Summary of Research
I am an incoming PhD student at MIT's EECS department. I will research the application of data driven methods, such as machine learning, to the improvement of large-scale power systems. In the fight against climate change, one of the most important areas to de-carbonize is electricity production. De-carbonizing sectors such as heating and transportation will involve their electrification, but without a clean source of electricity, this de-carbonization is not complete. Renewable energy provides a path towards clean electricity, but important technologies in this realm such as wind and solar energy do not supply a constant and controllable source of energy. To compensate for this, power systems operators will need access to new levels of computation to guarantee a reliable supply of electricity. My research will focus on using data driven methods that allow for the safe introduction of large intakes of renewable energy. Furthermore, I also want research how to make the grid more resilient to natural disasters and instability. I hope to achieve this through the creation of new algorithms that either provide mathematical guarantees of the grid's stability or that use relevant data to predict black-outs.
Publications
16. A. K. Rivera, A. Bhagavathula, A. Carbonero, and P. L. Donti. "PFDelta: A Benchmark Dataset for Power Flow with Load, Generator, and Topology Variations." In: Tackling Climate Change with Machine Learning Workshop, ICLR 2025.
15. A. Carbonero, S. Mao, and M. Mehana. “Deep Learning for Subsurface Flow: A comparative Study of U-Net, Fourier Neural Operators, and Transformers in Underground Hydrogen Storage". In: J. Geophys. Research: Machine Learning Comp.
14. A. Carbonero, H. Koerts, B. Moore, and S. Spirkl (2025). “On heroes in digraphs with forbidden induced forests". In: European J. of Comb.
13. A. Carbonero, S. Mao, and M. Mehana (2024). “Enabling Clean Energy Resilience with Machine Learning-Empowered Underground Hydrogen Storage". In: Tackling Climate Change with Machine Learning Workshop, ICLR 2024.
12. A. Carbonero, and D. Obata (2024). “The Integer-Magic Spectra of Trees". In: J. Comb. Math. Comb. Comp.
11. A. Carbonero, V. Schmidt, S. Miret, A. Hernandez-Garcia, Y. Bengio, and D. Rolnick (2023). “On the importance of relative 3D information in the prediction of catalyst-adsorbate relaxed energy with disconnected GNN". In: AI4MAT Workshop, NeurIPS 2023.
10. A. Carbonero, B. Castellano, G. Gordon, C. Kulick, B. Ohlinger, and K. Schmitz (2023). “Permutations of point sets in Rd". In: Australas. J. Comb.
9. A. Carbonero (2023). “On coloring digraphs with forbidden induced subgraphs". MA thesis. University of Waterloo.
8. J. Brooks, A. Carbonero, J. Vargas, R. Florez, B. Rooney, and D. Narayan (2023). “Fixing Numbers of Point-Block Incidence Graphs". In: MDPI Mathematics.
7. A. Carbonero, P. Hompe, B. Moore, and S. Spirkl (2023). “A counterexample to a conjecture about triangle-free induced subgraphs of graphs with large chromatic number". In: J. Comb. Theory, Ser. B.
6. A. Carbonero, W. Fletcher, J. Guo, A. Gyarfas, R. Wang, and S. Yan (2022). “Crowns in linear 3-graphs of Minimum Degree 4". In: Electron. J. Comb.
5. A. Carbonero, P. Hompe, B. Moore, and S. Spirkl (2022). “Digraphs with all induced directed cycles of the same length are not dichi-bounded". In: Electron. J. Comb.
4. A. Carbonero (2022). “On sequences of Pn-line graphs". In: Australas. J. Comb.
3. A. Carbonero, J. Domantay, and K. Guthrie (2022). “The Optimization of Signed Trees". In: Australas. J. Comb.
2. A. Carbonero, W. Fletcher, J. Guo, A. Gyarfas, R. Wang, and S. Yan (2022). “Crowns in linear 3-graphs". In: Electron. J. Comb.
1. J. Brooks, A. Carbonero, J. Vargas, R. Florez, B. Rooney, and D. Narayan (2021). “Removing symmetry in circulant graphs and point-block incidence graphs". In: MDPI Mathematics.
Awards
- Barry Goldwater Scholar, 2020
- Hispanic Society Foundation Scholarship, 2020
- Phi Kappa Phi Graduate Fellowship, 2021
- Outstanding Graduate (one of seven) at the UNLV Spring graduation ceremony, 2021
- Outstanding Student Service Award, Honors College at UNLV, 2021
- Summa Cum Laude graduation distinction at UNLV, 2021
- EM-MSIPP Postmasters Fellowship at Los Alamos National Lab, 2023