Language Models · AI Alignment
My research interests center on language models and AI alignment. I am particularly interested in the design and training of language models, including how training choices shape their capabilities and behavior, and in developing models that are more capable, reliable, and aligned.
I completed a B.S. in Computer Science, with minors in Mathematics and Physics, at the University of North Carolina at Pembroke. I was an AI Safety Research Fellow at Algoverse, am currently participating in MIT AI Alignment's AI Safety Fundamentals program, and have completed BlueDot Impact's Technical AI Safety course.
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DPBench: Structural Determinants of Multi-Agent LLM Coordination Under Simultaneous Resource Contention
Najmul Hasan and Prashanth BusiReddyGari. Preprint, 2026. Code
Benchmarking Large Language Models for Zero-shot and Few-shot Phishing URL Detection
Najmul Hasan and Prashanth BusiReddyGari. LAW Workshop, NeurIPS 2025.
Honeypot Protocol
Najmul Hasan. AI Control Hackathon, Apart Research, 2026. Code
SAGE
A Python framework in which language-model agents research, discuss, and synthesize answers through a structured workflow.
Sift
An autonomous IT ticket triage system that produces diagnoses, resolution steps, and escalation decisions.


