REU Site: Applied Research in Deep Learning

This project will provide undergraduate students with hands-on research experiences in the rapidly growing field of deep learning. Over three years, the program will engage 30 students from computer science, engineering, information technology, and related disciplines in meaningful research projects guided by faculty mentors. Participants will explore how deep learning techniques can be used to address real-world challenges in cybersecurity, robotics, hate speech in social media, and autonomous systems. Through these experiences, students will gain exposure to how deep learning technologies can be used to benefit society across a wide range of applications. In addition to research activities, the program will provide structured training to help students develop important professional skills, including scientific writing, oral communication, teamwork, and responsible research practices. By working in a collaborative and supportive environment, students will strengthen both their technical abilities and their confidence as emerging researchers. Overall, the program aims to inspire and prepare the next generation of scientists and engineers by giving them early, meaningful exposure to research in deep learning and its many beneficial uses. This project establishes a structured, research-intensive undergraduate training program aimed at increasing student engagement in state-of-the-art deep learning methodologies and applications. Research activities will focus on the design, analysis, and application of modern deep learning techniques to address contemporary challenges in areas such as multimedia security, robotic motion planning, autonomous multi-agent coordination, and malicious network activity detection. Faculty-mentored projects will emphasize both methodological advances and applied system development, with students contributing to end-to-end research pipelines including data preprocessing, model design, training, evaluation, and deployment-oriented analysis. Key objectives include: (i) developing students’ problem-solving and critical thinking skills in the context of deep learning research; (ii) building proficiency in contemporary deep learning frameworks and methodologies; (iii) training students in scholarly communication and research dissemination practices; and (iv) enhancing collaboration and teamwork. Collectively, this REU Site aims to cultivate a pipeline of well-prepared undergraduate researchers equipped for graduate study and careers in machine learning and artificial intelligence research. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria. NSF Award ID: 2548161 | Program: 01002627DB NSF RESEARCH & RELATED ACTIVIT | Principal Investigator: Mohammed Belkhouche | Institution: Missouri State University, SPRINGFIELD, MO | Award Amount: $489,183 View on NSF Award Search: https://www.nsf.gov/awardsearch/show-award/?AWD_ID=2548161 View on Research.gov: https://www.research.gov/awardapi-service/v1/awards/2548161.html