Andrew Quijano is a Computer Science Ph.D. candidate at the NYU Tandon School of Engineering under the supervision of Professor Danny Huang. His research focuses on automated vulnerability injection and software security benchmarking. This work expands upon the Large-Scale Automated Vulnerability Addition (LAVA) framework originally begun by his first Ph.D. advisor, Associate Professor Brendan Dolan-Gavitt. Additionally, as part of the NYU mLab, supervised by his new advisor, Danny Y. Huang, Quijano has contributed to IoT Inspector, an open-source tool for analyzing consumer IoT networks.
Quijano holds a B.A. in Mathematics from CUNY Queens College, a B.S. and an M.S. in Computer Science from Columbia University, and an M.S. in Cybersecurity via the NYU Cyber Fellows program. Beyond academia, Quijano brings a strong industry background to his research, courtesy of a Google Summer SWE Ph.D. Internship, and security engineering roles at Amazon, Fitch Ratings, and SMBC. His publications span several computer science and legal policy domains, and include an EMEASEC 2026 paper developed during his research internship at MIT Lincoln Laboratory, a Federal R&D facility developing technology in support of national security. This work established a programmatic framework for measuring architectural RTO/RPO compliance through SysML modeling. His other peer-reviewed work covers homomorphic encryption for secure decision trees, privacy-preserving drone navigation, and computational biology, and he has co-authored technical/legal analyses covering Section 230 immunity, digital privacy law, and protecting victims of technology-facilitated domestic abuse.