Area of research
Artificial Intelligence · Statistical and Nonlinear Physics
Research interest
Research interests include Advanced Graph Neural Networks, Topic Modeling, Anomaly Detection Techniques and Applications, and Complex Network Analysis Techniques.
Domain Specialization as the Key to Make Large Language Models Disruptive: A Comprehensive Survey
Going Beyond XAI: A Systematic Survey for Explanation-Guided Learning
Large Language Models for Spatial Trajectory Patterns Mining
Self-consistent Deep Geometric Learning for Heterogeneous Multi-source Spatial Point Data Prediction
3DPFIX: Improving Remote Novices' 3D Printing Troubleshooting through Human-AI Collaboration Design
Bridging the Gap between Spatial and Spectral Domains: A Unified Framework for Graph Neural Networks
Tutorials at The Web Conference 2023
Graph Neural Networks: Foundation, Frontiers and Applications
A Systematic Survey on Deep Generative Models for Graph Generation
Source Localization of Graph Diffusion via Variational Autoencoders for Graph Inverse Problems
Aligning Eyes between Humans and Deep Neural Network through Interactive Attention Alignment
An Invertible Graph Diffusion Neural Network for Source Localization
Unsupervised Deep Subgraph Anomaly Detection
Functional Connectivity Prediction With Deep Learning for Graph Transformation
Event Prediction in the Big Data Era
Generating tertiary protein structures via interpretable graph variational autoencoders
GNES: Learning to Explain Graph Neural Networks
Generative Adversarial Learning of Protein Tertiary Structures
Cognitive and Scalable Technique for Securing IoT Networks Against Malware Epidemics
Generative deep learning for macromolecular structure and dynamics
A Systematic Survey on Deep Generative Models for Graph Generation
'Beating the news' with EMBERS
ERI: Towards Efficient and Robust Federated Neuromorphic Learning in Wireless Edge Networks
Collaborative Research: OAC Core: Distributed Graph Learning Cyberinfrastructure for Large-scale Spatiotemporal Prediction
CAREER: Uncovering Solar Wind Composition, Acceleration, and Origin through Observations, Modeling, and Machine Learning Methods
Travel: NSF Student Travel Support for the 2023 IEEE International Conference on Data Mining (IEEE ICDM 2023)
SHINE: Understanding the Physical Connection of the in-situ Properties and Coronal Origins of the Solar Wind with a Novel Artificial Intelligence Investigation
CAREER: Spatial Network Deep Generative Modeling, Transformation, and Interpretation
III: Small: Deep Generative Models for Temporal Graph Generation and Interpretation
OAC Core: SMALL: DeepJIMU: Model-Parallelism Infrastructure for Large-scale Deep Learning by Gradient-Free Optimization
OAC Core: SMALL: DeepJIMU: Model-Parallelism Infrastructure for Large-scale Deep Learning by Gradient-Free Optimization
III: Small: Deep Generative Models for Temporal Graph Generation and Interpretation
III: Small: Graph Generative Deep Learning for Protein Structure Prediction
CAREER: Spatial Network Deep Generative Modeling, Transformation, and Interpretation
CRII: III: Interpretable Models for Spatio-Temporal Event Forecasting using Social Sensors
III: Small: Graph Generative Deep Learning for Protein Structure Prediction
CRII: III: Interpretable Models for Spatio-Temporal Event Forecasting using Social Sensors
AGS-PRF: Exploring the Equatorial Solar Wind from Photosphere to Heliosphere Along Solar Cycles