Trustworthy Language Intelligence for Security-Relevant and Harmful Online Text
Research in security-aware NLP, incident-centric social-media intelligence, and rigorous benchmarking of traditional ML, transformers, and LLM-based approaches (including Green NLP).
I develop methods for analyzing, grading, and prioritizing social-media discourse related to cybersecurity incidents, healthcare cyberattacks, and AI-generated/harmful language.
About
I am an Assistant Professor in Computer Science & Cybersecurity at Minot State University. My work focuses on building reproducible, reliable, and efficient language intelligence pipelines for high-noise, high-stakes settings—especially cybersecurity discourse on social platforms.
A central thread across my projects is controlled comparison: understanding when lightweight baselines (e.g., TF-IDF + linear models) are sufficient, when transformers and LLMs are justified, and how performance changes under cost, latency, and energy constraints.
Featured Research
CyberTweetGrader&Labeler (CTGL)
A domain-specific NLP pipeline for prioritizing cyber-incident discourse on Twitter/X, with incident-centric feature engineering and transparent relevance grading.
Benchmarking: ML vs Transformers vs LLMs
Controlled evaluations of prompted LLM inference vs fine-tuned encoders and traditional ML, emphasizing reliability and deployment-realistic trade-offs.
Green NLP for Online Abuse Detection
Energy-aware benchmarking that jointly measures accuracy, latency/throughput, and inference energy for lightweight baselines vs fine-tuned transformers.
News & Updates
- Jan 2026: Submitted work on controlled evaluation of prompted LLM inference vs fine-tuned encoders (under review).
- Jan 2026: Submitted Green NLP work on accuracy–latency–energy trade-offs for abuse detection (under review).
- Dec 2025: Submitted curated UHS Twitter/X dataset and documentation focused on incident-response utility (under review).
(This section is intentionally short; I update it a few times per year.)
For Collaborators & Students
I welcome collaborations in security-aware NLP, LLM/transformer benchmarking, Green NLP, and incident-centric social-media intelligence. Undergraduate and graduate students interested in research are encouraged to contact me.
The best starting point is the Trustworthy Language Intelligence Lab (TLI Lab) page, which lists current directions and open project ideas.
Contact
Email: Muhammad.Abusaqer@MinotStateU.edu
Office: Model Hall 110, Minot State University, Minot, ND
Office phone: (701) 858-3075