cv
My resume/curriculum vitae (CV).
Basics
| Name | Adebayo Braimah |
| Label | PhD Student in Computer Science |
| adebayo.braimah@stonybrook.edu | |
| Phone | +1 (620) 391-6062 |
| Url | https://www.stonybrook.edu/ |
| Summary | PhD student specializing in explainable artificial intelligence (XAI), explainable reinforcement learning (XRL), and the explainability of large language models (LLMs). |
Work
-
2025.06 - 2025.08 Graduate Research Intern
Air Force Research Laboratory (AFRL), Information Directorate
Internship via the NDSEG fellowship program at AFRL. Worked alongside Assoc. Prof. Garrett Katz, PhD (Syracuse University) and AFRL point of contact Simon Khan, PhD.
- Explainable Multi-Agent Reinforcement Learning (MARL) for cooperative tasks of unmanned aerial vehicles (UAVs).
-
2024.06 - 2024.08 Graduate Research Intern
Griffiss Institute, AFRL Information Directorate
Internship via the SFFP (Summer Faculty Fellowship Program) at AFRL. Worked alongside Asst. Prof. Yifan Sun, PhD and AFRL point of contact Walter Bennette, PhD.
- Explainability of machine learning models using loss landscape analysis.
-
2023.01 - 2023.05 Graduate Research Assistant
Stony Brook University
Focused on profiling eigenspectrum decomposition methods for large matrices under Prof. Yifan Sun.
- Applied advanced eigenspectrum decomposition techniques to analyze neural networks.
-
2018.06 - 2022.07 Research Software Engineer
Cincinnati Children’s Hospital Medical Center
Analyzed functional and structural connectivity in pediatric neuroimaging projects.
- Developed imaging solutions for neonatal hypoxic ischemic encephalopathy and ADHD studies.
Education
Awards
- 2025.03.01
SUNY GREAT Award
State University of New York (SUNY)
SUNY Graduate Research Empowering and Accelerating Talent (GREAT) Award. Provided to SUNY students who have been awarded prestigious national fellowships.
- 2024.08.01
- 2022.08.01
Skills
| Programming | |
| Python | |
| MATLAB | |
| Bash | |
| R | |
| LATEX |
| Research Methods | |
| Explainable AI | |
| Reinforcement Learning | |
| Large Language Models |