Restivo, R., & Walsh, C., & Duclos, W. C., & Mali, V., & Wang, J., & Wang, H. H., & Yang, T., & Stansbury, R., & Song, H. H. (2023, June), NSF REU Site: Swarms of Unmanned Aircraft Systems in the Age of AI/Machine Learning  Paper presented at 2023 ASEE Annual Conference & Exposition, Baltimore , Maryland. 10.18260/1-2--43739

\bibitem{asee_peer_43739}
  Restivo, R., \& Walsh, C., \& Duclos, W. C., \& Mali, V., \& Wang, J., \& Wang, H. H., \& Yang, T., \& Stansbury, R., \& Song, H. H. (2023, June), \emph{NSF REU Site: Swarms of Unmanned Aircraft Systems in the Age of AI/Machine Learning}
  Paper presented at 2023 ASEE Annual Conference \& Exposition, Baltimore , Maryland.
  10.18260/1-2--43739

Ryan Restivo, Connor Walsh, Wesley Chase Duclos, Vrushank Mali, Jian Wang, Huihui H. Wang, Thomas Yang, Richard Stansbury, and Houbing Herbert Song.  "NSF REU Site: Swarms of Unmanned Aircraft Systems in the Age of AI/Machine Learning".  2023 ASEE Annual Conference & Exposition, Baltimore , Maryland, 2023, June.  ASEE Conferences, 2023.  https://peer.asee.org/43739 Internet.  28 May, 2024

\bibitem{asee_peer_43739}
  Ryan Restivo, Connor Walsh, Wesley Chase Duclos, Vrushank Mali, Jian Wang, Huihui H. Wang, Thomas Yang, Richard Stansbury, and Houbing Herbert Song.
  "NSF REU Site: Swarms of Unmanned Aircraft Systems in the Age of AI/Machine Learning".
  \emph{2023 ASEE Annual Conference \& Exposition, Baltimore , Maryland, 2023, June}.
  ASEE Conferences, 2023.
  https://peer.asee.org/43739 Internet.  28 May, 2024

@INPROCEEDINGS{asee_peer_43739
author = "Ryan Restivo, Connor Walsh, Wesley Chase Duclos, Vrushank Mali, Jian Wang, Huihui H. Wang, Thomas Yang, Richard Stansbury, and Houbing Herbert Song"
title = "NSF REU Site: Swarms of Unmanned Aircraft Systems in the Age of AI/Machine Learning"
booktitle = "2023 ASEE Annual Conference \& Exposition"
year = "2023"
month = "June"
address = "Baltimore , Maryland"
publisher = "ASEE Conferences"
note = {https://peer.asee.org/43739}
number = {10.18260/1-2--43739}
}

TY  - CPAPER
AB  - Drone swarms, the ability of drones to autonomously make decisions based on shared information, create new opportunities with major societal implications. However, future drone swarm applications and services pose new networking challenges. A resurgence of artificial intelligence (AI) and machine learning (ML) research presents a tremendous opportunity for addressing these networking challenges. This REU site focuses on networking research for drone swarms in the age of AI. The first cohort of seven undergraduate students were recruited to participate in a ten-week summer program to perform networking research for drone swarms under the guidance of faculty and research mentors. In this paper, a couple of drone swarm projects were briefly summarized. By the end of the summer program, students were surveyed about their undergraduate research experiences. A couple of months after students were back to their home institutions, a couple of students were interviewed about the impact of their undergraduate research experiences on their continued learning. The faculty who helped to supervise the undergraduate students at the REU also were interviewed. The feedback from the students and reflections from the faculty would provide guidance about the integration of the undergraduate research experiences into the courses in order to broaden impacts of the undergraduate research on learning and teaching. In the future, at least another two cohorts of students especially from underrepresented groups will be recruited. We will have a longitudinal study to explore the impacts of the undergraduate research experiences on learning and teaching using a mixed research method of the surveys and interviews.
AU  - Ryan Restivo
AU  - Connor Walsh
AU  - Wesley Chase Duclos
AU  - Vrushank Mali
AU  - Jian Wang
AU  - Huihui H. Wang
AU  - Thomas Yang
AU  - Richard Stansbury
AU  - Houbing Herbert Song
CY  - Baltimore , Maryland
DA  - 2023/06/25
PB  - ASEE Conferences
TI  - NSF REU Site: Swarms of Unmanned Aircraft Systems in the Age of AI/Machine Learning
UR  - https://peer.asee.org/43739
DO  - 10.18260/1-2--43739
ER  -