I am an Assistant Professor of Computer Science and Engineering at Mississippi State University, focusing on dynamic behaviors over graphs and networks (see details of my research interests). I completed my Ph.D. degree at Virginia Tech in 2020, under the supervision of Dr. Chang-Tien Lu. For students interested in my research, check ways to work with me. If you are interested in my courses, please check the course syllabi.
Graph Theory & LLM
starting in Spring 2025. Fill this form to apply.[Papers]
7/24: 1 paper accepted at IJCAI 24 about Trajectory Mining on Graph
, 2 papers accepted at EMBC 2024 about Antimicrobial Resistance Prediction with Genetic Graph
and Generation of Synthetic Microbiomes
, 1 paper accepted at PAKDD on Appliances Graph for Energy Disaggregation
, 1 paper accepted at ICTAI on Neural Tangent Bayesian Optimization for Graph Dynamics
.[Honor]
4/24: Received Excellent Reviewer of the IEEE Transactions on Network Science and Engineering Journal.[Paper]
12/23: 2 papers are accepted by AAAI 24 proposing Graph Bayesian Optimization to conduct information propagation efficiently: Multiple-Source Localization from a Single-Snapshot Observation Using Graph Bayesian Optimization, and Graph Bayesian Optimization for Multiplex Influence Maximization[Paper]
12/23: Our theoretical framework for spectral GNN accepted by ACM Computing Survey, see paper, related work and slides.[Tool]
8/23: XFlow is released, which targets modeling generalized graph flows[Paper]
12/22: One paper is accepted by SIAM Data Mining (SDM) 23’ Understanding Influence Maximization via Higher-Order Decomposition: how seeds interact in higher-order perspective[Textbook]
7/22: Our textbook published by Springer Nature provides numerous code examples, Springer Nature, Amazon[Paper]
12/21: One paper about Graph Wavelet is accepted by SIAM Data Mining (SDM) 22’ with SIAM Early Career Travel Award[Tutorial]
5/21: See our tutorial Spreading Model for Epidemics (GNN, RNN, SIR and PDE) in SDM 21’[Paper & Award]
9/20: Papers on Kalman filtering and school redistricting are accepted by ACM SIGSPATIAL 20’ with (Best Paper Award)[Paper]
12/19: Unsupervised learning for material discovery is published in Nature Communications.Research Interest: My research focuses on graph dynamics, leveraging methodologies such as spectral graph theory, uncertainty quantification, higher-order analysis, and physics-inspired approaches. Currently, I am particularly interested in the dynamics of coupled and interdependent networks. My collaborations span diverse applications, including Social network 1, Transportation 2, Spatial epidemiology, ecological network 3, Brain network 4, Genetics 5, Circuit 6. Also, I have been working on spectral method 7, Uncertainty Quantification 8, LLM/transformers, AI education 9
Working with MS State faculty with crimiology expertise in cyberbully, hate speech in NSF projects: Advancing Speech Detection: A Hybrid Approach Using Large Language Models and Graph Neural Networks, and Synergistic Graph Flow Analytics: An Integrated Infrastructure for Bridging Complexity, Fragmentation, and Interdisciplinary Gaps ↩︎
traffic modeling, Graph Convolutional Networks with Kalman Filtering for Traffic Prediction, redistricting Geospatial clustering for balanced and proximal schools ↩︎
with USDA ecologists in funded projects. ↩︎
with MS State faculty and Univ. of Mississippi Medical Center (UMMC) on brain signals of ADHD & sleep ↩︎
with MS State College of Veterinary Medicine, EMBC paper ↩︎
UC Davis & GMU, DATE paper ↩︎
Bridging the Gap between Spatial and Spectral Domains: A Unified Framework for Graph Neural Networks ↩︎
AAAI 24: Multiple-Source Localization from a Single-Snapshot Observation Using Graph Bayesian Optimization, SDM 23: Understanding Influence Maximization via Higher-Order Decomposition ↩︎
NSF project: Learning to create Intelligent Solutions with Machine Learning and Computer Vision: A Pathway to AI Careers for Diverse High School Students ↩︎