I am a 2nd-year Ph.D. student in Computer Science at University of Southern California, supervised by Prof. Viktor Prasanna. Before that, I got my B.S. degree from Central South University, advised by Prof. Ning Gui.
My research interest is to improve the accuracy, efficiency, and scalability of graph learning from the perspective of ML algorithms and software systems. Specifically, I focus on the following areas:
Zhaoling Chen*, Xiangru Tang*, Gangda Deng*, Fang Wu, Jialong Wu, Zhiwei Jiang, Viktor Prasanna, Arman Cohan, Xingyao Wang LocAgent: Graph-Guided LLM Agents for Code Localization. Code ARXIV 2025
Yuhong Liu*, Gangda Deng*, Hongkuan Zhou, Cauligi S. Raghavendra, Rajgopal Kannan, Ananthram Swami, Viktor Prasanna AP Selection in Uplink Cell-Free Massive MIMO: An Unsupervised Heterogeneous GNN Approach. WCNC 2025
Gangda Deng, Hongkuan Zhou, Rajgopal Kannan, Viktor Prasanna Learning Personalized Scoping for Graph Neural Networks under Heterophily. ARXIV 2024
Yi-Chien Lin*, Gangda Deng*, Viktor Prasanna
A Unified CPU-GPU Protocol for GNN Training.
Code
CF 2024
Gangda Deng*, Hongkuan Zhou*, Hanqing Zeng, Yinglong Xia, Christopher Leung, Jianbo Li, Rajgopal Kannan, Viktor Prasanna TASER: Temporal Adaptive Sampling for Fast and Accurate Dynamic Graph Representation Learning. Code Slides IPDPS 2024
Gangda Deng*, Ömer Faruk Akgül*, Hongkuan Zhou, Hanqing Zeng, Yinglong Xia, Jianbo Li, Viktor Prasanna An Efficient Distributed Graph Engine for Deep Learning on Graphs. Code Slides MLG-HPCE Workshop at SC 2023
Chenhuan Yu, Gangda Deng, Ning Gui PairGNNs: enabling graph neural networks with pair-based view Neural Computing and Applications 2023