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Early involvement in engineering research has proven to be a highly effective way to inspire undergraduate students to pursue advanced studies or research-intensive careers. By engaging students in real-world, hands-on research projects, they not only sharpen their problem-solving skills but also develop the intellectual independence needed to tackle complex engineering challenges. These benefits are amplified when the research experience is multidisciplinary, allowing students to engage with topics beyond the confines of their chosen major. Moreover, participation in a collaborative cohort—where continual interactions and shared learning experiences occur—helps foster a sense of community and shared purpose, further enhancing the learning process. This paper presents the outcomes and impacts of a unique undergraduate research program conducted collaboratively between Oklahoma State University, Stillwater, and the University of Alabama in Huntsville. What sets this program apart is its fusion of engineering and engineering technology disciplines, its blend of applied and fundamental research, and its focus on multidisciplinary topics such as human safety, fire protection technology, mechanical engineering technology, electrical engineering, and artificial intelligence. The program engages students from sophomore to senior levels, offering them a chance to explore various research methodologies and work on projects that span multiple fields of engineering. This exposure helps them cultivate a comprehensive understanding of engineering systems and their real-world applications. In this paper, we will delve into the structure and activities of the Research Experiences for Undergraduates (REU) program, discussing its various components as well as the educational and research outcomes it has produced. A central theme of the program is its focus on multidisciplinary research, which ranges from technical fields such as fire protection and mechanical engineering technology to more advanced areas like electrical engineering and artificial intelligence. This breadth of topics ensures that students are equipped with a wide range of skills, from analytical problem-solving to creative thinking, as they learn to approach engineering challenges from multiple perspectives. Additionally, the program’s emphasis on cohort-building activities plays a crucial role in shaping the students’ experiences. By promoting collaboration among students from different disciplines, the program encourages the cross-pollination of ideas, mutual learning, and the development of soft skills such as communication, teamwork, and leadership. The interactions fostered within the cohort help students build a network of peers who share similar academic and career aspirations, strengthening their commitment to research and professional development. The paper will also present the results of both formative and summative assessments of the program, highlighting its impacts on student learning, skill development, and long-term career trajectories. By examining these outcomes, we demonstrate how this collaborative and multidisciplinary research program has successfully nurtured the next generation of independent researchers and engineering leaders, equipping them to meet the challenges of an increasingly complex and interconnected world. 

This paper proposes a novel intelligent human activity recognition (HAR) framework based on a new design of Federated Split Learning (FSL) with Differential Privacy (DP) over edge networks. Our FSL-DP framework leverages both accelerometer and gyroscope data, achieving significant improvements in HAR accuracy. The evaluation includes a detailed comparison between traditional Federated Learning (FL) and our FSL framework, showing that the FSL framework outperforms FL models in both accuracy and loss metrics. Additionally, we examine the privacy-performance trade-off under different data settings in the DP mechanism, highlighting the balance between privacy guarantees and model accuracy. The results also indicate that our FSL framework achieves faster communication times per training round compared to traditional FL, further emphasizing its efficiency and effectiveness. This work provides valuable insight and a novel framework which was tested on a real-life dataset.