{"id":6,"date":"2026-02-16T13:20:42","date_gmt":"2026-02-16T13:20:42","guid":{"rendered":"https:\/\/www.marshall.edu\/reu\/?page_id=6"},"modified":"2026-02-27T13:57:28","modified_gmt":"2026-02-27T13:57:28","slug":"projects","status":"publish","type":"page","link":"https:\/\/www.marshall.edu\/reu\/projects\/","title":{"rendered":"2025 Research Projects"},"content":{"rendered":"
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Analyzing intrusion detection logs in IoT networks<\/h4>\n
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Develop lightweight countermeasures to detect and prevent cyber threats in low-power networks.<\/p>\n

Faculty Mentor: Dr. Wook-Sung Yoo <\/a><\/strong><\/p>\n<\/div>\n<\/div>\n\n<\/div>\n<\/div>\n\n

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Emotional Analytics for Images<\/h4>\n

Have you ever wondered how computers can recognize emotions from images? In this REU project, students will explore the fascinating field of Emotional Analytics, where artificial intelligence (AI) is used to analyze facial expressions and determine emotions such as happiness, sadness, surprise, or frustration. By leveraging deep learning techniques and computer vision, participants will develop models capable of accurately interpreting human emotions from images. This research has powerful applications in mental health monitoring, human-computer interaction, and personalized user experiences.<\/p>\n

Faculty Mentor: Dr. Haroon Malik<\/a><\/strong><\/p>\n<\/div>\n<\/div>\n\n<\/div>\n<\/div>\n\n

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Innovative Railroad Maintenance System <\/h4>\n

Are you excited about transforming the future of transportation? In this innovative REU project, we aimed to create a maintenance warning system for railroads using cutting-edge computer vision and object detection technologies. This project offers a unique opportunity for undergraduate students to apply their machine learning and computer vision skills to a real-world problem that impacts safety and efficiency in the railway industry. By participating in this project, you will gain hands-on experience, collaborate with experts in the field, and contribute to pioneering solutions that could revolutionize railroad maintenance. Don’t miss the chance to be part of a team driving technological advancements. Apply now and make a tangible difference!
\nFaculty Mentor: Dr. Husnu Narman<\/a><\/strong><\/p>\n

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Immersive Virtual Reality for Enhanced Learning Environments<\/h4>\n

Have you ever imagined how virtual reality (VR) can transform the way we learn and interact with complex concepts? In this REU project, students will dive into the cutting-edge field of immersive virtual reality, exploring how VR technologies can create engaging and effective learning environments. Under the guidance of Dr. Tanvir Irfan Chowdhury, participants will design and develop VR applications that simulate real-world scenarios, enabling users to interact with and understand intricate subjects in fields such as science, engineering, and medicine. By combining principles of human-computer interaction, 3D modeling, and user experience design, students will create immersive experiences that enhance comprehension, retention, and engagement. This research has far-reaching implications for education, training, and beyond, offering new ways to make learning more accessible and impactful.<\/p>\n

Faculty Mentor: Dr. Tanvir Irfan Chowdhury<\/a><\/strong><\/p>\n<\/div>\n<\/div>\n<\/div>\n\n<\/div>\n<\/div>\n\n

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AI\/ML-Driven Battery Health Monitoring via Multi-Modal Data Analytics and Data Fusion<\/h4>\n

Lithium-ion batteries are foundational to electric vehicles, drones, medical devices, and grid storage. Yet, their performance degrades in ways that are difficult to forecast reliably, especially under changing temperatures, charging rates (C-rates), and duty cycles. This REU project will develop and rigorously benchmark a reproducible data analytics + AI\/ML pipeline to estimate State of Health (SOH) and, when feasible, Remaining Useful Life (RUL) using publicly available datasets (e.g., NASA aging datasets and other open benchmarks). Students will analyze contact signals and contactless\/non-invasive indicators, with the emphasis on analytics and robust generalization rather than data collection or database construction; specifically, the work will enforce leakage-free evaluation (e.g., split-by-battery), quantify dataset shift across operating conditions, and investigate multi-modal data fusion (early fusion and ensemble\/stacking) to improve prediction stability for practical monitoring and deployment. A core deliverable is a conference-ready research paper grounded in the benchmark results.<\/p>\n

Faculty Mentor: Dr. Yousef Fazea<\/a><\/strong><\/p>\n<\/div>\n<\/div>\n\n<\/div>\n<\/div>\n\n

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Truth Tracker: Can AI Spot Fake News?<\/h4>\n
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Fake news spreads fast\u2014but can AI track how far a story drifts from the truth? This project dives into real news sources like AP and Reuters to uncover patterns in misinformation. Using AI and Machine Learning, we\u2019ll analyze linguistic clues, detect deviations, and explore how fake news spreads. Instead of just labeling stories as true or false, we\u2019ll measure their distance from reality. Join us to build cutting-edge tools that fight misinformation with data-driven insights!<\/p>\n

Faculty Mentor: Dr. Char Sample<\/a><\/strong><\/p>\n<\/div>\n<\/div>\n\n<\/div>\n<\/div>\n\n

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Understanding Virtual Reality Players<\/h4>\n
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In this project, students will explore virtual reality (VR) game players through their in-game behaviors and post-experience reviews. The project may involve one or more of the following tasks: Developing a VR application, running a lab study, analyzing quantitative game data to determine behavior patterns, and analyzing qualitative data to understand player experiences. This project would be an excellent fit for students who are interested in games and\/or virtual reality!<\/p>\n

Faculty Mentor: Dr. Mehmet Kosa<\/a><\/strong><\/p>\n<\/div>\n<\/div>\n\n<\/div>\n<\/div>\n\n

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