Support Post-Doctoral researchers and junior faculty
Lead: Clive Woods (USA); Members: Dr. Kerry Hartman (NHSC), Dr. Zhigang Xiao (AAMU), Dr. Juan Li (NDSU), Dr. Kristine Steffen (NDSU), Dr. Na Gong (USA), Dr. Roma Hanks (USA), Dr. Hulya Kirkici (USA), Dr. Jia Di (UA), & Dr. Eric L. Johnson (UND)
To find more information about the program, check the SARJ Solicitation form.
Submission deadline to Campus Coordinator is April 18th at 11:59 PM CT.
A single .PDF file (including Technical Case and Research Qualifications) must be submitted to the Campus Coordinator of each Institution by email. Find your campus coordinator in the list below.
Please review the SARJ Solicitation form to ensure you have completed all proposal components.
If you have any questions about this solicitation, you may contact EdgeAI@southalabama.edu.
North Dakota State University:
Dr. Danling Wang
701-231-8396
danling.wang@ndsu.edu
University of Arkansas:
Dr. Jia Di
479-575-5728
jdi@uark.edu
University of North Dakota
Dr. Eric L. Johnson
701-777-3811
eric.l.johnson@und.edu
Nueta Hidatsa Sahnish College
Dr. Kerry Hartman
701-627-8053
khartm@nhsc.edu
Alabama A&M University
Dr. Zhigang Xiao
256-372-5679
zhigang.xiao@aamu.edu
University of South Alabama
Ms. Hannah Oswalt
251-460-6997
hoswalt@southalabama.edu
EDGE AI NSF Project announces Seed Awards for Researchers at Junior Level (SARJ)
The award phase of Seed Awards for Researchers at Junior Level (SARJ) has been completed under Theme 7, Education and Workforce Development. A total of 17 research proposals were received for award consideration by the November 1, 2023 deadline. Four research proposals were selected for funding by the EDGE AI NSF program. The four proposals awarded funding are:
Assistant Professor: University of South Alabama, Department of Computer Science
Title: An Investigation of Cyberattacks on Edge AI Components of Self Driving Vehicles
The security of Edge AI devices is often overlooked and this can be a concern, as these devices often are used to perform critical and sometimes dangerous tasks such as autonomous driving. This project aims to develop a small autonomous vehicle with a machine learning based driving architecture for cybersecurity research. The vehicle and its machine learning architecture will first be implemented in a simulation environment and then transferred to a physical vehicle in the form of a small commercially available robot. The robot will serve as an Edge AI test platform for examining cybersecurity attacks and possible defenses across the vehicle’s driving architecture.
Assistant Professor: North Dakota State University, Department of Mechanical Engineering.
Title: Development of Multi UAV System for Context Awareness and Seamless Operation
In this research, a distributed UAV coordination framework to ensure the continuous and seamless operation of multiple UAVs will be developed. These UAVs will be equipped with edge AI computing devices connected through a wireless mesh network. The proposed system will enable UAVs to autonomously recharge their batteries when depleted and dynamically adjust their roles to minimize task disruptions.
Assistant Professor: University of North Dakota, School of Electrical Engineering and Computer Science
Title: TinyML ADD: Tiny Machine Learning for Adaptive Anomaly Detection
Tiny machine learning (TinyML)-based anomaly detection, bringing anomaly detection, edge computing, and AI together to power-constrained edge devices, has become an emerging research topic. However, it may become suboptimal, unreliable, or even catastrophically fail at worst when concept drift affects the data generation process. This project aims to develop a TinyML-based framework that can effectively deploy robust anomaly detection on edge devices and enable edge AI to perform on-device adaptive learning to deal with non-stationary environments.
Assistant Professor: University of North Dakota, School of Electrical Engineering and Computer Science
Title: Edge AI Driven Data Synthesis with Configurable Privacy and Usability in Edge AI
This project focuses on the crucial role of Edge AI in enabling AI-based services with reduced latency and scalability in modern networks and connected infrastructures. It highlights the challenges of acquiring fresh and comprehensive datasets while protecting privacy and the risk of personal information leakage
