About Me

Education

I am currently a fourth year Ph.D candidate in Computer Engineering at University of Southern California under professor Viktor K. Prasanna. I have been working in the FPGA/PARALLEL COMPUTING LAB since 2017.

a photo with professor Viktor K. Prasanna (left) and me (Right)

Research Interests

I am interested in the acceleration of computational intensive algorithm and its real-world applications. I am currently working on the efficient training and inferencing of (Dynamic) Graph Neural Network.

Publications

Zhou, Hongkuan; Zheng, Da; Song, Xiang; Karypis, George; Prasanna Viktor, DistTGL: Distributed Memory-Based Temporal Graph Neural Network Training, The International Conference for High Performance Computing, Networking, Storage, and Analysis (SC), 2023

Zhou, Hongkuan; Kannan, Rajgopal; Swami, Ananthram, Prasanna, Viktor K, HTNet: Dynamic WLAN Performance Prediction using Heterogenous Temporal GNN, IEEE International Conference on Computer Communications (InfoCom), 2023

Wang, Ta-Yang; Zhou, Hongkuan; Kannan, Rajgopal; Swami, Ananthram, Prasanna, Viktor K, Throughput Optimization in Heterogeneous MIMO Networks: A GNN-based Approach, Proceedings of the 1st International Workshop on Graph Neural Networking (GNNet), 2023

Zhou, Hongkuan; Zheng, Da; Nisa, Israt; Ioannidis, Vasileios; Song, Xiang; Karypis, George, TGL: A General Framework for Temporal GNN Training on Billion-Scale Graphs, Proceedings of the VLDB Endowment (PVLDB), 2022

Zhou, Hongkuan*; Zhang, Bingyi*; Kannan, Rajgopal; Prasanna, Viktor K; Busart, Carl, Model-Architecture Co-Design for High Performance Temporal GNN Inference on FPGA, 36rd International Parallel and Distributed Processing Symposium (IPDPS), 2022 (*: Equal Contribution)

Zhou, Hongkuan; James Orme-Rogers; Kannan, Rajgopal; Prasanna, Viktor K, SeDyT: A General Framework for Multi-Step Event Forecasting via Sequence Modeling on Dynamic Entity Embeddings, 30th ACM International Conference on Information and Knowledge Management (CIKM), 2021

Zhou, Hongkuan; Srivastava, Ajitesh; Hanqing Zeng; Kannan, Rajgopal; Prasanna, Viktor K, Accelerating Large Scale Real-Time GNN Inference using Channel Pruning, Proceedings of the VLDB Endowment (PVLDB), 2021

Zeng, Hanqing*; Zhou, Hongkuan*; Srivastava, Ajitesh; Kannan, Rajgopal; Prasanna, Viktor K, Accurate, Efficient and Scalable Graph Embedding, Journal of Parallel and Distributed Computing (JPDC), 2020 (*: Equal Contribution)

Zeng, Hanqing*; Zhou, Hongkuan*; Srivastava, Ajitesh; Kannan, Rajgopal; Prasanna, Viktor K, GraphSAINT: Graph sampling based inductive learning method, International Conference on Learning Representations (ICLR), 2020 (*: Equal Contribution)

Zhou, Hongkuan; Srivastava, Ajitesh; Kannan, Rajgopal; Prasanna, Viktor K, Design and Implementation of Knowledge Base for Runtime Management of Software Defined Hardware, IEEE High Performance Extreme Computing Conference (HPEC), 2019 (Best Student Paper Nominee)

Zeng, Hanqing*; Zhou, Hongkuan*; Srivastava, Ajitesh; Kannan, Rajgopal; Prasanna, Viktor K, Accurate, Efficient and Scalable Graph Embedding, 33rd International Parallel and Distributed Processing Symposium (IPDPS), pp. 1–10, 2019 (*: Equal Contribution)

Personal Interests

I am interested in photography and outdoor activities such as hiking, fishing and camping. I shoot landscape photography, wildlife (bird) photography, milky way & star photography. I am also a beginner of vlogging. You can check out my channel ZHKhaha on Bilibili. Please contact me through tedzhouhk@gmail.com if you need a photographer.