Email: makbar[at]utep[dot]edu | Phone: (915) 747-5883
I am an Associate Professor in the Department of Computer Science at The University of Texas at El Paso (UTEP), where I have been a faculty member since Fall 2017. Before that, I was a Research Assistant Professor and the Assistant Director of the CyberShare Center of Excellence at UTEP. I received my Ph.D. in Computer Science from Virginia Tech, a Master’s degree from Montana State University–Bozeman, and a Bachelor’s degree from Shahjalal University of Science and Technology in Bangladesh.
My research focuses on information retrieval, data integration, and analytics, with applications in areas such as cybersecurity for industrial control systems and manufacturing, as well as public health. I develop methods to identify and analyze vulnerabilities and weaknesses and to anticipate emerging threats, with the goal of making critical systems more secure and resilient. As the faculty lead for UTEP’s Miner Cybersecurity Clinic, I guide applied cybersecurity education and community engagement that connect research with real-world practice. In teaching, I design and integrate technology-enabled approaches that support student learning and strengthen problem-solving in computing and programming.
Research Focus
Information Retrieval and Integration
My research develops frameworks for retrieving, integrating, and representing heterogeneous information. I focus on linking structured and unstructured data such as cybersecurity knowledge bases (CVE, CWE, ATT&CK), network packet traces, public health records, and sensor data to support intelligent search, reasoning, and decision-making. These efforts use graph-based modeling and language-model embeddings to uncover relationships and strengthen knowledge representation across diverse datasets.
Predictive Modeling and Data Analytics
I design data-driven models to assess risk, forecast events, and explain complex system behaviors. My work includes predicting cyberattacks, modeling vulnerability evolution, and detecting multi-disease outbreaks. It also extends to translating wearable accelerometer data into interpretable narratives that support neuro-rehabilitation and patient monitoring. These efforts deepen understanding of evolving data patterns and demonstrate how predictive insights can guide timely interventions.
Cybersecurity
A central focus of my research is cybersecurity for critical infrastructure sectors, including manufacturing, healthcare, and other critical infrastructure. I investigate software vulnerabilities, ICS/OT attack vectors, and anomaly detection using AI-enabled reasoning and graph analytics. This work builds systematic links between weaknesses and attacks, enhances the interpretability of threat intelligence, and strengthens system resilience.
Technology-Enabled Learning
I investigate how technology can advance learning by creating systems that adapt to learner needs and provide meaningful insights for instructors. My work spans mobile platforms, game-based environments, and AI/ML-based predictive systems such as PULSE, which analyzes programming activity data to detect and address student struggles in real time. Across projects like Dysgu, Sol y Agua, and PULSE, I focus on building innovative, evidence-based approaches that enhance how learning is experienced, supported, and understood.
Prospective Students
I am looking for motivated graduate students to join my research group. My work spans AI/ML, data integration, and predictive modeling, with applications in cybersecurity, public health, and technology-enhanced education. If you are interested, please reach out to me by email.