CV

Doctorate student in the Computer Science program working with the Software Systems Research Group at the Institute of Mathematics, Statistics and Computer Science (IME) of the University of São Paulo (USP). Graduated in Molecular Sciences in the same institution. Active in scientific research since 2020, supported by multiple prestigious Brazilian scholarships including FAPESP Technical Training (TT-1), Scientific Initiation (IC), Research Internship Abroad (BEPE), and Doctoral Fellowships (PROEX CAPES/DD FAPESP).

Education

2023–present	Universidade de São Paulo Doctorate in Computer Science São Paulo, SP
2019–2023	Universidade de São Paulo BSc. in Molecular Sciences, emphasis on Computer Science São Paulo, SP

Professional Experience

02/2026–05/2026

Université Grenoble Alpes
Ingénieur d’Études
Developed a simulation environment and a SimGrid plugin to model water consumption and carbon emissions of high-performance computing platforms. This environment was used to evaluate environmental-aware scheduling heuristics aiming to balance environmental impact with system performance.

Publications

Rosa, L. de S., Lima, V. P., Carastan-Santos, D., Da, A. A., Amaris, M., de Camargo, R. Y., & Goldman, A. (2026). The Environmental Impacts of High-Performance Computing: A Systematic Mapping Study (Hal-05601113). https://hal.science/hal-05601113.
Rosa, L., & Goldman, A. (2024). Energy-Aware Scheduling for Serverless Scientific Workflows: A Machine Learning Approach. In Proceedings of the 15th Regional School of High-Performance Computing of São Paulo, (pp. 89-92). Porto Alegre: SBC. https://doi.org/10.5753/eradsp.2024.239934.
Rosa, L., Carastan-Santos, D., Goldman, A. (2023). An Experimental Analysis of Regression-Obtained HPC Scheduling Heuristics. In: Klusáček, D., Corbalán, J., Rodrigo, G.P. (eds) Job Scheduling Strategies for Parallel Processing. JSSPP 2023. Lecture Notes in Computer Science, vol 14283. Springer, Cham. https://doi.org/10.1007/978-3-031-43943-8_6.
de Sousa Rosa, L., Carastan-Santos, D., Goldman, A., & Trystram, D. (2023). On limits of Machine Learning techniques in the learning of scheduling policies. Electronic Journal of Undergraduate Research on Computing, 21(2), 61–70. https://doi.org/10.5753/reic.2023.3419.
Rosa, L., Carastan-Santos, D., & Goldman, A. (2023). Exploring Simplicity and Efficiency: Regression-based Scheduling Heuristics in HPC. In Proceedings of the 14th Regional School of High-Performance Computing of São Paulo, (pp. 41-44). Porto Alegre: SBC. https://doi.org/10.5753/eradsp.2023.232635.
Rosa, L., & Goldman, A. (2022). In search of efficient scheduling heuristics from simulations and Machine Learning. In Companion Proceedings of the 23rd Symposium on High Performance Computing Systems, (pp. 17-24). Porto Alegre: SBC. https://doi.org/10.5753/wscad_estendido.2022.226323.

Scholarships

09/2026–08/2027	FAPESP Research Internship Abroad (BEPE-DD) Sustainable Supercomputing: Energy Efficiency and Resource Management through Statistical Modeling and Machine Learning Enhance supercomputing resource management using machine learning and optimization techniques with an emphasis on energy-aware scheduling. Principal Investigator: Alfredo Goldman vel Lejbman. Grant Number: 23/09048-8.
09/2024–02/2026	FAPESP Doctorate (DD) Sustainable Supercomputing: Energy Efficiency and Resource Management through Statistical Modeling and Machine Learning Enhance supercomputing resource management using machine learning and optimization techniques with an emphasis on energy-aware scheduling. Principal Investigator: Alfredo Goldman vel Lejbman. Grant Number: 23/09048-8.
08/2023–08/2024	CAPES Doctorate (DD) Academic Excellence Program (PROEX) Scholarship obtained through academic merit to support my doctorate research.
02/2023–04/2023	FAPESP Research Internship Abroad (BEPE-IC) Evaluating machine learning techniques and simulations to create efficient scheduling heuristics Collaboration with the DATAMOVE research group to investigate regression methods to create simple and efficient scheduling heuristics. Principal Investigator: Alfredo Goldman vel Lejbman. Supervisor: Denis Trystram. Co-supervisor: Danilo Carastan-Santos. Grant Number: 22/14673-6.
07/2022–08/2023	FAPESP Scientific Initiation (IC) On limits of Machine Learning techniques in the learning of scheduling policies Explore the emerging relationship between managing resources on high-performance computing (HPC) platforms and the use of regression-derived scheduling heuristics to optimize performance. Principal Investigator: Alfredo Goldman vel Lejbman. Co-supervisor: Danilo Carastan-Santos. Grant Number: 22/06906-0.
08/2020–12/2021	FAPESP Technical Training (TT-1) Technical training in numerical methods and administration of computational resources Training in the management of high-performance computing systems and introduction to numerical methods relevant to scientific computing, particularly in the area of molecular simulation. Principal Investigator: Guilherme Menegon Arantes. Grant Number: 20/09918-4.

Teaching Experience

03/2025–07/2025	Introduction to Computing for Exact Sciences and Technology Universidade de São Paulo · Alfredo Goldman Teaching assistant for undergraduate course. I taught a few classes and created homework assignments for a class of 70 students.
08/2024–12/2024	Concurrent and Parallel Programming Universidade de São Paulo · Alfredo Goldman Teaching assistant for graduate-level course. I taught a few classes and created homework assignments for a class of 90 students.

Honors & Awards

2023	Brazil’s top 10 Scientific Initiation (IC) project Undergraduate Research Contest (CSBC-CTIC) Work selected as one of the 10 best scientific initiation works of 2023 according to the Brazilian Computer Society (SBC).
2022	Honorable Mention Undergraduate Research Workshop (WSCAD-WIC) Awarded for the paper “In search of efficient scheduling heuristics from simulations and Machine Learning”.

Languages

Brazilian Portuguese: Native Speaker.

English: Advanced.

French: Basic.

Spanish: Basic.