BERT-OTA: Ontology-Guided Transformer Architecture
Dual-stream architecture integrating BERT with Graph Neural Networks and scaled attention for binary hate speech detection. Achieved 91.30% accuracy, establishing a new benchmark.

Researcher & Data Scientist
Specializing in NLP, LLMs, deep learning, and transformer architectures. Author of 9+ publications in venues including IEEE Access, ICMLA, and SEKE.
I am a researcher and engineer working at the intersection of natural language processing, large language models, and deep learning. My work focuses on designing architectures that combine the contextual power of transformers with structured knowledge from graph neural networks, resulting in 9 publications in venues such as IEEE Access, JVLC, ICMLA, SEKE, and ACMSE.
I am broadly driven by how AI systems learn, reason, and generalize. Whether benchmarking 303 model configurations for hate speech detection or evaluating parameter-efficient fine-tuning across 20 LLM architectures, I thrive on rigorous experimentation and collaborative, interdisciplinary problem-solving.
On the industry side, I have worked as a Software and Data Science Engineer at Metachain Technologies and as a Software Engineer at Xenomics, where I designed scalable backend systems and integrated machine learning models into production applications.
Research, industry, and teaching roles
May 2024 – Present
Missouri State University · Springfield, MO
Feb 2023 – Apr 2024
Metachain Technologies Inc.
Aug 2022 – Feb 2023
Xenomics
Aug 2022 – Aug 2023
Islamic University of Gaza · Gaza, Palestine
Research and engineering projects
Dual-stream architecture integrating BERT with Graph Neural Networks and scaled attention for binary hate speech detection. Achieved 91.30% accuracy, establishing a new benchmark.
Dual-stream architecture combining RoBERTa with Enhanced 3-Layer GNN for 5-class hate speech detection. Achieved state-of-the-art 96.06% F1-score, outperforming previous best by 1.62 percentage points.
Evaluated 20 LoRA-adapted LLMs (Llama, Phi, Qwen) and three zero-shot API models for hate speech detection. Best fine-tuning achieved 91.84% accuracy with only 0.15% trainable parameters.
Autonomous AI agent using Claude API for university course registration assistance. Implements agentic architecture with web scraping pipeline, Flask, and context-aware conversation management.
Cross-platform mobile AI assistant using Unity and C# with OpenAI API integration. Real-time NLP with text-to-speech and speech-to-text capabilities for multiple languages.
Genetic algorithm-based optimization system generating conflict-free schedules for 2000+ students across 38+ departments with custom mutation operators.
9 papers across IEEE, ACM, and other top venues
M. Abusaqer, S.M. Faiaz Mursalin, and J. Saquer
ACM Southeast Conference (ACMSE), 2026 (submitted)
M. Abusaqer, J. Nwobodo, K. Hasan, and J. Saquer
ACM Southeast Conference (ACMSE), 2026 (submitted)
M. Abusaqer, J. Saquer
International Conference on Computational Science and Computational Intelligence (CSCI), 2025
Peer Reviewer -- IEEE Access (5 verified reviews) | ORCID: 0009-0006-2850-6566
Missouri State University · Springfield, MO
Jan 2024 – May 2026 (Expected)
GPA: 3.76 / 4.0
Thesis
Advancing Hate Speech Detection: A Comprehensive Evaluation of Traditional, Deep Learning, and Parameter-Efficient Transformer Approaches with Ontological Knowledge Integration
Islamic University of Gaza · Gaza, Palestine
Sep 2018 – Aug 2022
GPA: 86 / 100
Ranked 4th in graduating class
Senior Project
Automated University Timetable Scheduling Using Evolutionary Algorithms with Shotgun Hill-Climbing Optimization
Einhellig Interdisciplinary Forum (EIDF), Missouri State University
Missouri State University
Bachelor of Computer Science, Islamic University of Gaza
AI Agents Course Certificate
Hugging Face
DataCamp Professional Data Scientist
DataCamp
ML Bootcamp
Gaza Sky Geeks
IEEE Access Peer Reviewer
5 verified reviews
Open to research collaborations, industry positions, and new opportunities
Open to relocation. U.S. work authorization in progress.