work.

I research quantum computing. Most recently, I spent a summer at the National Security Agency working on classical decoders for Decoded Quantum Interferometry. Before that: quantum machine learning research with the RPI School of Science, cybersecurity research with the Rensselaer Cybersecurity Collaboratory, and a semester abroad at the Chinese University of Hong Kong.

research

Future of Computing Summer Intern — National Security Agency Summer 2026

Co-developed the blocked fast Walsh–Hadamard transform (BFWHT), an exact set-covering decoder for the low-weight binary closest vector problem, with CUDA/GPU implementations benchmarked on A100-class hardware. Built the end-to-end Decoded Quantum Interferometry (DQI) research codebase with interchangeable decoders and reversible resource estimation, and executed small circuits on IBM's 156-qubit Kingston system.

Quantum Machine Learning Researcher — RPI School of Science Summer 2025

Designed a Qiskit quantum-kernel/SVM pipeline for network intrusion detection, achieving 85%+ accuracy on RPI's 127-qubit IBM Quantum System One. Built the reproducible QML-NIDS codebase and first-authored the resulting AAAI QIML proceedings paper.

Cybersecurity Research Assistant — Rensselaer Cybersecurity Collaboratory 2024 – 2025

Developed augmented-reality CTF challenges in steganography, DNS poisoning, and MFA bypass; coauthored a CISSE journal article and ASIA proceedings paper on generative AI and cybersecurity pedagogy.

publications & presentations

Towards Efficient Low-Weight Decoding: A Blocked Fast Walsh–Hadamard Transform

Cotrupi, M. L., Gutti, M., Ralph, B., & Sandberg, K. (2026). FCSI 2026 final research report, National Security Agency.
At n=64, m=2,097,152, t=8, the BFWHT reduced estimated decoding operations by 732× versus optimized Gray-code enumeration, and reduced estimated logical qubits for DQI by 688× on a 64×64 benchmark.

Towards Practical Quantum Kernels for Network Intrusion Detection

Cotrupi, M. L., & Callahan, B. R. (2025). Proceedings of the AAAI Symposium Series, 7(1), 339–342. doi:10.1609/aaaiss.v7i1.36903 · pdf
Author's note: my first foray into quantum computing. This 10-week project culminated in a paper and poster at the first AAAI QIML symposium—and it was so much fun that it entirely shifted my focus within computer science toward quantum, which I now aim to pursue in post-graduate study.

A Case Study for Combating Student Overuse of Generative AI in Cybersecurity Education

Sugerman, S., Joseph, S., Colognato, Q., Cotrupi, M. L., et al. (2026). Journal of The Colloquium for Information Systems Security Education, 13(1), Article 11. doi:10.53735/cisse.v13i1.216

RC3TF, an Augmented Reality CTF: Improving Cybersecurity Pedagogy for the Undergraduate Research Laboratory

Callahan, B. R., Joseph, S., Sugerman, S., Colognato, Q., Cotrupi, M. L., et al. (2025). Proceedings of the 20th Annual Symposium on Information Assurance (ASIA). doi:10.2139/SSRN.6550178

Generative AI for Cybersecurity Awareness Training: Skills, Strategies, and Accountabilities