Mr. Muhammad Abusaqer

Assistant Professor of Computer Science

Office: Model Hall #110
Email: muhammad.abusaqer@minotstateu.edu

Muhammad Abusaqer holds a B.S. degree in Mathematics with a minor in Computer Science. He earned his M.S. in Computer Science Computer Science from Virgina Tech. He is currently completing his Ph.D. in Computer Science at North Dakota State University, with an expected defense in Spring 2025. His research focuses on developing a robust tool for mining and analyzing cybersecurity content on social media.

His current research interests include developing analytical tools to assess cybersecurity discussions on social media, improving digital platforms' ability to manage cyber threat narratives, and evaluating the performance of pre-trained AI Transformer models in various cybersecurity domains, including cyberbullying and hate speech detection. Additionally, his work explores AI-generated text and large language models, reflecting his growing engagement in this field. He is also interested in Computer Science education and pedagogical methodologies.

Academic Service

Abusaqer has mentored several undergraduate research teams, fostering an environment that promotes innovation and problem-solving in AI fields. His guidance has led to multiple student-led presentations and publications at academic conferences and symposiums.

Publications

(Student co-authors under my supervision are underlined)

Muhammad Abusaqer and Jacob Jensen, "Global Echoes of the FIFA World Cup 2022: Sentiment and Theme Analysis via Deep Learning and Machine Learning on Twitter," submitted to the Midwest Instruction and Computing Symposium (MICS) 2024. Link
Saif Khan, Kaif Khan, and Muhammad Abusaqer, "Text Detection between an AI Written Passage vs. a Human Written Passage," submitted to the Midwest Instruction and Computing Symposium (MICS) 2024. Link
Travis Smith and Muhammad Abusaqer, "Predicting Campus Crime Based on State Firearm Policy," submitted to the Midwest Instruction and Computing Symposium (MICS) 2024. Link
Muhammad Abusaqer and Charles Fofie Jr., "Cyberbullying Classification Using Three Deep Learning Models: GPT, BERT, and RoBERTa," presented at the Midwest Instruction and Computing Symposium (MICS) 2023. Link
Muhammad Abusaqer and Quinn Sullivan, "Darknet Traffic Classification Using Deep Learning," presented at the Midwest Instruction and Computing Symposium (MICS) 2023. Link
Aden Scott, J.T. Snow and Muhammad Abusaqer, "Automated Categorization of Cybersecurity News Articles through State-of-the-Art Text Transfer Deep Learning Models," presented at the Midwest Instruction and Computing Symposium (MICS) 2023. Link
Muhammad Abusaqer, M.B. Senouci, and K. Magel, "Twitter User Sentiments Analysis: Health System Cyberattacks Case Study," presented at the International Conference on A.I. in Information and Communication (ICAIIC 2023). Link
Muhammad Abusaqer and K. Magel, "Comparison of Students' Learning and Engagement on an Online Course Before and After the Spread of COVID-19," presented at MICS 2022. Link
Muhammad Abusaqer and K. Magel, "Teaching Computer Packages to Students Different in Everything," presented and published at MICS 2019. Conference Presentation Schedule link, here is the PDF
Yogita Bhardwaj, Muhammad Abusaqer, and M. Pérez-Quiñones, "General Interface Description of Websites using CLICK and UIML," Technical Report T.R. 04-37, Dept. of Computer Science, Virginia Tech, 2004. link

Teaching Experience

Minot State University

Applied Cryptography – Fall 2022, Spring 2024
Ethical Hacking – Fall 2022, Spring 2024
Computer Networks I – Fall 2022, Fall 2023, Fall 2025
Mobile and Wireless Security – Spring 2023
Software Engineering and Testing – Spring 2023, Spring 2025
Defensive Network Security – Fall 2023, Spring 2025
Vulnerability Analysis – Fall 2023, Spring 2025

North Dakota State University (During Ph.D. Studies)

Business Use of Computers (CSCI/MIS 116) – Served as an adjunct professor for this high-enrollment course, managing 420 students in Fall and 350 students in Spring, ensuring effective instruction, student engagement, and large-scale course administration.
Microcomputer Packages (CSCI 114)