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William & Mary — Ph.D. in Computer Science.

• Research Focus: Green AI for Software Engineering.
• Working on resource-efficient Large Code models and parameter-efficient fine-tuning for large code models.
• Published research in top-tier conferences including IEEE/ACM Forge 2025.

Daffodil International University — B.Sc. Computer Science and Engineering.

Research Assistant at William & Mary.

• Conducting research on Green AI for Software Engineering with focus on resource-efficient machine learning.
• Adapting parameter-efficient fine-tuning techniques for large code models.
• Published multiple research papers in top-tier conferences and journals.

Teaching Assistant at William & Mary.

• Assisted in teaching computer science courses and mentoring students.
• Graded assignments and provided feedback to improve student learning outcomes.

Lecturer at Daffodil International University.

• Taught courses in Computer Science and Engineering department.
• Conducted research in machine learning and published multiple papers.
• Mentored undergraduate students in their research projects.

Project Intern at Bangladesh Venture Capital LTD.

• Worked on various technology projects and gained industry experience.

Recent Publications (Ph.D. Work)

• S. Afrin, B. Xu, and A. Mastropaolo, "Is Quantization a Deal-breaker? Empirical Insights from Large Code Models," arXiv preprint arXiv:2507.09665, 2025.
• S. Afrin, J. Call, K.-N. Nguyen, O. Chaparro, and A. Mastropaolo, "Resource-efficient & effective code summarization," in 2025 IEEE/ACM Second International Conference on AI Foundation Models and Software Engineering (Forge), IEEE, 2025, pp. 224–235.
• A. Giagnorio, A. Mastropaolo, S. Afrin, M. Di Penta, and G. Bavota, "Quantizing large language models for code generation: A differentiated replication," arXiv preprint arXiv:2503.07103, 2025.
• M. Z. Haque, S. Afrin*, and A. Mastropaolo, "A systematic literature review of parameter-efficient fine-tuning for large code models," arXiv preprint arXiv:2504.21569, 2025. (*Equal contribution)
• Y. Gong, A. Tarafder, S. Afrin, and P. Kumar, "Single-gpu gnn systems: Traps and pitfalls," arXiv preprint arXiv:2402.03548, 2024.

Earlier Publications

• S. Afrin, F. J. M. Shamrat, T. I. Nibir, et al., "Supervised machine learning based liver disease prediction approach with lasso feature selection," Bulletin of Electrical Engineering and Informatics, vol. 10, no. 6, pp. 3369–3376, 2021.
• S. Chakraborty, F. J. M. Shamrat, S. Afrin, S. Saha, I. Ahmed, and S. Thapa, "Industrial fault detection using transfer learning models," in 2021 2nd International Conference on Smart Electronics and Communication (ICOSEC), IEEE, 2021, pp. 1–6.
• P. Ghosh, A. Karim, S. T. Atik, S. Afrin, and M. Saifuzzaman, "Expert cancer model using supervised algorithms with a lasso selection approach," International Journal of Electrical and Computer Engineering, vol. 11, no. 3, pp. 2632–2640, 2021.
• P. Ghosh, F. J. M. Shamrat, S. Shultana, S. Afrin, A. A. Anjum, and A. A. Khan, "Optimization of prediction method of chronic kidney disease using machine learning algorithm," in 2020 15th international joint symposium on artificial intelligence and natural language processing (iSAI-NLP), IEEE, 2020, pp. 1–6.

• Dr. Aminul Islam Scholarship, selected as the top candidate.

• Department Scholarship for Outstanding Student Performance, Daffodil International University.