013

  • An enhanced aircraft engine gas path diagnostic method based on upper and lower singleton type-2 fuzzy logic system JOURNAL OF THE BRAZILIAN SOCIETY OF MECHANICAL SCIENCES AND ENGINEERING Calderano, P. S., Ribeiro, M. C., Amaral, R. F., Vellasco, M. R., Tanscheit, R., de Aguiar, E. P. 2019; 41 (2)

    View details for DOI 10.1007/s40430-019-1567-4

    View details for Web of Science ID 000456889700001

https://orcid.org/0000-0002-9600-5759