Guojing Cong Senior Staff Contact CONGG@ORNL.GOV All Publications A Study on Contrastive Graph Neural Network Pretraining for Predicting Transcriptome Profiles Predicting Drug Effects from High-dimensional Asymmetric Drug Data Sets using Graph Neural Networks: A Comprehensive Analysis... Optimizing Distributed Training on Frontier for Large Language Models Comparative Study of Large Language Model Architectures on Frontier... Derivative-based pre-training of graph neural networks for materials property predictions Hyperparameter Optimization and Feature Inclusion in Graph Neural Networks for Spiking Implementation Clustering High-dimensional Toxicogenomics Data with Rare Signals... Improving materials property predictions for graph neural networks with minimal feature engineering * Neuromorphic Computing for Scientific Applications Augmenting Graph Convolution with Distance Preserving Embedding for Improved Learning... Extensive Attention Mechanisms in Graph Neural Networks for Materials Discovery Exaflops Biomedical Knowledge Graph Analytics Semi-Supervised Graph Structure Learning on Neuromorphic Computers Scalable multiscale modeling of platelets with 100 million particles... Online Machine Learning for Accelerating Molecular Dynamics Modeling of Cells Elastic distributed training with fast convergence and efficient resource utilization Visual Understanding of COVID-19 Knowledge Graph for Predictive Analysis Versatile feature learning with graph convolutions and graph structures Dynamics-Based Peptide–MHC Binding Optimization by a Convolutional Variational Autoencoder: A Use-Case Model for CASTELO CASTELO: clustered atom subtypes aided lead optimization—a combined machine learning and molecular modeling method Enabling AI-Accelerated Multiscale Modeling of Thrombogenesis at Millisecond and Molecular Resolutions on Supercomputers
Research Highlight Elastic Distributed Training with Fast Convergence and Efficient Resource Utilization
