Stony Brook University Computer Science

Three undergraduate students (Dongyeong Kim, Gilho Kim, and Myeongkjun Kim), together with one Ph.D. student (Si Yong Lee), have published a research paper in IEEE Access, a Q1-ranked international journal, demonstrating an outstanding educational and research achievement. This work was conducted under the supervision of Prof. Yoon Seok Yang and reflects the strong research-oriented educational environment fostered through close faculty mentorship.

The research focuses on improving the training and inference efficiency of spiking neural network (SNN)–based object detection models for low-power, real-time edge AI applications. To address the high computational cost of conventional CNN-based detectors such as YOLO, the group enhanced a Sigma–Delta spiking YOLOv3 framework by incorporating advanced training and optimization techniques.

The proposed methods improved convergence, detection accuracy, and energy efficiency, and were validated using multiple real-world datasets. In addition, the trained model was successfully deployed on Intel’s Loihi 2 neuromorphic platform, demonstrating its feasibility for real-time and low-power operation.

Through this project, undergraduate students gained hands-on experience in advanced AI research, from algorithm development to deployment on a neuromorphic hardware platform. This work demonstrates the value of active undergraduate participation in research and the CS Department’s commitment to integrating education and cutting-edge research.