hi! i'm aaditya rengarajan, cybersecurity/ai researcher & engineer and a ms cybersecurity grad at nyu. my work sits at the crossroads of offensive security, agentic ai, and automation-first systems design.
over the past few years, i’ve contributed to a mix of gov-grade threat intelligence platforms, large-scale system automation tools, and privacy-preserving ml pipelines. alongside that, i’ve led cybersecurity education initiatives training over 500+ learners in real-world offensive security and python for cyber ops.
(6m) intel corporation: worked on automating operations research tasks within intel foundry using ml and deep rl; also designed an agentic ai framework architecture for internal workflows
(2m) indian space research organization: modeled a new cyber threat intelligence framework using stix/taxii protocols and visualized ioc-based threats using graph analytics
(3y) tactical cyberange simulations: developed a modular, extensible framework that aggregates and orchestrates offensive security tools for red-teamers; focused heavily on system design and architecture
(1m+) equate petrochemical company: built a phishing trace-back tool and org-wide recon dashboard including dns mapping, device exposure tracking, and employee breach visibility
(6m) information sharing and analysis center (isac): built dark web monitoring systems and base IT systems
ongoing research
activeNIST C reference implementation for pqc
building a NIST-submission-ready C reference implementation of eidolon, a post-quantum digital signature scheme based on the NP-complete k-colorability problem. security validated against classical solvers and custom graph neural network attackers.
with prof. delaram kahrobaei
tracing the full mine-to-microchip supply chain of supercomputer hardware, from minerals through component vendors to finished TOP500 machines, to find risk points and security issues hiding in the chain.
with prof. alon hillel-tuch & prof. ramesh karri
activepost-quantum oblivious inference for constrained devices
a systematic review exploring the intersection of post-quantum cryptography and privacy-preserving machine learning inference on resource-constrained devices.
examining vulnerabilities and defensive strategies for the protocol underpinning modern aircraft tracking.
supervised by lab faculty director prof. alon hillel-tuch
a framework for dynamic, privacy-preserving network defense that leverages federated learning and encrypted model updates to adapt to emerging threats in real time.
federated learningprivacy-preservationencrypted model updatesadaptive firewalls