RUSSELLVILLE — Students from the Arkansas Tech University College of Science, Technology, Engineering and Mathematics were among more than 100 university students from around the state who participated in Arkansas STEM Posters at the Capitol 2026 event on Wednesday.
The annual event brings students together to share their scientific findings with their peers at other universities, legislators and state officials. Thirteen institutions of higher learning were represented this year, and the results of 72 projects were presented.
“Being able to adapt to new knowledge, new research and being able to find uses for that are the skills I have developed from this experience,” said Arkansas Tech student Malaya Wilburd of Sherwood. “Honestly, a year ago, I would not have thought I would have been here, but it feels good to know I made it here and that my knowledge and my education have led me to a moment like this.”
“The big takeaway for the students is they are exposed to not only the Arkansas Tech University research community, but the entire state of Arkansas as a whole,” said Robin Ghosh, assistant professor of computer and information science. “They are learning new concepts and new topics while networking and collaborating with other researchers. This is the beginning conference that I send my students to in order to expose them to the Arkansas research community. We want to gradually boost their confidence.”
Six projects were presented by student participants and faculty mentors from the Arkansas Tech delegation at Arkansas STEM Posters at the Capitol 2026 in Little Rock. Presentations included:
A Comprehensive Survey of Agentic AI: Design Principles, Security Risks and Ethical Considerations — presented by Md Shaba Sayeed. Faculty mentor: Robin Ghosh.
Optimizing Energy Consumption to Minimize Carbon Emissions: A Machine Learning Approach for Facility Operations — presented by Malaya Wilburd. Faculty mentor: Tolga Ensari.
Neuromorphic Seizure Detection: EEG Analysis with Spiking Neural Networks — presented by Shruti Bhandari and Md Shaba Sayeed. Faculty mentor: Tolga Ensari.
Small Language Models for Edge AI: Enabling Private, Efficient On-Device Intelligence — presented by Kaan Boke. Faculty mentor: Tolga Ensari.
Neuromorphic Approaches to Data Compression with Spiking Models — presented by Malaya Wilburd, Shruti Bhandari, Md Shaba Sayeed and Harsha V Gudala. Faculty mentor: Tolga Ensari.
Privacy-Preserving RAG System for Personal Document Question Answering — presented by Kesava Manikanta Chirumamilla. Faculty mentor: Md Abdus Siddique.
